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www.sbmc2016.de
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CONTENT
CONTENT
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WELCOME5
ORGANIZER
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GENERAL INFORMATION
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SOCIAL PROGAM
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PROGRAM OVERVIEW
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PROGRAM
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POSTER OVERVIEW
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MTZ AWARD
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INVITED TALKS
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ORAL PRESENTATIONS
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POSTER PRESENTATIONS
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INDEX
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WELCOME
Message of greeting for the 6th International Conference on Systems Biology of
Mammalian Cells (SBMC 2016)
Major diseases such as cancer and dementia are caused by specific dysfunctions in complex, dynamic life processes of human
cells and organs. Many genetic factors as well as environmental influences and individual lifestyles have a decisive effect on
the development and progress of such diseases as well as on the patient’s prospects. Systems biology and systems medicine,
which derives from it, make it possible to understand the complex connections and thus to improve patient care in the long
term.
The Federal Ministry of Education and Research has been supporting systems medicine research through the research and
funding scheme “e:Med – Paving the Way for Systems Medicine” since 2012. The latter is part of the Federal Government’s
Health Research Framework Programme. Along with medical genome research, systems biology is the main contributor
to progress in systems medicine. German research has a pioneering role in this field in Europe and we must continue to
strengthen it. My Ministry will continue to expand national expertise, support international collaborations and promote
young research talent.
The exchange of experience between researchers often contributes substantially to advances in research. The Conference
on Systems Biology of Mammalian Cells 2016 provides an excellent opportunity for scientists to share their insights and
make new contacts. It also provides the opportunity to learn about developments in systems biology and systems medicine
research. I am particularly glad that the conference will be recognizing the work of young scientists: The doctoral theses
which will be honoured with the “MTZ Award for Medical Systems Biology” of the MTZ Foundation clearly show the potential
of young scientists in the field of medically oriented systems biology.
I wish all the participants interesting discussions and many new ideas for their scientific work.
Prof. Dr. Johanna Wanka
Federal Minister of Education and Research
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WELCOME
As the CEO of the Helmholtz Zentrum München I am pleased to welcome you to the 6th Conference on Systems Biology of
Mammalian Cells (SBMC 2016) in Munich.
The Helmholtz Zentrum München has a unique profile by focusing on major common diseases such as diabetes mellitus,
chronic lung diseases and in the near future additionally on allergies. Our goal is to contribute to personalized approaches
for the prevention and therapy of those common diseases thus causing an impact on the patient’s health. To achieve this, the
scientists investigate the interaction of genetics, environmental factors and lifestyle with new technologies in different areas
such as epidemiology, epigenetic and stem cells.
The most exciting new project though is the Pioneer Campus. The Pioneer Campus will offer brilliant young scientists in
interdisciplinary teams from around the world a research facility in a highly creative, excellent academic and outstanding
technological environment. The central vision of the Pioneer Campus is to enable decisive breakthroughs in the development
of concepts for key technological innovations.
System biology - I am sure - will also play an important role in this concept of the Pioneer Campus as it is not only an enabler
but also a driver in itself for development of new hypotheses. Systems biology has therefore a long tradition at the Helmholtz
Zentrum München. In 2008, the interdisciplinary project ‘Control of Regulatory Networks’ was begun within the ‘Alliance
for Systems Biology’ funded by the Helmholtz Association. Today, there are many experimental and theoretical approaches in
systems biology at our Center as well as in the larger context of the research field “Health” of the Helmholtz Alliance. That is
why we are glad to organize this year’s SBMC here in Munich.
At SBMC 2016, systems biology methods and their applications in various fields of basic research will be presented
along with translation. Thus the conference contributes as a platform for the presentation and the exchange of the latest
systems biology research into important social objectives: the elucidation of disease mechanisms and the development of
personalized medicine.
Considering all these facts, I am sure the research we are doing will have an important and positive impact on human health.
I hope that you will enjoy the conference and wish you successful days.
rof. Dr. Günther Wess
P
CEO Helmholtz Zentrum München
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WELCOME
Dear colleagues and friends,
With great pleasure we welcome you today to the 6th Conference on Systems Biology of Mammalian Cells here in Munich.
This conference is kindly supported by the BMBF, the German Federal Ministry of Education and Research and is a
continuation of a conference series started in 2006, organized early on by the competence network HepatoSys, which 4
years later was successfully extended by the Virtual Liver Network promoting excellent interdisciplinary research. In the
beginning of 2016, the BMBF granted the follow-up competence network LiSym, enabling further progress of systems biology
towards applied systems medicine in the context of liver research.
Systems biology has arrived, and many experimental groups have started to incorporate corresponding data analyses and
modeling approaches into their daily work flows. Similarly, theoretical groups have either taken up biological experiments,
or have started intense collaboration with experimental partners with daily interactions. Next steps are towards integration
of ever increasing, ever more complex data as well as available information from existing resources into models, from basic
research to translational systems medicine. SBMC 2016 will present such novel approaches progress, gathering outstanding
theoretical and practical scientists from all over the world with the intention to promote exchange of ideas and knowledge for
further acceleration of advances of systems biology.
The program was designed to cover a broad range of theoretical, experimental and computational approaches and technology
trends including highlights in the field of novel technologies like single-cell profiling and image-based systems biology
as well as scientific progress in modeling approaches like signaling and metabolism and genetic/epigenetic mechanisms.
Moreover, progress in systems medicine and pharmacology complete the compilation of cutting-edge research and excellent
progress in theoretical and applied systems biology. The outstanding list of invited speakers include top international
researchers like our plenary speaker Chris Sander from Harvard Medical School and many more. Moreover, we are glad to
host the MTZ-Award ceremony 2016 for excellent PhD theses in systems biology, with three young scientists presenting their
innovative research to the SBMC audience.
The SBMC program includes two poster sessions, which encourage detailed one-on-one interactions, as well as a welcome
reception on the first day and a conference dinner/ party at the BMW World in Munich.
We are sure you will enjoy this year´s SBMC in Munich and we are looking forward to many fruitful discussions, productive
interactions and exciting after-conference activities here at Munich. Thanks to all those who have contributed to enabling
and organizing SBMC 2016!
Fabian Theis Ursula Klingmüller
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ORGANIZER
Conference Chairs
Fabian Theis
Institute of Computational Biology
Helmholtz Zentrum München
Deutsches Forschungszentrum für
Gesundheit und Umwelt (GmbH)
Ingolstädter Landstr. 1
85764 Neuherberg
Ursula Klingmüller
Systems Biology of Signal Transduction
German Cancer Research Center (DKFZ)
Im Neuenheimer Feld 280
69120 Heidelberg
In Cooperation with:
Virtual Liver Network
/ LiSyM
The 6th Conference on Systems Biology
of Mammalian Cells is under the auspices of Prof. Dr. Johanna Wanka, Federal Minister of Education and Research
Scientific Committee
Dirk Drasdo (INRIA French National Institute for
Research in Computer Science and Control,
Le Chesnay Cedex)
Jan Hengstler (Leibniz-Institut für Arbeitsforschung
an der TU Dortmund, Dortmund)
Thomas Höfer (DeutschesKrebsforschungszentrum (DKFZ), Heidelberg)
Hermann-Georg Holzhütter
(Charité - Universitätsmedizin Berlin, Berlin)
Ursula Klingmüller (Deutsches
Krebsforschungszentrum (DKFZ), Heidelberg)
Frank Lammert (Universitätsklinikum des Saarlandes,
Homburg/Saar)
Madlen Matz-Soja (Universität Leipzig, Medizinische
Fakultät, Leipzig)
Werner Mewes (Helmholtz Zentrum München, Deutsches
Forschungszentrum für Gesundheit und Umwelt (GmbH),
Neuherberg)
Nassir Navab (Technische Universität München, München)
Ivo Sbalzarini (Max Planck Institute of Molecular Cell
Biology and Genetics, Dresden)
Heribert Schunkert (Deutschen Herzzentrum München,
Lübeck)
Fabian Theis (Helmholtz Zentrum München, Deutsches
Forschungszentrum für Gesundheit und Umwelt (GmbH),
Neuherberg)
Jens Timmer (Freiburg Institute for Advanced Studies,
School of Life Sciences – LifeNet, Freiburg im Breisgau)
Nachiket Vartak (Leibniz-Institut für Arbeitsforschung an
der TU Dortmund, Dortmund)
Conference Office
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event lab. GmbH
Dufourstraße 15
04107 Leipzig
Phone: Phone on site :
Mail: 0341 / 240596-72
+49 177 2673158
sbmc2016@eventlab.org
Plenary Speaker
Chris Sander
(cBio Center at Dana-Farber Cancer Institute and Harvard
Medical School, Boston)
Invited Speakers
Alexander Anderson (H. Lee Moffitt Cancer Center &
Research Institute, Tampa)
Ina Bergheim (Friedrich Schiller University Jena, Jena)
Johan Björkegren (Mount Sinai Hospital, New York)
Bernd Bodenmiller (University of Zürich, Zürich)
Javier Diego (EMBL-CRG, Barcelona)
Alexander Hoffmann (UCLA, Los Angeles)
Martin Howard (John Innes Centre, Norwich)
Andrzej M. Kierzek (Simcyp, Sheffield)
Markus Löffler (University of Leipzig, Leipzig)
John Marioni (EBI, Hinxton)
Robert Murphy (Carnegie Mellon University, Pittsburgh)
Tudor Oprea (University of New Mexico, Albuquerque)
David Rand (University of Warwick, Coventry)
Ines Thiele (Esch-sur-Alzette, Luxembourg)
GENERAL INFORMATION
Conference Venue
Klinikum rechts der Isar
Technische Universität München
Hörsaal A
Ismaninger Straße 22 / Entrance Einsteinstraße
81675 München
Germany
You’ll find a map at the back.
All lectures take place in the Hörsaal A.
The exhibition and poster area and the registration desk can
be found in the Foyer of Hörsaal A as well.
Transport
From Munich main station (about 10 min):
From the main station take U4 (direction
`Arabellapark’) or U5 (direction `Neuperlach
Süd’) to`Max-Weber-Platz’
From the airport (about 40 min):
Take S train S8 (direction `Herrsching’) to
`Ostbahnhof’. Change to the underground
U4 (direction `Laimer Platz’) or U5 (direction
`Westendstrasse’) to `Max-Weber-Platz’
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Wifi Access
The “eduroam” network is available at the university.
Login details are available at the reception desk.
Registration Desk
The Registration Desk is located in the Foyer area.
Opening hours of registration desk:
Wednesday, April 06, 2016 12:00 noon – 08:00 pm
Thursday, April 07, 2016 08:00 am – 07:00 pm
Friday, April 08, 2016
08:00 am – 01:00 pm
Technical Facilities / Speakers Preview
The lecture room is equipped with laptop/data projectors and with blackboards. Speakers are requested to bring the file of
their talks (preferably on a flash drive in PDF format) to the “speakers preview” at the registration desk, two hours before
the beginning of their session, or to the technical support personnel in the lecture room not later than 30 minutes before the
session starts. The “speakers preview” will be open during the opening hours of the registration desk.
Poster Exhibition
The poster exhibition of the 6th Conference on Systems Biology of Mammalian Cells will be located at the foyer
area (level 0).
The poster exhibition will be opened all-day on the conference days. The Poster Party, during which each presenter must be
available for discussion, will take place on Wednesday, April 6th, at 6:30 pm (for even poster numbers) and Thursday, April
7th, at 6:00 pm (for odd poster numbers).
Materials for putting up the posters will be provided. Posters must be removed from the board until the end of the
conference. The meeting staff will remove posters not taken down by Friday, April 8th, and the meeting organizers cannot
take any further responsibility for the material.
We can proudly announce that the committee will award a prize to at least one outstanding poster. The poster prize ceremony
will be part of the closing statement on Friday, April 8th.
Conference Language
The official language of the conference is English.
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SOCIAL PROGAM
Welcome Reception
Do not miss the chance to mingle with colleagues and friends on the evening of April 6th, 2016 at 6:30 pm at the exhibition
area in the Foyer.
Tickets are included in the registration fee, but registration is required.
Conference Dinner and Party
The Conference Dinner and Party takes place at the Restaurant Bavarie at BMW Welt (Am Olympiapark 1).
The dinner starts at 7:30 pm.
Public transport:
Coming from the station “Max-Weber-Platz”, likewise take the underground U4/U5 (direction “Westend”/”Laimer Platz”).
Change at “Odeonsplatz” into U3 (direction “Olympia Einkaufszentrum” to “Olympiazentrum”. From there is is only a few
more steps to the BMW Welt.
Sponsors
We are grateful for the support of our partner and sponsors.
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PROGRAM OVERVIEW
Wednesday: April 6th
01:30–02:00 pm
Welcome Addresses
02:00–04:00 pm
Image-based Systems Biology
04:00 pm
Coffee Break
04:30–05:30 pm
MTZ Award
05:30–06:15 pm
Plenary Talk
06:30 pm
Welcome Reception & Poster Session (even numbers)
Thursday: April 7th
09:00–11:00 am
Single-Cell Systems Biology (Technology & Methods)
11:00 am
Coffee Break
11:15 am–01:00 pm Signaling Modeling
01:00 pm
02:00–03:45 pm Lunch Break
Metabolism
03:45 pm
Coffee Break
04:15–06:00 pm
Multi-Scale Approaches
06:00–07:00 pm
Poster Session
07:30 pm
Conference Dinner & Party at Restaurant Bavarie at BMW Welt
Friday: April 8th
09:00–10:45 am
Systems Medicine I & Systems Pharmacology
10:45 am
Coffee Break
11:15 am–01.00 pm Systems Medicine II & Genetic/Epigenetic Mechanisms
01:00 pm
Poster Prize Award & Closing Statement
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Program
Wednesday: April 6th
01:30–02:00 pm
Welcome Addresses
Fabian Theis (Helmholtz Zentrum München)
Alfons Enhsen (Helmholtz Zentrum München)
Klaus-Peter Michel (Projektträger Jülich, Forschungszentrum Jülich)
Peter Jansen (LySiM Network)
02:00–03:45 pm
Image-based Systems Biology
Chairs: Ivo Sbalzarini & Nassir Navab
02:00 pm
Building models of cell organization, differentiation and perturbation
directly from microscope images
Bob Murphy (Carnegie Mellon University, Pittsburgh)
02:30 pm
Image-driven modeling of limb development across 3D space and time
Javier Diego (EMBL-CRG, Barcelona)
03:00 pmA quantitative model for macrophage activation predicts tissue thresholds for the propagation
of inflammation
James Bagnall (University of Manchester, Manchester)
03:15 pm
Stem cells dynamics and its regulation during spinal cord regeneration
Fabian Rost (Technische Universität Dresden, Dresden)
03:30 pm
Tuning out cell cycle entry control – cell fate decisions under oncogenic MYCN
Erika Kuchen (DKFZ, Heidelberg)
03:45 pm
In silico synchronization and modulation of nuclear ruptures in laminopathy model cells
Winnok de Voss (University of Antwerp, Antwerpen)
04:00 pm
04:30–05:30 pm
MTZ Award
04:30 pm
Introduction and Laudatio
Monika Zimmermann, Thomas Zimmermann, Fabian Theis
04:45 pm
Robustness of MAPK Signalling
Franziska Witzel (Charité Universitätsmedizin Berlin, Berlin)
05:00 pm
MicroRNAs decrease protein expression noise
Jörn Schmiedel (Charité Universitätsmedizin Berlin, Berlin)
05:15 pm
Models of Influenza A Virus Infection: From Intracellular Replication to Virus Growth in Cell Populations
Stefan Heldt (University of Oxford, Oxford)
05:30–06:15 pm
Plenary Talk
Systems Biology in Action: Design of Cancer Combination Therapy Chris Sander (DFCI & HMS, Boston)
06:15 pm
Publishing a Paper: the editorial process at Molecular Systems Biology
Maria Polychronidion (Molecular Systems Biology)
06:30 pm
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Coffee Break
Welcome Reception & Poster Session (even numbers)
Thursday: April 7th
09:00–11:00 am
Single-Cell Systems Biology (Technology & Methods)
Chairs: Nachiket Vartak & Fabian Theis
09:00 am
Comprehensive analysis of single cell states through time and space by mass cytometry
Bernd Bodenmiller (University of Zürich, Zürich)
09:30 am
Using single-cell genomics to study early development
John Marioni (EBI, Hinxton)
10:00 am
Single Molecule Approaches For Studying Liver Hetereogeneity
Shalev Itzkovitz (Weizmann Institute of Science, Rehovot)
10:15 am
Inferring Tumour Evolution from Single-Cell Sequencing Data
Edith Ross (University of Cambridge, Cambridge)
10:30 am
Comparative analysis of single-cell RNA-sequencing methods
Wolfgang Enard (LMU, Munich)
10:45 am
Perturbation of dynamics of NF- B and the regulation of gene expression
Polly Downton (University of Manchester, Manchester)
11:00 am
Coffee Break
11:15 am–01:00 pm Signaling Modeling
Chairs: Ursula Klingmüller & Jens Timmer
11:15 am
Specificity and Noise in NF- B Signaling
Alexander Hoffmann (UCSD, Los Angeles)
11:45 am
Signals, effectors, information and decisions
David Rand (University of Warwick, Coventry)
12:15 pm
Analysing the interplay of NF- B precursors using a mathematical modelling approach
Bente Kofahl (Max Delbrueck Center for Molecular Medicine, Berlin)
12:30 pm
Identification of an intracellular negative and an autocrine positive feedback coordinating the interferon-induced antiviral response
Marcel Schilling (DKFZ, Heidelberg)
12:45 pm
Detecting emergent properties in genomic data: Consolidating inflammatory response dynamics
Jason Shoemaker (University of Pittsburgh, Pittsburgh)
01:00 pm
Lunch Break
02:00–03:45 pm Metabolism
Chairs: Madlen Matz-Soja & Hergo Holzhütter
02:00 pm
Hepatic ethanol metabolism
Ina Bergheim (Freidrich-Schiller-Universität Jena, Jena)
02:30 pm
Computational modeling of human metabolism and its application to Parkinson’s disease
Ines Thiele (Université du Luxembourg, Luxembourg)
03:00 pm
SteatoNet as a predictive and gender-based liver metabolic model
Tanja Cvitanović (University of Ljubljana, Ljubljana)
03:15 pm
Trans-omic reconstruction of insulin signal flow in global phosphorylation and metabolic network
Katsuyuki Yugi (University of Tokyo, Tokyo)
03:30 pm
Dynamic glycosylation flux analysis
Sandro Hutter (ETH Zürich, Zürich)
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03:45 pm
Coffee Break
04:15–06:00 pm
Multi-Scale Approaches
Chairs: Dirk Drasdo & Jan Hengstler
04:15 pm
Modelling of human haematopoietic cell production to optimize supportive cancer treatment
Markus Löffler (IMISE, Leipzig)
04:45 pm
Exploiting Evolutionary Trade-Offs as a Novel Cancer Therapy
Alexander Anderson (H. Lee Moffitt Cancer Center & Research Institute, Tampa)
05:15 pm
Clonal competition in the stem cell niche: New insights from 3D computation
Torsten Thalheim (Leipzig University, Leipzig)
05:30 pm
Heterogeneity and cell fate control in mouse embryonic stem cells
Ingmar Glauche (TU Dresden, Dresden)
05:45 pm
Multiscale Model of Liver Intoxication after APAP overdose
Noemie Boissier (Sorbonne Universités, Paris)
06:00–07:00 pm
Poster Session (odd numbers)
07:30 pm
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Conference Dinner & Party at Restaurant Bavarie at BMW Welt
Friday: April 8th
09:00–10:45 am
Systems Medicine I & Systems Pharmacology
Chairs: Werner Mewes & Frank Lammert
09:00 am
Linking Diseases, Drugs and the Druggable Proteome
Tudor Oprea (University of New Mexico, New Mexico)
09:30 am
Assimilation of Mammalian Cell Biology knowledge into Model Based Drug Development in Pharmaceutical Industry
Andrzej M. Kierzek (Simcyp, Sheffield)
10:00 am
Think Adjoint – Methods facilitating parameter estimation for genome-scale mechanistic dynamic models
Fabian Fröhlich (Helmholtz Zentrum München |TU München, Neuherberg)
10:15 am
Development of an integrative model to improve anemia treatment in non-small cell lung carcinoma patients
Agustin Rodriguez-Gonzalez (DKFZ | Heidelberg University | DZL, Heidelberg)
10:30 amAn expandable, multi-level, and multi-scale model for drug simulations of weight-loss and
type 2 diabetes
10:45 am
Gunnar Cedersund (Linköping University, Linköping)
Coffee Break
11:15 am–01.00 pm Systems Medicine II & Genetic/Epigenetic Mechanisms
Chairs: Thomas Höfer & Heribert Schunkert
11:15 am
Systems Genetic Approaches to Coronary Artery Disease - Toward Diagnostics and Therapies of Molecularly Defined Subcategories of Patients
Johan Björkegren (Mount Sinai Hospital, New York)
11:45 am
Dissecting analogue versus digital regulation in Polycomb-based epigenetics
Martin Howard (John Innes Centre, Norwich)
12:15 pm
Model-guided target identification for synergistic combination therapies in the DNA damage response pathway
Andreas Raue (Merrimack, Cambridge)
12:30 pm
Identification of genetic determinants of immune cellular homeostasis using genetically diverse mouse strains
Shai Shen-Orr (Technion-Israel Institute of Technology, Haifa)
12:45 pm
Assessing aneuploidy heterogeneity using single cell sequencing
Maria Colomé Tatché (Helmholtz Zentrum München, Neuherberg)
01:00 pm
Poster Prize Award & Closing Statement
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POSTER OVERVIEW
Image-based Systems Biology
PP 1-01 Label-free cell cycle analysis for high-throughput imaging flow cytometry
Thomas Blasi (Neuherberg)
PP 1-02 Cells commit to the cell cycle by rapid and irreversible inactivation of APCCdh1
Steven Cappell (Stanford)
PP 1-03 Image-based systems medicine: from mechanistic models to decision-support systems in the clinic
Gunnar Cedersund (Linköping)
PP 1-04 Investigation of Bile canaliculi formation and biliary transport in 3D in vitro liver cultures
Georg Damm (Berlin)
PP 1-06 Functional intravital imaging of acetaminophen induced liver injury and regeneration
Ahmed Ghallab (Dortmund)
PP 1-07 A Systems Survey of Progressive Host Cell Reorganization During Rotavirus Infection
Victoria Green (Zurich)
PP 1-08 A Qualitative Method of Identifying Stiff Regions in Fibrous Proteins
Sean Horan (Irvine)
PP 1-10 Image-based modeling of organogenesis
Christine Lang (Basel)
PP 1-11 Clustering-based classification of autophagy phenotypes in single cell images as a novel readout of autophagic activity
Paula Andrea, Marin Zapata (Heidelberg)
PP 1-12 Maldi imaging of a prostate cancer biopsy - a pilot study
Kjersti Rise (Trondheim)
PP 1-13 Complete evaluation of dynamics in an unlabelled live cell from a z-stack of super-resolved bright-field microscopic images
Renata Rychtarikova (Nove Hrady)
PP 1-14 3D reconstruction and quantitative analysis of liver human samples
Fabian Segovia-Miranda (Dresden)
PP 1-15 Quantitative analysis of tumor cell load based on diffusion-weighted mri and histology data
Yi Yin (Paris)
PP 1-16 Investigating Microenvironment-to-cell Signaling in 3D Spheroids through Imaging Mass Cytometry
Vito Zanotelli (Zurich)
PP 1-17 Analysing the Impact of Errors in Single Cell Tracking Experiments
Thomas Zerjatke (Dresden)
Single-Cell Systems Biology
PP 2-01 destiny – diffusion maps for large-scale single-cell data in R
Philipp Angerer (Neuherberg)
PP 2-02 Sensitive Detection of Rare Disease-Associated Cell Subsets via Representation Learning
Eirini Arvaniti (Zurich)
PP 2-03 Information theoretic analysis of interleukin-6-induced signalling by multi-colour flow cytometry
Ulrike Billing (Magdeburg)
PP 2-04 Refractory states imprinted in the NF- B system regulate encoding of temporal inflammatory signals
Christopher Boddington (Manchester)
PP 2-05 Wnt/Planar cell polarity signaling regulates commitment of intestinal stem cells to the secretory lineage
Anika Böttcher (Garching-Hochbrück)
PP 2-06 T cell immune responses generate diversity through linear cell-fate progression
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Michael Flossdorf (Heidelberg)
PP 2-07 Estimating single-cell regulatory heterogeneities from cell populations
Christiane Fuchs (Neuherberg)
PP 2-08 Universal time and diffusion pseudotime for single-cell data
Laleh Haghverdi (Munich)
PP 2-09 Dynamical Modeling of Clonal Evolution in Primary and Relapsed Follicular Lymphoma
Matthias Horn (Leipzig)
PP 2-10 An Interplay of Determinism and Stochasticity in the Information Flow via STAT3
Xiaoyun Huang (Heidelberg)
PP 2-11 Dynamic NF- B and E2F interactions control the priority and timing of inflammatory signalling and cell proliferation
Nicholas Jones (Manchester)
PP 2-12 Inferring cell ensemble models of heterogeneous cell populations by multi-experiment and multi-data-type fitting
Stefan Kallenberger (Heidelberg)
PP 2-13 Identifying mediators of disease co-morbidities by integrating omics data.
Gabi Kastenmüller (Munich)
PP 2-14 Unraveling signaling dynamic patterns with single cell mass cytometry Sunil Kumar (Rueschlikon)
PP 2-15 Too Young to Die: Age Structured Population Models Capture Cell Cycle Dependent Apoptosis from Snapshot Data Karsten Kuritz (Stuttgart)
PP 2-16 GenSSI: Generating Series Structural Identifiability on new Matlab versions
Thomas Ligon (Munich)
PP 2-17 Mechanistic modeling of subpopulation structures for multivariate single-cell data Carolin Loos (Neuherberg)
PP 2-18 Single cell clone transcriptomics derived from murine brown adipose tissue discloses cell type heterogeneity of brown preadipocytes
Dominik Lutter (Neuherberg)
PP 2-19 Heterogeneity of TLR4 signalling and robust pathogen sensing
Pawel Paszek (Manchester)
PP 2-20 Cell-to-cell heterogeneity unraveled by computational analysis of single-cell mass cytometry data: cell cycle patterns and trajectories
Maria Anna Rapsomaniki (Rüschlikon)
PP 2-21 Multiplexed Imaging Cytometry Analysis Toolbox (miCAT) Coupled to Imaging Mass Cytometry (IMC) Reveals Patterns of Cell Interactions Amongst the Heterogeneity of Breast Cancer Denis Schapiro (Zurich)
PP 2-22 Inferring gene regulation using pseudotemporal ordering of single cell snapshots
F. Alex Wolf (Neuherberg)
PP 2-25 Dimensionality Reduction to Networks: Single Cell Sign Consistency Models Enable Identification of Subpopulations with Distinct Signaling Network States in Mammalian Cancer Cells
Sophie Tritschler (Zurich)
Signalling Modelling
PP 3-01 Predicting T-helper cell differentiation and plasticity using logical modeling and model-checking
Wassim Abou-Jaoudé (Paris)
PP 3-02 Protein abundance of AKT and ERK pathway components governs cell-type-specific regulation of proliferation
Lorenz Adlung (Heidelberg)
PP 3-03 A system based time series analysis unravels proliferation to differentiation switch in erythroid progenitors cells upon erythropoietin stimulation
Geoffroy Andrieux (Freiburg)
PP 3-04 The sensitivity of oscillatory properties
Katharina Baum (Berlin)
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PP 3-05 Mathematical Modelling Suggests Differential Impact of β-TrCP Paralogues on Wnt/β-Catenin Signalling Dynamics
Uwe Benary (Berlin)
PP 3-06 Bringing Systems Biology to Material Science: Construction of an Information Processing Material
Raphael Engesser (Freiburg)
PP 3-07 Mathematical modelling of drug-induced receptor internalisation in HER2-positive breast cancer cell-lines
Mirjam Fehling-Kaschek (Freiburg)
PP 3-08 Simulation-based parameter estimation for kinetic data for the Raf-Mek-Erk pathway
Anna Fiedler (Neuherberg)
PP 3-09 Signal transduction analysis reveals key switches in inflammatory signaling during acute myocardial infarction determining disease recurrence
Heinrich Huber (Leuven)
PP 3-10 Investigating and Modulating Apoptosis Sensitisation in Cultured Cardiac Cells after Exposure to Doxorubicin Using Quantitative Biochemistry and Computational Systems Biology
Heinrich Huber (Leuven)
PP 3-11 Linking mechanistic understanding of cellular signalling in gastric cancer and cellular phenotypes
Sabine Hug (Neuherberg)
PP 3-12 Dynamic pathway modeling of IL-6 signaling to unravel mechanisms of Drug-Induced Liver Injury
Anja Jünger (Heidelberg)
PP 3-13 Auto-correlation of high-precision NF- B oscillation data for dynamic mean population models of TNFα signaling
Daniel Kaschek (Freiburg)
PP 3-14 Case Study for Attractor Detection of Asynchronous Logical Networks
Hannes Klarner (Berlin)
PP 3-15 Towards genome-scale mechanistic models of signal transduction
Marcus Krantz (Berlin)
PP 3-16 Therapeutic Response Modeling Towards Personalized Medicine
Jian Li (Munich)
PP 3-17 Analysis of the TRAIL and cisplatin induced apoptosis and MAPK-PI3K/AKT signal transduction pathways in melanoma with a probabilistic Boolean network approach
Philippe Lucarelli (Belvaux )
PP 3-18 Post-transcriptional regulation by sRNA in Synechocystis PCC 6803
Wolfgang Mader (Freiburg)
PP 3-19 A new logic of unified classification of intracellular processes
Ozar Mintser (Kyiv)
PP 3-20 Dynamic pathway modelling of TNFα signalling to unravel mechanisms of Drug Induced Liver Injury
Angela Oppelt (Heidelberg)
PP 3-21 Autocrine TGF-beta/ZEB/microRNA-200 signal transduction drives epithelial-mesenchymal transition: Kinetic models predict minimal drug dose to inhibit metastasis
Katja Rateitschak (Greifswald)
PP 3-22 Tailored Steady-State Constraints for Parameter Estimation in Non-linear Ordinary Differential Equation Models
Marcus Rosenblatt (Freiburg)
PP 3-23 Dynamics of IL-6-induced classic and trans-signaling
Heike Rummel (Magdeburg)
PP 3-24 Selective Control of Upregulated and Downregulated Genes by Temporal Patterns and Doses of Insulin
Takanori Sano (Tokyo ) PP 3-25 Sensing and antiviral signaling by RIG-I
Darius Schweinoch (Greifswald)
PP 3-27 One model to rule them all
20
Bernhard Steiert (Freiburg)
PP 3-28 Systematic Analysis of Time-Resolved Transcriptional Signature of the Cross-Talk Between HGF and IL6 Reveals Genetic Program of Hepatocyte Proliferation Control
Sebastian Vlaic (Jena)
PP 3-29 Mathematical modeling of the impact of acetaminophen on the HGF-induced cellular responses in primary mouse hepatocytes Artyom Vlasov (Heidelberg)
PP 3-30 Modeling signaling dynamics with differential equations: are single-cell data and their analysis useful?
James Wade (Zurich)
Metabolism
PP 4-01 New Standard Resources for Systems Biology: BiGG Models and Visual Pathway Editing with Escher
Andreas Dräger (Tübingen)
PP 4-02 Combination of Mass Spectrometry-Based Proteomics and Mathematical Modelling Predicts Therapy Targets of Liver Cancer
Merve Erdem (Aachen)
PP 4-03 Glucose is not the only source for lactate production by hepatocellular carcinoma cells Jurgen Haanstra (Amsterdam)
PP 4-04 Metabolic profiling of CHO-K1 cells adapted to glutamine-free media
Michael Hanscho (Vienna)
PP 4-05 The relative importance of kinetic mechanisms and variable enzyme abundances for the regulation of hepatic glucose metabolism - Insights from mathematical modeling
Hermann-Georg Holzhütter (Berlin)
PP 4-06 Significance test for difference between paired temporal observations
Ivan Kondofersky (Neuherberg)
PP 4-07 A mathematical model of the bile enterohepatic circulation
Krystian Kubica (Wroclaw)
PP 4-08 New insights in the relation of liver and adipose tissue via Hedgehog Signalling
Madlen Matz-Soja (Leipzig)
PP 4-09 Why Respirofermentation? Explaining the Warburg effect in tumour (and other) cells by a minimal model
Philip Möller (Jena)
PP 4-10 Context-specific metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic networkMaria Pires Pires Pacheco (Esch-sur-aLzette)
PP 4-11 How Hedgehog signaling pathway activity controls steroidogenesis in the liver
Christiane Rennert (Leipzig)
PP 4-12 Cause and Cure of Sloppiness in Ordinary Differential Equation Models
Christian Tönsing (Freiburg)
PP 4-13 Sexual dimorphism during development of cyp51 liver conditional knockout mice
Ziga Urlep (Ljubljana)
21
Program /
Abstracts
Multi-Scale Approaches
PP 5-01 Tissue architecture representation in pharmacological models. Insights from liver.
Noemie Boissier (Paris)
PP 5-02 Agent-based modelling characterises the effect of localized versus spread damage among mitochondrial population
Giovanni Dalmasso (Heidelberg)
PP 5-03 Morpheus 2.0: an open-source framework for multi-scale multicellular systems biology
Walter de Back (Dresden)
PP 5-04 Modelling And Simulation Of Tumour Growth Compared With Xenograft Models And Db-scTRAIL Therapy In 2D Cell Culture Population.
Simona Galliani (Stuttgart)
PP 5-05Data Needs Structure: Data and Model Management for Distributed Research Networks in Systems Biology and
Systems Medicine
Martin Golebiewski (Heidelberg)
PP 5-06 An integrated temporal molecular response of vascular endothelial cells exposed to ionizing radiation
Olivier Guipaud (Fontenay-Aux-Roses)
PP 5-07 Systems analysis of the structural and molecular changes along the dynamics of liver fibrosis development
Seddik Hammad (Mannheim)
PP 5-08 Multiscale modeling of liver regeneration
Stefan Hoehme (Leipzig)
PP 5-10 Modeling Tumor-Immune System Interaction
Nick Jagiella (Munich)
PP 5-11 Can in vivo 31P MRS assay of myocardial PCr/ATP ratio homeostasis test model predictions?
Jeroen Jeneson (Amsterdam)
PP 5-12 Multiscale modelling of hepatocellular carcinoma and transarterial chemoembolisation therapy
Tanvi Joshi (Stuttgart)
PP 5-13 Personalized liver function tests: A Multiscale Computational Model Predicts Individual Human Liver Function From Single-Cell Metabolism
Matthias König (Berlin)
PP 5-14 Model-based predictions of inflammatory patterns in the breast lobular epithelium in relation to epithelial cell turnover
Juan Carlos, Lopez Alfonso (Dresden)
PP 5-15 Modeling disease Progression in Myeloproliferative Neoplasms, a Systems Medicine Approach Maryam Montazeri (Aachen)
PP 5-16 Development of a Multiscale Systems Biology Approach to Study Atherosclerotic Plaque Progression in WTLdlrKO and M-S196ALdlrKO mice: Integrating Mathematical and Murine Models
Cesar Pichardo-Almarza (London)
PP 5-17 3D multiscale modeling of vascularized tumor development inside colon
Elena Shchekinova (Stuttgart)
PP 5-18 Interplay of nucleosome positioning, covalent modifications and transcription factor binding
Vladimir Teif (Colchester )
PP 5-19 Modelling of tertiary lymphoid organ development in the context of kidney transplant.
Alexey Uvarovskii (Braunschweig)
PP 5-20 Parallel analysis of the proteome, phosphoproteome and N-terminome to characterize altered platelet functions in the human Scott syndrome
22
René Zahedi (Dortmund)
Systems Medicine & Systems Pharmacology
PP 6-01 SIMPLEX: a combinatorial multimolecular omics approach for systems biology
Robert Ahrends (Dortmund)
PP 6-02 Baseline Matters: the Effect of Initial Immune State on Outcome of Septic Patients
Ayellet Alpert (Haifa)
PP 6-03 Dynamics of the tumor-infiltrating lymphocyte repertoire in melanoma and pancreatic cancer
Lena Maetani Appel (Heidelberg)
PP 6-04 Markov-Chain Monte-Carlo methods to analyze mechanistic disease simulators: Applicability and shortcommings
Benjamin Ballnus (Neuherberg)
PP 6-05 Modeling biliary fluid dynamics reveals possible mechanism for dose-response and personalization of UDCA treatment in PSC
Lutz Brusch (Dresden)
PP 6-06 Improving non-invasive liver function diagnostics
Sascha Bulik (Berlin)
PP 6-07 Integrative analysis of chemical high-throughput screens uncovers novel biological information. Monica Campillos (Munich)
PP 6-08 A NAT2 Pharmacogenomic based PBPK model of Isoniazid in Men and Its Application in Adjusting Tuberculosis Chemotherapy Henrik Cordes (Aachen)
PP 6-09 Validation of a pregnancy population physiologically-based pharmacokinetic model for renally cleared drugs
Andre Dallmann (Münster)
PP 6-10 A hypothesis-free, systemic approach to identify pharmacological targets for anxiety disorders
Michaela Filiou (Munich)
PP 6-11 The NormSys registry for modeling standards in systems and synthetic biology
Martin Golebiewski (Heidelberg)
PP 6-12 Systems biology of MHC class I antigen presentation studied in human cancer cell lines
Jennifer Hahlbrock (Mainz)
PP 6-13 NormSys & CHARME Two initiatives that aim at Harmonizing the Standardization Processes for Data Exchange in Systems Biology
Susanne Hollmann (Potsdam)
PP 6-14 A dynamic model of bile acid transport in the HepaRG cell-line
Daniel Kaschek (Freiburg)
PP 6-15 Modeling Individual Time Courses of Thrombopoiesis During Multi-Cyclic Chemotherapy
Yuri Kheifetz (Leipzig)
PP 6-16 DESIGNING AND SIMULATION STUDIES OF Mycobacterium Tuberculosis DNA GYRASE.
Vidya Niranjan (Bengaluru)
PP 6-17 Early biomarkers and magnetic resonance imaging for diagnosing Bronchopulmonary Dysplasia
Steffen Sass (Neuherberg)
PP 6-18 Using physiologically-based pharmacokinetic modelling to analyze the effect of hepatic impairment on drug detoxification capacity at the whole-body level
Arne Schenk (Aachen)
PP 6-19 Dynamical modelling of the murine immune response to pneumococcal lung infection with and without antibiotic treatment
Sibylle Schirm (Leipzig)
PP 6-20 Modelling immuno-chemotherapy of lymphomas
Markus Scholz (Leipzig)
PP 6-21 Nuclear-encoded mitochondrial genes in hypercholesterolema and atherosclerosis progression
Baiba Vilne (Munich)
23
Program /
Abstracts
Systems Medicine & Genetic/ Epigenetic Mechanisms
PP 7-01 Acute and Chronic inflammation and mast cell activation, IN Silico
Ablikim Abdukerim (Amsterdam)
PP 7-02 Prioritization of candidate causal genes in rare genetic diseases through integration of exome sequencing data and biological databases
Ziga Avsec (Garching)
PP 7-03 A glance at smoking effect on preeclamptic placenta with transcriptional regulation approach
Büşra Aydin (Istanbul)
PP 7-04 Post-transcriptional gene regulation in mitochondrial disorder patients
Daniel Magnus Bader (Munich)
PP 7-05 Quantitative analysis of gene expression based on large distance histone acetylation modifications as enhancers
Elham Bavafaye Haghighi (Neuherberg)
PP 7-06 Time series analysis of macrophage activation and signalling from RNAseq data
Francois Bergey (Berlin)
PP 7-07 In situ transcriptomics reveals metabolic and immunological zonation in human liver
Mario Brosch (Dresden)
PP 7-08 Deep learning of regulatory factors of the transcriptional landscape
Gökcen Eraslan (Neuherberg)
PP 7-09 Integrant roles of microRNAs and transcription factors in ovarian cancer transcriptional regulatory network
Esra Gov (Istanbul)
PP 7-10 Cigarette smoking increased expression of the G protein-coupled receptor 15 by change in CpG methylation
Tina Haase (Hamburg)
PP 7-11 Genetic analysis of spontaneous (non-toxic) liver fibrosis in a congenic mouse model
Rabea Hall (Homburg)
PP 7-12 ChIP-Seq pipeline for the identification of differential binding of the glucocorticoid receptor in high fat versus low fat diet in mouse livers
Johann Hawe (Neuherberg)
PP 7-13 Experimentally-based mechanistic modeling to study t helper 17 cell differentiation
Jukka Intosalmi (Espoo)
PP 7-14 Uncovering potential therapeutic targets and prognostic biomarkers of ovarian tissue related diseases via systems biomedicine approach
Medi Kori (Istanbul)
PP 7-15 Transcriptional profile of Aldara induced skin-reaction in humans
Linda Krause (Neuherberg)
PP 7-16 A next generations sequencing toolbox with application to schizophrenia
Robert Küffner (Neuherberg)
PP 7-17 The properties of Ewing Sarcoma driver cells - widen the view beyond the master fusion gene
Nikhil Vinod Mallela (Muenster)
PP 7-18 Genome-wide interplay of DNA methylation with genetic variants on the human metabolism
Sophie Molnos (Munich)
PP 7-19 Reconstruction of the FANCA interactome and discovery of new putative oncogenes in head and neck squamous cell carcinoma.
Adriana Pitea (Neuherberg)
PP 7-20 Modelling the crosstalk between DNA methylation and histone modifications in cancer
Jens Przybilla (Leipzig)
PP 7-21 Importance of rare gene copy number alterations for personalized tumor characterization
Michael Seifert (Dresden)
PP 7-22 Microarrays in large-scale phenotyping at the GMC
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Julia Söllner (Neuherberg)
PP 7-24 ModuleDiscoverer: a novel approach to the discovery of disease modules refining molecular characterization of diet induced rodent models of fatty liver.
Christian Tokarski (Jena)
PP 7-25 Differential gene expression in atrial fibrillation patients
Lars von den Driesch (Neuherberg)
PP 7-26 Integrative analysis of exome, transcriptome and cellular bioenergetics profiles in patients with rare mitochondrial diseases
Vicente Yepez (Garching)
25
Program /
Abstracts
The MTZ®-Award for Medical Systems Biology 2016
2016 will be a very important year for the MTZ®foundation. The foundation was established in 2006, so that the
MTZ®foundation will celebrates the 10th anniversary. Furthermore it will be the 5th time, that the MTZ®foundation will
honour three young scientists for their excellent outstanding dissertations.
Adopting the motto “For a better future ...” the MTZ®foundation promotes science and research in the field of Human
Medicine. The foundation supports the young generation of scientific talent and promotes their world class and cutting - edge
work in biomedical stem cell and genetic research, which is in conformity with the highest bioethical standards. It promotes
an interdisciplinary approach, that is, a conscious bringing together of traditional scientific approaches with medical systems
biology.
Thus, it reaches new dimensions in pathogenic research and drug development.
Bioethical questions play a very important role in the work of the MTZ®foundation. Therefore, it is a special concern of her
work to indicate the opportunities, but also the limits and risks of this type of research. Bioethical issues play an important
role when it comes to maintaining the quality of life within an increasingly ageing society.
Germany is a country with much young research talents. The MTZ®foundation aims to support these excellent talents and
their potential to be outstanding in the field and seeks to make sure that they get the special attention and public recognition
that they deserve.
Trial and error and at least success – these elements characterize science and research. These elements are also part of the
work of the MTZ-Awardees. The MTZ®foundation wants to motivate them for new research and we are sure, that a MTZ®Award could be a very important step for the further career of the scientific young talents.
Scientists from Germany with an international reputation help us to grant awards for research work of world class. The
cooperation between the Federal Ministry of Education and Research (BMBF) and these internationally recognized
researchers within the MTZ®-Panels provides a rigorous selection of the MTZ®-Awardees.
In the meantime the MTZ®-Award is nationally recognized as a national brand - the first important scientific prize for
innovative research in the vita of the younger generation of promising scientific talent.
Talented young researchers are eager to be discovered and to be promoted early.
The MTZ®foundation wishes to be central to that process. That is why it has developed a special students` program to inspire
students of the natural sciences and hence, to assure research of world class in Germany in future. A good example is the
launch of the virtual MTZ®online “für Schüler”. Participating at this part of the website allows the students of German
secondary schools to explore also the fascinating world of medical Systems Biology.
26
MTZ®-Award for Medical Systems Biology 2016
Offering the MTZ®-Award for Medical Systems Biology 2016 should foster future-oriented research approaches in the
field of medical Systems Biology. The award is granted by the MTZ®foundation in order to recognize outstanding excellent
dissertations in the field of medical Systems Biology, with the aim of attracting special attention and public recognition to
these promising researchers. In this endeavour, the MTZ®foundation cooperates with the Federal Ministry of Education and
Research (BMBF). The award promotes young scientists that outstandingly contribute to the research in Systems Biology.
Our main focus lies on individualised medicine and systems medicine. In this context systems biology is the foundation for
a medicine that responds to the individual needs of each patient. The way to a more personalized medicine will become more
important in future funding.
The prize is divisible and will be assigned for the three best dissertations. The award is being offered for the 5th time.
We are very proud to present our three award winners 2016. And we are looking forward with great pleasure to the prize
ceremony at the beginning of the SBMC 2016.
The managing board
Monika Zimmermann
Thomas Zimmermann
CONTACT
For more information about the MTZ®foundation please contact us and visit our website
www.mtzstiftung.de.
Or just have a look to our new flyer with a short summary of our “MTZ-world”
www.mtzstiftung.de/sites/mtzstiftung.de/myzms/content/e191/e705/e6111/ImageFlyer10JahreMTZstiftung.pdf.
27
Program /
Abstracts
MTZ Award
Robustness of MAPK Signalling
Witzel F. 1, Fritsche-Guenther, R., Lehmann, N., Sieber, A., Herr, R., Braun, S., Morkel, M., Brummer, T., Sers, C., Blüthgen, N.
1 Charité-Universitätsmedizin Berlin, Germany
Variability in gene expression leads to a broad distribution of signalling protein levels in a population of clonal cells, posing a major
challenge for the reliability of signal transduction. In my thesis I have investigated how the MAPK pathway, a central signalling
pathway controlling various cell fate decisions, remains functional at different expression levels of the terminal kinase Erk1/2.
Surprisingly, steady-state signalling remains unaffected when Erk levels are reduced or Erk is highly overexpressed.
Moreover, the kinetics and intensity of immediate early gene induction is unaffected when Erk is strongly overexpressed,
which indicates the presence of robust pathway features. When analysing kinetic activation data, we find that Erk activity
pulses are higher in Erk overexpressing cells when compared to normal Erk levels, but Erk activity is downregulated much
quicker. Mathematical modelling points to a combination of fast negative feedback regulation and sequestration phenomena
that lead to this strong pathway robustness both at the level of steady-state signaling as well as at pathway output.
Taken together, our data show that changes in kinase levels do not necessarily change the pathway activity and might explain
the low prevalence of Erk overexpression in diseases such as cancer.
MicroRNAs decrease protein expression noise
Schmiedel, J. M.1,2,3, Klemm, S.4, Zheng, Y. 3, Sahay, A. 3, Blüthgen, N.1,2,#, Marks, D.S. 5,#, van Oudenaarden, A. 3,6,7,#
1 Institute of Pathology, Charité-Universitätsmedizin, Berlin, Germany
2 Integrative Research Institute for the Life Science and Institute for Theoretical Biology, Humboldt Universität, Berlin, Germany
3 Department of Physics, Massachusetts Institute of Technology, Cambridge, USA
4 Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, USA
5 Department of Systems Biology, Harvard Medical School, Boston, USA
6 Department of Biology, Massachusetts Institute of Technology, Cambridge, USA
7 Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences and University Medical Center Utrecht, Utrecht, The Netherlands
#These authors contributed equally
MicroRNAs repress many genes in metazoan organisms by accelerating mRNA degradation and inhibiting translation. It has
been speculated that microRNAs not only regulate mean protein expression but also protein expression variability, or noise.
Here we use mathematical modeling and single cell reporter assays to show that microRNAs – in conjunction with increased
transcription - decrease protein expression noise for lowly expressed genes, but increase noise for highly expressed genes.
Genes that are regulated by multiple microRNAs show more pronounced noise reduction. We estimate that hundreds of genes
in mouse embryonic stem cells have reduced noise due to substantial microRNA regulation. Our findings therefore show how
microRNAs confer precision to protein expression and offer plausible explanations for the commonly observed combinatorial
targeting of endogenous genes by multiple microRNAs as well as the preferential targeting of lowly expressed genes.
Models of Influenza A Virus Infection: From Intracellular Replication to Virus Growth in Cell
Populations
Heldt, F. S.1, Kupke, S. Y.1, Frensing, T.1,2, Reichl U.1,2
1
2
Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
Chair of Bioprocess Engineering, Otto von Guericke University Magdeburg, Germany
Influenza A viruses are human respiratory pathogens that cause annual epidemics with 3 to 5 million cases of severe illness
and up to 500,000 deaths. Yet, there are currently only two classes of antiviral drugs licensed for treatment and resistant
virus strains are on the rise.
In order to elucidate the viral life cycle and identify new targets for antiviral therapy, we constructed a multiscale model of
influenza A virus infection. It accounts for the key steps of intracellular virus replication including virus entry, viral genome
and mRNA synthesis, and virus release. The model combines this information with infection dynamics at the cell-population
level, i.e., the transmission of virions between host cells and the kinetics of virus-induced apoptosis. This integrated
modelling approach allows us to capture a variety of experimental data from cell-culture experiments and to predict the
most promising targets for direct-acting antivirals.
In particular, we find that interference with the viral polymerase represent a highly efficient treatment strategy because it
interrupts the autocatalytic cycle of viral RNA synthesis. Moreover, inhibitors of nuclear export and virus assembly/release
can readily reduce virus titers. By contrast, targeting the steps of virus entry primarily delays virus spreading but does not
protect host cells from infection in vitro.
In summary, our multiscale model provides a systems-level understanding of viral infection and is therefore an ideal
platform to include further levels of complexity such as the host’s immune response and between-host transmission.
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INVITED TALKS
PT 01
Building models of cell organization, differentiation and perturbation directly from
microscope images
Murphy, R.1
1
Murphy Lab, Pittsburgh, United States
Ray and Stephanie Lane Professor of Computational Biology and Professor of Biological Sciences, Biomedical Engineering and
Machine Learning, Carnegie Mellon University, Pittsburgh, USA
Honorary Professor of Biology and Senior External Fellow, Albert Ludwig University of Freiburg, Germany
Machine learning methods are critically needed for building systems models of cell and tissue behavior and for studying their
perturbations. Such models require accurate, cell-type specific information about the shape and distributions of subcellular
structures and the distributions of proteins, RNAs and other macromolecules in order to be able to capture and simulate
cellular spatiotemporal dynamics. Most efforts towards this goal have used either compartmental models that are not
spatially-realistic or geometries constructed from an image of a single cell that are difficult to generate and do not capture
cell-to-cell variation. We have developed tools to build generative models of cell organization directly from microscope images
of many cells. Generative models are capable of producing new instances of a pattern that are expected to be drawn from the
same underlying distribution as those it was trained with. Our open source system, CellOrganizer (http://CellOrganizer.
org), currently contains components that can build probabilistic generative models of cell, nuclear and organelle shape,
organelle position, and microtubule distribution. These models capture heterogeneity within cell populations, and can be
dependent upon each other and can be combined to create new higher level models. The parameters of these models can be
used as a highly interpretable basis for analyzing perturbations (e.g., induced by drug addition), and generative models of
cell organization can be used as a basis for cell simulations to identify mechanisms underlying cell behavior. Recent work
has focused on learning relationships between different cellular structures and structures and on constructing models of the
dynamics of changes in cell organization.
PT 02
Image-driven modeling of limb development across 3D space and time
Diego, J.1
1
EMBL-CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), UPF, Barcelona, Spain
Organ development is a multi-scale process. At the molecular scale, regulation of genes and proteins creates complex
networks which control the activities of cells (cell division, migration, cell fate decisions, differentiation, and many other
activities). This molecular control system includes an extracellular part – secreted ligands which move between cells allowing
cell-cell communication (such as FGFs, WNTs, etc). The coordination of thousands of cells by this molecular network, leads
to large-scale morphogenesis at the scale of tissues and organs. However, these large-scale movements also feedback to
the molecular scale: the movement of tissue regions relative to each other, causes cells to receive dynamically changing
concentrations of signaling molecules, and this in turn changes the activation or repression of genes.
A full understanding of how genes control organogenesis will require multi-scale computer modeling, and we have chosen
vertebrate limb development as a model system to explore this problem. Such a model should be based on data about dynamic
tissue shapes and spatial distributions of gene activities. Traditional high-throughput and “omics” technologies do not
preserve spatial information, and we are therefore using 3D imaging from OPT and SPIM to generate geometric and spatial
data for the model. We will present results on our finite-element modeling which is allowing us to tackle this complex
problem.
PT 03
Systems Biology in Action: Design of Cancer Combination Therapy
Sander, C.1
1
Dana-Farber Cancer Institute and Harvard Medical School, Boston, United States
Cells and organisms have evolved as robust to external perturbations and adaptable to changing conditions. This capacity
poses severe problems for cancer patients. Some targeted anti-cancer drugs work remarkably well, yet resistance to
therapeutic perturbation is almost certain to emerge.
We address three scientific challenges:
(1) Discover the adaptation of cell systems in response to drugs and how to block the escape pathways by combinatorial
intervention;
29
Program /
Abstracts
(2) Describe empirically the landscape of oncogenic alterations for improved and personalized therapeutic approaches, using
The Cancer Genome Atlas, and
(3) Derive executable network models for cancer cells that guide the development of combination therapy, using perturbation
biology (systematic perturbation coupled with rich observation of response, such as changes in protein levels and protein
modifications).
—— Work done in collaboration with Anil Korkut, Evan Molinelli, Martin Miller, Wei Qing Wang, Xiaohong Jing, Alex Root,
Deb Bemis, David Solit, Christine Pratilas, Emek Demir, Arman Aksoy, Onur Sumer, Özgün Babur, Andrea Pagnani, Riccardo
Zecchina, Giovanni Ciriello, Nikolaus Schultz, Sven Nelander, Debora Marks et alii.
Link: http://bit.ly/1loSE84 - Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor
resistant melanoma cells.
PT 04
Comprehensive analysis of single cell states through time and space by mass cytometry
Bodenmiller, B.1
1
Bodenmiller Lab, Zurich, Switzerland
Each year approximately 1.1 million new cases of breast cancer are diagnosed and about 0.3 million women worldwide
die from this disease. Tumor metastases, relapse, and resistance to therapy are the main causes of death in breast cancer
patients. Communication between heterogeneous cancer cells and normal cells in the so-called tumor microenvironments
(TME) drives cancer development, metastasis formation, and drug resistance. To understand the TME and its relationship to
clinical data, comprehensive investigation of the components of the microenvironment and their relationships is necessary.
We recently developed a novel imaging modality based on mass cytometry, called imaging mass cytometry (IMC) that enables
this type of study. In IMC, tissues are labelled with antibodies that carry pure metal isotopes as reporters. Theoretically
135 antibodies can be visualized simultaneously; in practice, we routinely quantify 44 markers. The antibody abundance
is determined using laser ablation coupled to inductively coupled plasma mass spectrometry. Here we demonstrate the
capability of IMC and show the results of the analysis of hundreds of breast cancer samples by IMC. To extract biological
meaningful data and potential biomarkers from this dataset, we developed a novel computational pipeline for the interactive
and automated analysis of large-scale, highly multiplexed tissue image datasets. Our IM data and downstream data analysis
revealed a surprising level of inter- and intra-tumor heterogeneity and diversity within known human breast cancer
subtypes as well as the stromal cell types in the TME. Furthermore, we identified cell-cell interaction motifs in the tumor
microenvironment that correlated with clinical outcomes.
In summary, our results show that IMC provides targeted, high-dimensional, subcellular resolved images of tissue samples.
The identified spatial relationships among complex cellular assemblies have potential as biomarkers. We envision that IMC
will enable a systems biology approach to diagnosis of disease and will ultimately guide treatment decisions.
PT 05
Using single-cell genomics to study early development
Marioni, J.1
The European Bioinformatics Institute (EBI), Hinxton, Great Britain
1
Gastrulation and the specification of the three germ layers are key events in animal development. However, molecular
analyses of these processes have been limited due to the small number of cells present in gastrulating embryos. With recent
developments in the field of single-cell biology however, it is now possible to overcome these limitations and to characterize,
for the first time at the single-cell level, how cell fate decisions are made.
In this presentation I will discuss data generated to study cell fate specification in mouse, as well as the computational
strategies we have developed to model such data. I will then illustrate how these data can provide insight into germ layer
specification and early erythropoiesis.
PT 06
Specificity and Noise in NFkB Signaling
Hoffmann, A.1
University of California, Los Angeles, Signaling Systems Lab, Los Angeles, United States
1
Now classic studies revealed complex, stimulus-specific dynamics of NFkB signaling. This led to the hypothesis that the
dynamics of signaling constitute a code that allows the cells to transmit information about the external environment
(pathogen threats, inflammatory cytokines etc.) to the nucleus to regulate gene expression. However, the information
30
carrying capacity of this dynamical code has remained unclear, largely because available experimental set ups involved
cell lines whose adaptation to cell culture conditions rendered them less responsive to many stimuli than primary cells.
To examine the responses of primary macrophages, we developed a new NFkB fluorescent reporter mouse and image
analysis tools for quantifying NFkB translocation in live cell tracking experiments. These tools enabled unprecedented high
throughput experimentation of NFkB activation dynamics allowing us to quantify information transmission capacity and to
identify informative dynamical features. These features are recapitulated by surprisingly simple design principles common to
ligand-receptor signaling models that are coupled to the core IkB-NFkB model that is capable of producing both oscillatory
and non-oscillatory NFkB responses. I will discuss our recent and ongoing studies.
Werner, S.L., Barken, D., Hoffmann, A. 2005 Stimulus-specificity of gene expression programs determined by temporal
control of IKK activity. Science, 309, pp.1857-61. PMID: 16166517
Behar, M., Barken, D., Werner, S.L., Hoffmann, A. 2013 The Dynamics of Signaling as a Pharmacological Target. Cell, 155,
pp.448-461. PMID: 24120141, PMC3856316
Selimkhanov, S.*, Taylor, B.*, Yao, J., Pilko, A., Albeck, J., Hoffmann, A.+, Tsimring, L.+, Wollman, R.+ 2014 Accurate
information transmission through dynamic biochemical signaling networks. Science, 346, pp.1270-3. PMID: 25504722.
Cheng, Z.*, Taylor, B.*, Rios, D., Hoffmann, A. 2015 Distinct Single Cell Signaling Characteristics Conferred by the MyD88
and TRIF Pathways in TLR4 Activation. Science Signaling, 4(161):ra11
PT 07
Signals, effectors, information and decisions
Rand, D.1
University of Warwick, Warwick Systems Biology, Coventry, Great Britain
1
I will discuss a new theoretical approach to information and decisions in signalling systems. In this approach the value of
the information in the signalling system is defined by how well it can be used to make the “correct decisions” when those
“decisions” are made by molecular networks. As part of this I will introduce a new mathematical method for the analysis and
simulation of large stochastic non-linear oscillating systems. This allows an analytic analysis of the stochastic relationship
between input and response and shows that for tightly-coupled systems like those based on current models for signalling and
clocks, this relationship is highly constrained and non-generic.
PT 08
Hepatic ethanol metabolism
Bergheim, I.1
1
Friedrich-Schiller-Universität Jena, Biologisch-Pharmazeutischen Fakultät, Jena, Germany
Ethanol has been consumed for many centuries and is still one among the leading causes of death and injury world-wide.
However, besides being ingested mainly through the consumption of alcoholic beverages ethanol is also constantly produced
by intestinal bacteria. Both, ethanol stemming from foods and beverages but also ethanol produced by intestinal bacteria,
is due to the lipophilic and hydrophilic properties of ethanol readily taken up in the stomach and even more so in the small
intestine. In the liver, alcohol is primarily metabolized by cytosolic alcohol dehydrogenase (ADH) to acetaldehyde which is
further metabolized to acetate by mitochondrial aldehyde dehydrogenase (ALDH) 2. Both enzymes use NAD+ as a cofactor,
producing a reducing equivalent NADH in both steps. The increased production of NADH has been suggested to be involved
in many dehydrogenase-related reactions in the cytoplasm and mitochondria thereby altering energy supply and fatty acids
oxidation. Besides ADH, members of the cytochrome P450 family and herein especially the ethanol-inducible cytochrome
P 450 2E1 (CYP2E1) are key enzymes involved in the elimination of ethanol, metabolizing ethanol to acetaldehyde using
NADPH + H+ as cofactor and molecular O2. Here, ethanol metabolism and its consequences in regards to the damaging
effects of ethanol as well as possible modulations of enzymes involved in ethanol metabolism in the liver will be reviewed in
detail.
PT 09
Computational modeling of human metabolism and its application to Parkinson’s disease
Thiele, I.1
Université du Luxembourg, Luxembourg Centre for Systems Biomedicine, Esch-sur-Alzette,
1
The human metabolic network, Recon 2, captures physiological and biochemical knowledge on human metabolism in a
stoichiometrically accurate manner and is amenable to computational modeling. Recon 2 has be applied by the systems
biology community to understand normal and cellular disease states as metabolism has been shown to play an important
31
Program /
Abstracts
role in many complex human diseases. I will present our work on using Recon 2 to investigate drug-diet interactions in
Parkinson’s disease.
The gastrointestinal tract is the site of absorption and metabolism of nutrients and xenobiotics. Several drug-diet
interactions take place in the small intestine, such as levodopa (L-DOPA) and high protein diet. These interactions can reduce
the drug efficacy. Levodopa has been the gold standard treatment for Parkinson‘s disease treatment since almost 50 years.
Despite its efficacy, levodopa leads to fluctuations in the motor response as a consequence of several factors, including diet.
Recently, the intestinal transporters of levodopa has been identified in vitro as a neutral and dibasic amino acids exchanger,
which provided a molecular basis for the observed competition and suggested a basolateral trans-stimulation of levodopa
secretion with amino acids. We have translated these in vitro observations to predict the in vivo bioavailability of levodopa
with different diets. Using a combined physiologically based pharmacokinetic and constraint-based reconstruction and
analysis approach, we predicted that a serine rich diet could lead to a 10% increase in levodopa bioavailability. A clinical trial
published in January 2016 confirmed our predictions. The results showcase the importance of mathematical modeling as a
valuable tool for in vitro in vivo extrapolation.
Recon 2, and its continuous updates, are accessible through the Virtual Metabolic Human web portal (http://vmh.uni.lu),
which links human metabolism to gut microbial metabolism and hundreds of diseases.
PT 10
Modelling of human haematopoietic cell production to optimize supportive cancer
treatment
Markus Löffler (Universität Leipzig, Leipzig)
PT 11
Exploiting Evolutionary Trade-Offs as a Novel Cancer Therapy
Anderson, A.1
1
Anderson Lab, Tampa, United States
Solid tumours export metabolically derived acid into surrounding stroma. We view this microenvironmental acidosis as a
niche construction evolutionary strategy in which acid-producing/acid-adapted cancer cell phenotypes benefit by decreasing
the fitness of non-adapted stromal competitors, promoting local invasion. These phenotypic properties, in turn, promote
transition from in-situ to invasive cancer and a progressive expansion of primary or metastatic tumours. However, there is
a significant cost for maintaining an acid-producing/adapted phenotype due to reduced efficiency in energy production and
increased energy demand for adaptations to the acidic environment. We hypothesize that this cost may be cancers Achilles’
heel and a novel route for therapeutic intervention. We have generated a multiscale mathematical model that predicts
that even subtle perturbations in tumour extracellular pH (pHe) can dramatically alter the emergence and progression of
invasive and non-invasive tumour populations. We investigated these eco-evolutionary dynamics and model predictions
in the TRAMP (TRansgenic Adenocarcinoma of the Mouse Prostate) model. Consistent with the model predictions, our
experiments demonstrated that an increase of intratumoural pHe by only 0.2 pH units prevented transition from in situ
to invasive cancer during carcinogenesis and significantly reduced growth and invasion in primary and metastatic cancers.
Taken together, our results demonstrate a novel strategy that exploits evolutionary trade-offs to steer the tumour population
towards less invasive phenotypes.
PT 12
Linking Diseases, Drugs and the Druggable Proteome
Oprea T.1
UNM Department of Internal Medicine, Translational Informatics Division, Albuquerque, United States
1
The Illuminating the Druggable Genome Knowledge Management Center (IDG KMC) evaluates, organizes and distils
more than 80 protein-centric and over 20 gene-centric resources for over 20,000 curated human proteins (UniProt). The
IDG KMC currently focuses on four protein families: G-Protein Coupled Receptors, Nuclear Receptors, Ion Channels
and Kinases. However, data integration concerns all human proteins curated in UniProt. Data wrangling, coupled with
algorithmic processing, text mining of drug labels, patents, medical literature, as well as human curation and drug-target
ontology development, yield emergent properties and knowledge for target-disease associations. Tissue expression data
from GTEx, the Human Proteome Map , the Human Protein Atlas and other sources, disease-centric text mining, genomewide association studies and other resources are integrated using a number of specialized ontologies, e.g., the Brenda
Tissue Ontology and the Disease Ontology. IDG KMC is also developing a specific drug-target ontology to better integrate
32
chemogenomic and drug information. Using metrics derived from text mining and gene reference into function, as well
as the number of antibodies, IDG KMC catalogs 20,204 proteins. Of these, ~38% (7,681) are categorized as “Tdark”, i.e.,
proteins that lack functional information and disease relevance – the so-called ‘ignorome’. Another 592 proteins (2.9%) have
a confirmed drug mechanism of action (“Tclin”) - see box plot. The other two categories reflect levels of literature, functional
and disease annotations (“Tbio”), and knowledge about (potent) small molecules (“Tchem”), respectively.
From genomics to pharmaco- and clinical informatics, this integrated array of data and machine learning models is currently
used to prioritize “Tdark” proteins for further experiment via the 7 NIH funded IDG Technical Development grants, the
International Mouse Phenotype Consortium, and other collaborations. An interactive web-based tool, TIN-X, explores the
relationship between diseases and proteins, and is currently focused on the 4 IDG protein families. The IDG KMC interface
portal, Pharos, supports mining and interactive browsing of this multi-dimensional data collection, which provides
informative summaries for the broader scientific community. This integrative effort led to the following observations: i) there
appears to be a knowledge deficit, i.e., we lack understanding of protein function for 38% of human proteome; ii) less than
3% of the human proteome is therapeutically addressed by drugs; iii) given current understanding of disease (~8,800 disease
concepts), as well as all diseases addressed via on-label (~2,000) and off-label (~400) indications, we currently address at most
a quarter of all diseases via therapeutic agents.
IDG KMC teams: • Translational Informatics Division, University of New Mexico School of Medicine (Albuquerque,
NM); • Ma’ayan Laboratory, Icahn School of Medicine at Mount Sinai (New York, NY); • ChEMBL team, EMBL European
Bioinformatics Institute (Hinxton, UK); • Jensen Laboratory, Novo Nordisk Foundation Center for Protein Research
(Copenhagen, Denmark); • Schurer Laboratory, University of Miami Miller School of Medicine (Miami, FL); • Division of PreClinical Innovation, NIH National Center for Advancing Translational Sciences (Rockville, MD). Websites: pharos.nih.gov
(Pharos) and newdrugtargets.org (TIN-X)
PT 13
Assimilation of Mammalian Cell Biology knowledge into Model Based Drug Development in
Pharmaceutical Industry
Kierzek, A. M.1,2
1
2
Simcyp, a Certara Company, Head of Systems Modelling, Sheffield, Great Britain
University of Surrey, Visiting Professor of Systems Biology, Guildford, Great Britain
The Physiologically Based Pharmacokinetic Modelling of virtual populations (PopPBPK) is a bottom up, mechanistic
modelling methodology, which has already made tremendous impact on the Model Based Drug Development in
Pharmaceutical Industry. In a number of cases the U. S. Food and Drug Administration (FDA) has accepted PopPBPK
simulation as a sole evidence for drug label, thus decreasing a number of clinical trials required. Building on this
unprecedented success of mechanistic modelling approach, Simcyp is now developing Quantitative Systems Pharmacology,
where the scope of mechanistic modelling will be extended towards molecular processes in the mammalian cell. The approach
will be illustrated by case studies, where kinetic models of Nerve Growth Factor pathway and Fatty Acid Amide Hydrolase
inhibitors were extended to incorporate physiological and pharmacological parameters. The possible future directions of
incorporating individual genome and ~omics data into genome scale mechanistic models of mammalian cells, as well as
further integration of these models with PopPBPK will be discussed.
33
Program /
Abstracts
PT 14
Systems Genetic Approaches to Coronary Artery Disease - Toward Diagnostics and
Therapies of Molecularly Defined Subcategories of Patients
Björkegren, J.1
1
Mount Sinai Hospital, New York, United States
Cardiometabolic diseases place a heavy burden on society, and new diagnostics, therapies, and strategies for early prevention
are needed. Genome-wide association studies (GWAS) have identified hundreds of new genetic risk loci, thereby improving
our understanding of the complex inheritance of cardiometabolic disease. However, their contribution to expected genetic
variance is typically low, and most of the downstream gene-regulatory mechanisms remain unknown. We genotyped
and RNA-sequenced seven disease-relevant tissues from 600 coronary artery disease patients in the Stockholm-Tartu
Atherosclerosis-Related Network Engineering Task (STARNET) study. The STARNET datasets were highly informative
in unfolding mechanisms for risk loci identified by GWAS. We unexpectedly found that the principal site for the genetic
regulation of the LDL-cholesterol and CAD risk gene PCSK9 appears to be abdominal fat, not liver. In addition using strict
statistical thresholds, we found evidence of extensive sharing of cis and trans genes across tissues and diseases by revealing
casual disease networks of multiple loci. A better understanding of downstream gene-regulatory mechanisms of risk loci
for cardiometabolic diseases provided by STARNET is essential to translate the initial GWAS findings into opportunities for
diagnosis, therapy and prevention.
PT 15
Dissecting analogue versus digital regulation in Polycomb-based epigenetics
Howard, M.1
1
John Innes Centre, Dept of Computational and Systems Biology, Norwich, Great Britain
Polycomb repressive complex 2 (PRC2) is involved in transcriptional repression of thousands of genes in higher eukaryotes.
Expression of a PRC2 target gene is not only controlled digitally in cis by its chromatin state, but also in an analog (smoothlyvarying) manner by gene-specific trans-regulators. Here, we introduce a mathematical model linking cis- and transregulation, where transcription directly antagonizes Polycomb silencing. Our model reveals that locally acting feedbacks
stabilizing the PRC2-repressed state allow chromatin to buffer noise in trans-regulators. Buffering is particularly robust
when chromatin dynamics are slow, suggesting a biological function for observed slow histone methylation by PRC2. When
trans inputs to gene expression are balanced, bistable chromatin states instruct their own inheritance, with analog transregulators fine-tuning expression of the two states. However, strong transcriptional activation or repression abolishes
bistability, with chromatin becoming purely responsive to trans-factors. Our model therefore clarifies causality relationships
between transcription and chromatin in epigenetic memory.
34
ORAL PRESENTATIONS
OP 01
A quantitative model for macrophage activation predicts tissue thresholds for the
propagation of inflammation
Bagnall, J.1, Boddington, C.1, Downton, P.1, Boyd, J.1, Brignall, R.1, Rowe, W.1, Schmidt, L.1, Spiller, D.1, White M.1, Paszek, P.1
University of Manchester, Manchester, Great Britain
1
Toll-like receptor (TLR) signaling regulates macrophage cell activation and effector cytokine amplification in response to
pathogen stimulation. Here, using quantitative single cell approaches and mathematical modeling we propose that TLR4
signaling involves a “tissue level feedback”, which regulates propagation of the effector cytokine response by competitive
uptake in a local tissue environment. The project was undertaken using a combination of fluorescence correlation
spectroscopy calibrated microscopy, single-molecule FISH and flexible recombination lentiviral platform. We found that in
macrophage populations, the extent of TLR4 stimulation defines a dose-dependent expression of Nuclear Factor -kappaB
(NF- B) target genes. However, only a subset of macrophages was able to produce a strong Tumor Necrosis Factor a (TNFα)
response, which resulted in cellular heterogeneity. Based on the measured high TNFα uptake seen in tissue cells (in
comparison with macrophages), mathematical modeling suggests that the local propagation of the TLR4 cytokine effector
response is limited to small distances of a few cell diameters. In our predictive model, the heterogeneity of TNFα production
controlled the requisite interaction distance and the probability of signal propagation between tissue-resident macrophage
cells. We propose that “tissue-level feedback” may allow robust amplification of localized inflammatory cues, while avoiding
out-of-context propagation of inflammation at long range.
OP 02
Stem cells dynamics and its regulation during spinal cord regeneration
Rost, F.1, Rodrigo Albors, A. 2,3, Mazurov, V. 2, Deutsch, A.1, Brusch, L.1, Tanaka, E. M. 2, Chara, O.1,4
Technische Universität Dresden, Center for High Performance Computing, Dresden, Argentina
Technische Universität Dresden, Center for Regenerative Therapies, Dresden, Germany
3
University of Dundee, Cell and Developmental Biology, Dundee, Great Britain
4
National University of La Plata, Instituto de Física de Líquidos y Sistemas Biológicos, La Plata, Argentina
1
2
Question: Regeneration, the ability to recreate lost tissues is a long studied question in biology. Strikingly, mammals have
generally poor regenerative abilities. On the contrary, the Mexican salamander or axolotl is uniquely able to mobilize neural
stem cells to completely regenerate many lost neural tissues including the spinal cord. An outstanding biological question
is why this animal can and why so many other species, including mammals, can’t. To answer this question we first need to
understand what the cellular mechanisms controlling axolotl spinal cord regeneration are.
After tail amputation the spinal cord regrows about 2 mm within 1 week. What are the cell scale mechanisms that drive this
tissue outgrowth?
Methods & Results: Morphogenetic processes like cell proliferation, and (de-)differentiation, cellular rearrangements are
obvious candidates. We aimed to quantify them by tightly linking quantitative data analysis and mathematical modelling.
We quantified the density of neural stem cells and mitotic events along the anteroposterior axis in the axolotl spinal cord
during the first week of regeneration. We analyzed these data with an empirical mathematical model of two spatial zones
of cell proliferation along the anteroposterior axis using Bayesian inference. This analysis indicated a region close to the
amputation plane that shows an increase in cell proliferation. To estimate the proliferation rate dynamics, we quantified and
modelled the incorporation of a thymidine analog (BrdU) in the regenerating spinal cord. Furthermore, we tracked individual
cell clones to estimate the rate of cell displacements and hence the flux of cells into the regenerating zone.
Finally, we set up a mechanistic mathematical model of spinal cord outgrowth during regeneration that incorporates
proliferation, differentiation and cell displacements. By comparing this model to spinal cord outgrowth data we can show
that the proliferation in a zone close to the amputation plane is the key to explain the observed outgrowth.
35
Program /
Abstracts
OP 03
Tuning out cell cycle entry control - cell fate decisions under oncogenic MYCN
Kuchen, E.1, Ryl, T.1, Bell, E. 2, Shao, C.1, Florez, A.1, Westermann, F. 2, Höfer, T.1
1
2
DKFZ, Theoretical Systems Biology, Heidelberg, Germany
DKFZ, Neuroblastoma Genomics, Heidelberg, Germany
While most tumours respond to initial therapy, tumour relapse remains a major medical challenge. Fundamentally,
tumour regression or regrowth are determined by the individual decisions of cancer cells under treatment, including cell
death, senescence and continued proliferation of survivors. Eliminating viable survivors relies on a mechanistic molecular
understanding of the processes that determine the fate of individual cells and their deregulation in cancer cells. Key
regulators of all these cellular decisions are the members of the MYC family of transcription factors. MYC is frequently
overexpressed in a multitude of human cancers and is one of the most prominent drivers of tumour relapse. MYC’s
promiscuous binding abilities make it difficult to understand its role in cellular decision-making. Using cells of the paediatric
cancer neuroblastoma harbouring a MYCN amplification, we investigated whether the bistable switch, underlying both
the cell cycle G1-S transition and DNA-damage arrest, is still present under oncogenic MYCN. Transcriptomics of cell cycle
synchronised populations with tuneable MYCN expression revealed that specific MYCN-induced changes in gene expression
shift the balance between positive and negative regulators of this bistable switch. Using a data-driven mathematical model,
we demonstrate that, through this shift, the switch is tuned out under high MYCN expression and unperturbed growth,
thus pushing cellular decisions towards proliferation. This detuning homogenises cell cycle progression and increases the
rate of passage through the G1-phase, as validated by single-cell time-lapse microscopy and flow cytometry experiments.
However, even in these aggressive cancer cells, the bistable switch is not abolished and can be restored by pharmacological
perturbations. Corroborated by experiments, the model shows that cells remain responsive to chemotherapy-induced cell
cycle arrest, coupling DNA-damage and cell cycle checkpoints. Upon chemotherapy, MYCN delays cell cycle arrest and
thus shifts treatment effects from arrest to apoptosis. This effect is accentuated after chemotherapy, where high MYCN
levels suppress cellular senescence and drive surviving cells back into the cell cycle. Our quantitative analyses provide a
mechanistic understanding of how MYCN shifts single-cell decisions from quiescence to proliferation under unperturbed
growth and suggest that long-term arrest after chemotherapy is maintained via the same mechanism.
OP 04
In silico synchronization and modulation of nuclear ruptures in laminopathy model cells
Robijns, J.1, Molenberghs, F.1, Corne, T.1,2, Sieprath, T.1,2, Verschuuren, M.1, De Vos, W.1,2
University of Antwerp, Antwerp, Belgium
University of Ghent, Molecular Biotechnology, Ghent, Belgium
1
2
The nuclear lamina is a critical regulator of nuclear structure and function. We have shown that nuclei from laminopathy
patient cells experience repetitive disruptions of the nuclear envelope, causing transient intermingling of nuclear and
cytoplasmic components (De Vos et al., Human Molecular Genetics, 2011). Ruptures occur at weak spots of the nucleus,
pointing to mechanical defects, while the uncontrolled translocation of transcription factors during those events alter
gene expression programs. Since ruptures have recently also been described in aging and cancer cells - both associated
with abnormal expression of lamins or their precursors - it most likely represents a pathophysiological mechanism with
generic relevance. However, the exact causes and consequences of rupture events are not fully understood, and their
stochastic occurrence complicates in-depth analyses. To resolve this, we have established a method that enables quantitative
investigation of nuclear ruptures, based on minimally invasive imaging of two fluorescent reporters, automated cell tracking
and in silico synchronization of individual rupture events. Using this approach, we systematically quantified nuclear rupture
kinetics in laminopathy model cell lines. We found that rupture frequency correlates inversely with lamin A/C levels in model
cells and we discovered novel regulators of rupture kinetics.
OP 05
SINGLE MOLECULE APPROACHES FOR STUDYING LIVER HETEREOGENEITY
Itzkovitz S.1
Weizmann Institute of Science, Rehovot, Israel
1
The liver is a highly heterogeneous organ composed of polyplooid hepatocytes operating in structured lobules, which are
polarized by blood flow. The polarized blood flow along the lobule axis creates gradients of oxygen, nutrients and hormones.
As a result different lobule layers sub-specialize in distinct processes, a phenomenon termed ‘metabolic zonation’. To study
liver heterogeneity in the intact mammalian liver we have implemented single molecule Fluorescense in-situ Hybridization
(smFISH), a technique that reveals individual mRNA molecules as diffraction limited dots under a fluorescence microscope.
This technique facilitates quantification of the zonation profiles of any liver gene of interest. We also developed techniques,
36
based on dual color labeling of introns and exons, to extract both transcription and degradation rates of genes in the
intact mouse liver. Using these methods we found that gluconeogenic genes such as Pck1 and G6pc have extremely high
transcription and mRNA degradation rates, facilitating rapid shut-down upon refeeding. Many liver genes are expressed in
a bursty manner, with promoters stochastically transitioning between active transcription and quiescence. Strikingly the
liver seems to have developed features to minimize cell-to-cell variability generated by such bursts, including coordination
of burst frequency and mRNA lifetime, liver polyplopidy and nuclear retention of mRNA for highly bursty genes. Our results
reveal new layers of liver heterogeneity.
OP 06
Inferring Tumour Evolution from Single-Cell Sequencing Data
Ross, E.1, Markowetz, F.1
University of Cambridge, Cancer Research UK Cambridge Institute, Cambridge, Great Britain
1
Question: Tumour evolution leads to genetic intra-tumour heterogeneity, which poses major challenges to cancer therapy.
While tumour heterogeneity has been documented in several cases, many details of the underlying evolutionary processes
are still unknown.
Studying pathways of tumour evolution promises to provide insights into these processes and the early stages of cancer
development and could enable predictions about whether or not early-stage tumours are likely to progress to more aggressive
forms. Inferring the evolutionary history of a tumour, however, remains challenging.
So far, most methods for inferring tumour phylogenies use bulk sequencing data, but these methods struggle to deconvolute
the mixed signal into separate clones and their corresponding genotypes. Recent advances in single-cell sequencing
technologies promise to reveal tumour heterogeneity at a much higher resolution. However, single-cell sequencing data come
with their own challenges and new methods are needed that take into account the noise that is inherent to this type of data.
Results: Here, we present oncoNEM (oncological Nested Effects Model), a probabilistic method for inferring intra-tumour
evolutionary lineage trees from noisy exome- or genome-wide single-cell sequencing data. OncoNEM is based on the
nested structure of mutations observed between cells and jointly infers the tree structure, the number of clones and their
composition.
We evaluate the accuracy of oncoNEM in the controlled setting of simulation studies and demonstrate that (i) our method
can accurately infer trees of tumour evolution despite the high allelic dropout rates of current single-cell sequencing
technologies, (ii) it can estimate error rates directly from the data and is robust to inaccuracies in these model parameters
and (iii) it substantially outperforms competing methods. Finally, we show its applicability in case studies of muscle-invasive
bladder cancer and essential thrombocythemia.
Conclusions: OncoNEM is an accurate probabilistic method for inferring intra-tumour phylogenies from single-cell
sequencing data. It identifies subpopulations within a sample of single-cells and estimates their evolutionary relationships.
In simulations, oncoNEM performs well for false-positive and false-negative rates of current single-cell data sets, it is robust
to inaccuracies in the estimation of model parameters and outperforms competing methods.
37
Program /
Abstracts
OP 07
Comparative analysis of single-cell RNA-sequencing methods
Enard, W.1, Ziegenhain, C.1, Parekh, S.1, Vieth, B.1, Smets, M.1, Leonhardt, H.1, Hellmann, I.1
1
LMU Munich, Biology II, Planegg, Germany
Single-cell mRNA sequencing (scRNA-seq) allows to profile heterogeneous cell populations, offering exciting possibilities to
tackle a variety of biological and medical questions. A range of methods has been recently developed, making it necessary to
systematically compare their sensitivity, accuracy, precision and cost-efficiency.
Here, we have generated and analyzed scRNA-seq data from 479 mouse ES cells and spike-in controls that were prepared
with four different methods in two independent replicates each. We compare their sensitivity by the number of detected
genes and by the efficiency with which they capture spiked-in mRNAs, their accuracy by correlating spiked-in mRNA
concentrations with estimated expression levels, their precision by power simulations and variance decomposition and their
efficiency by their costs to reach a given amount of power. While accuracy is similar for all methods, we find that Smart-seq
on a microfluidic platform is the most sensitive method, CEL-seq is the most precise method and SCRB-seq and Drop-seq are
the most efficient methods. Our analysis provides a solid basis to choose among four available scRNA-seq methods and to
benchmark future method development.
OP 08
Perturbation of dynamics of NF- B and the regulation of gene expression
Downton, P.1, Ashall, L.1, Rowe, W.1, Adamson, A.1, Boddington, C.1, Harper, C.1, Boyd, J.1, Daniels, D.1, Lam, C.1, Ryan, S.1, Spiller, D.1, Paszek, P.1, White, M.1
University of Manchester, Faculty of Life Sciences, Manchester, Great Britain
1
The nuclear factor kappa B (NF- B) signalling system is critically important with respect to inflammation, immune responses
and cell survival. NF- B regulates the expression of hundreds of genes and is activated by a large range of biological factors
and environmental conditions. Uncontrolled regulation of the NF- B pathway can lead directly to a number of disorders,
including chronic inflammation and various cancers.
Imaging of single cells reveals that induction of NF- B signaling by tumour necrosis factor alpha (TNFalpha) results in
oscillations of NF- B between the cytoplasm and nucleus [Nelson et al., (2004) Science 306:704], where NF- B can bind
promoters to induce target gene transcription. Continuous treatment with TNFalpha results in persistent nuclear oscillations
that lose synchronicity over time, with an average period of 100 minutes between translocations. When cells are treated
with pulses of TNFalpha at 100 minute intervals, the majority of cells show a relatively synchronous nuclear translocation in
response to each treatment [Ashall et al., (2009) Science 324:242]. We have modelled this oscillatory process mathematically,
and characterized the importance of the molecular feedback mechanisms in heterogeneity in this system [Paszek et al.,
(2010) Proc. Natl. Acad. Sci. 107:11644].
To determine the functional relevance of these system properties, we conducted a large scale microarray analysis of gene
expression in response to continuous and pulsatile TNFalpha treatment regimens. We identify hundreds of induced
target genes, and find that different subsets of NF- B-dependent genes respond to continuous and pulsatile stimulation
protocols. We have also perturbed both NF- B oscillation dynamics and target gene expression using combinations of
physiological stimuli. Target gene differences are visible at a transcript and protein level, suggesting oscillation frequency
and synchronicity have functional rolesin the regulation of downstream gene expression. Single molecule fluorescence in situ
hybridisation (smFISH) has been used to characterise the heterogeneity of target gene expression at a single cell level, and
these data are being used to inform and refine our mathematical models of NF- B-dependent transcription.
38
OP 09
Analysing the interplay of NF- B precursors using a mathematical modelling approach
Kofahl, B.1, Yilmaz, Z. B.1, Beaudette, P.1, Baum, K.1, Ipenberg, I.1, Dittmar, G.1, Scheidereit, C. 1, Wolf, J.1
1
Max Delbrueck Center for Molecular Medicine, Berlin, Germany
NF- B signalling is involved in inflammatory responses, innate and adaptive immunity, cell proliferation and cell death. Its
aberrant regulation is linked to inflammatory and cardiovascular diseases as well as to cancer.
Generally, the NF- B signalling network is divided into the canonical and the non-canonical branch. They are activated by
different stimuli and act on different time scales. The non-canonical branch relies on the processing of the precursor protein
p100 to its product p52. Via the canonical branch, the amount of p50 (generated by the precursor p105) that can enter the
nucleus is regulated. In recent years, it has been shown that both branches are interconnected, e.g. via target gene expression.
Our combined experimental and theoretical study reveals a further linkage between the two branches by interplay of the two
precursors.
Our experimental data showed that the dynamics of p100 and p105 proteolysis are similar upon pathway activation by a noncanonical stimulus and that the precursors can form a hetero-complex. Furthermore, the data showed that their dynamics
are interdependent - the presence of one precursor influences the temporal behaviour of the respective other one. Based
on the experimental findings, different mathematical models are developed and used to dissect the mechanism underlying
the concerted response of p100 and p105. Comparing the fit quality of the models reveals that the p100/p105 complex is
stimulus-responsive.
Quantitative dissection and modeling of the NF- B p100-p105 module reveals interdependent precursor proteolysis.
Yilmaz, ZB; Kofahl, B; Beaudette, P; Baum, K; Ipenberg, I; Weih, F; Wolf, J; Dittmar, G; Scheidereit, C
Cell Rep. 2014, 9(5): 1756-1769
OP 10
Identification of an intracellular negative and an autocrine positive feedback coordinating
the interferon-induced antiviral response
Schilling, M.1, Robichon, K.1, Maiwald, T. 2, Schneider, A.1, Willemsen, J. 3, Kreutz, C. 2, Ehlting, C.4, Chakraborty, S.1, Huang, X.1, Böhm, M. E.1, Damm, G. 5,
Seehofer, D. 5, Bode, J. G.4, Binder, M. 3, Bartenschlager, R.6, Timmer, J. 2, Klingmüller, U.1
German Cancer Research Center (DKFZ), Division Systems Biology of Signal Transduction, Heidelberg, Germany
University of Freiburg, Institute for Physics, Freiburg, Germany
3
German Cancer Research Center (DKFZ), , Division Virus-Associated Carcinogenesis, Heidelberg, Germany
4
University of Düsseldorf, University Hospital, Gastroenterology, Hepatology and Infect. Diseases, Düsseldorf, Germany
5
Charité-University Medicine, General, Visceral, and Transplantation Surgery, Berlin, Germany
6
University of Heidelberg, Department of Infectious Diseases, Heidelberg, Germany
1
2
Interferon alpha (IFNα)-induced activation of an antiviral response is the first line of defense against viral infection.
Initiation of an antiviral response comprises the induction of a multitude of antiviral genes that remarkably vary in
their extent and dynamics of expression. By combining time-resolved quantitative data and mathematical modeling, we
established mRNA stability and a negative as well as a positive feedback loop as key mechanisms controlling the expression
dynamics of the antiviral genes. We show that distinct antiviral gene expression profiles are induced by IFNα stimulation.
Iterating between experiment and modeling, we analyzed the pathway structure using a dynamic model of IFNα-induced
signaling and gene expression. Model prediction and experimental validation revealed that distinct mRNA stability affects
expression profiles of antiviral genes and that the intracellular feedback mediated by an interferon-induced gene negatively
regulates expression of early antiviral genes. Furthermore, model analyses predicted an autocrine feedback, which cooperates
with IFNα to enhance antiviral gene expression. We experimentally identified the key mediator of this positive feedback loop
by mass spectrometry and by employing knock-out mice. Furthermore, we developed model-based strategies to potentiate
the IFNα-induced antiviral responses. This effect could be therapeutically exploited to promote IFNα-mediated viral clearance
and thereby antagonize viral persistence.
39
Program /
Abstracts
OP 11
Detecting emergent properties in genomic data: Consolidating inflammatory response
dynamics
Shoemaker, J.1,2, Satoshi, F. 2, Eisfeld, A. 3, Zhao, D. 2, Kawakami, E. 2, Sakabe, S. 2, Maemura, T. 2, Goria, T. 2, Katsura, H. 2, Muramoto, Y. 2, Watanabe, S. 2,
Watanabe, T. 2, Fuji, K. 2, Matsuoka, Y. 2, Kitano, H. 2, Kawaoka,Y. 2,3
University of Pittsburgh, Chemical & Petroleum Engineering, Pittsburgh, United States
ERATOInfection-induced Host Responses Project, Tokyo, Japan
3
University of Wisconsin, Madison, Japan
1
2
Main Objectives: Understanding the emergent behaviors that result from molecular interactions is essential to detecting
divergent outcomes. This is particularly true in biology when a common mechanism may drive distinct dynamic results.
Bottom-up modeling approaches that rely on mathematical models can help define the feasible dynamic landscape of the
biological system but such approaches are difficult in genomic studies when system size can overwhelm applicability. Here,
we describe an approach to leverage bioinformatics and clustering to screen for select emergent behaviors in whole genome
expression data. The approach is applied to gene expression data from the lungs of mice infected with influenza viruses
to evaluate whether different virus strains invoke unique inflammatory gene responses or if emergent properties, e.g.
ultrasensitive responses, can consolidate apparent, virus-specific responses.
Materials and Methods: Animal models: Mice were infected with a seasonal H1N1, an H1N1 virus from the 2009 pandemic
or a deadly H5N1 virus. At several times post infection, three animals per group were sacrificed and the lungs sectioned for
microarray analysis and virus titer assays.
Module detection and modeling: Dynamic gene expression was clustered using WGCNA and screened for association with
inflammation using a variety of bioinformatics techniques. Inflammation-associated gene modules were fit to equations
relating gene response to virus titer.
Model validation: To evaluate the fitted models, the animal study was repeated but the inoculation levels altered. The model
prediction was evaluated by goodness of fit.
Results: We found that a single module of clustered genes was highly enriched for inflammatory signaling. The module’s
scaled mean gene expression appeared to have an ultrasensitive-like relationship with virus growth, i.e. minimal gene
response is observed at low virus concentration but gene expression increases rapidly once a threshold virus titer was
reached. Fitting the gene expression to a simplified model of ultrasensitivity, we showed that not only could the model
consolidate the inflammatory gene expression dynamics that appeared distinct to each virus, but we could also predict
inflammatory gene expression in newly infected populations.
Conclusions: The findings of this work strongly suggest that ultrasensitive responses coordinate the inflammatory response
and are responsible for major inflammatory events during virus infections. Furthermore, this study demonstrates the
importance of integrating signaling framework in to gene expression analysis.
OP 12
SteatoNet as a predictive and gender-based liver metabolic model
Cvitanović, T.1, Moškon, M. 2, Mraz, M. 2, Urlep, Z.1, Rozman, D.1
1
2
Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Center for Functional Genomics and Bio-chips, Ljubljana, Slovenia
Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
Several large-scale computational models of liver metabolism address the liver dynamics from systems biology/medicine
perspective. Herein we describe applications of the state-of-the-art model SteatoNet designed by the object-oriented
approach (Naik et al., PLOS Comput. Biol., 2014). SteatoNet accounts for interactions between the liver and peripheral tissues
and includes metabolic as well as gene regulatory and signal transduction pathways describing the dynamics of non-alcoholic
fatty liver disease. SteatoNet requires only a minimal set of parameters and can be used even in a case of sparse experimental
data. Furthermore, due to its object-oriented nature, it can be easily adapted to investigate different liver-associated
pathologies. This makes the model an excellent starting point for testing biological hypotheses prior to experimentation.
We applied SteatoNet to address the question of metabolic consequences in adipose tissue after knocking out gene Cyp51 from
cholesterol synthesis in the liver. The experimental data for the Cyp51 liver knockout are available (Lorbek et al., Sci. Rep.
2015). The model simulations demonstrate the network disturbances in adipose tissue, which is an excellent starting point
for further experimental testing on gene expression and protein levels.
Another important application is the gender-based model adaptation. Liver has been known for decades as a sexually
dimorphic organ especially at the gene expression level. Gender-based differences were discovered also in the experimental
Cyp51 liver knockout responses. We extended SteatoNet to differentiate between genders based on literature data and expert
based knowledge. As far as we can tell this represents the first gender-based liver metabolic model. Current applications
include simulations of sex hormone ratios in blood and their networking with gender-based differences in cholesterol
synthesis and regulatory nodes. The future SteatoNet adaptation will be guided towards personalization, aimed at predicting
the network effects of liver disease -related polymorphisms in individuals.
40
OP 13
Trans-omic reconstruction of insulin signal flow in global phosphorylation and metabolic
network
Yugi, K.1, Kubot, H. 2, Kuroda, S.1
University of Tokyo, Department of Biological Sciences, Tokyo, Japan
Kyushu University, Medical Institute of Bioregulation, Fukuoka, Japan
1
2
Cellular functions are realized by a global network of molecular interactions across multiple ‘omic’ layers such as genome,
transcriptome, proteome, and metabolome. We designate this global network as a ‘trans-omic’ network. Conventional
molecular biological studies and comprehensive measurement of each omic layer have collectively provided clues to elucidate
the landscape of the trans-omic network. However, conventional molecular biology has limited comprehensiveness, and indepth measurement of one particular omic layer does not reveal interaction across multiple omic layers. Consequently, the
landscape of the trans-omic network remains unknown. Here we developed an unbiased method to reconstruct trans-omic
networks based on time-series phosphoproteome and metabolome data together with public databases. This method was
applied to reconstruct a trans-omic network underlying acute insulin action in rat hepatoma FAO cells. We found that the
insulin signal flowed through the trans-omic network involving 26 protein kinases, 76 phosphorylated metabolic enzymes,
and 80 allosteric effectors, resulting in quantitative changes in 97 metabolites. Kinetic modeling analysis predicted selective
control of a subnetwork around phosphofructokinase by specific phosphorylation and allosteric regulation. Thus, we provide
an unbiased method that reconstructs the trans-omic network from phosphoproteome and metabolome data, which will be
applicable to other cellular responses.
OP 14
Dynamic glycosylation flux analysis
Hutter, S.1, Villiger, T. K.1, Brühlmann, D. 2, Stettler, M. 2, Broly, H. 2, Morbidelli, M.1, Gunawan, R.1
1
2
ETH Zürich, Chemie und Angewandte Biowissenschaften, Zürich, Indonesia
Merck Serono S.A., Biotech Process Sciences, Corsier-sur-Vevey, Switzerland
Objectives: N-linked glycosylation is essential in determining the biological activity and efficacy of many therapeutic
proteins [1]. Hence, there is a great interest for modulating and controlling protein glycosylation to optimize and regulate
product quality. Much of the previous developments have been driven by the optimization of process parameters and media
composition, and by genetic engineering [2]. Engineering approaches that rely on mathematical modeling of glycosylation
networks however have not been adopted widely for this purpose. In this work, we present a method for dynamic flux
analysis of glycosylation networks from time series measurements of glycan structures, aptly called dynamic glycosylation
flux analysis (DGFA) and apply it to a CHO cell culture production of IgG monoclonal antibody.
Materials & Methods: In developing DGFA, we adapted a previous method called dynamic metabolic flux analysis (DMFA)
[3]. DGFA, like DMFA, relies on a stoichiometric model of the network which can be written as:
dX/dt = Nv
Sv = 0
X denotes the external concentrations of glycosylated protein products, v the vector of glycosylation fluxes, and N and S
respectively denote the external and internal stoichiometric matrices. DGFA generates estimates for the fluxes v at an
optimally determined set of switching time points, by minimizing the sum of squares of the errors (SSE) between the
measured concentrations and model predictions of X(t). We employed a piecewise linear approximation for v(t) between the
switching times. In typical glycosylation networks, the stoichiometric matrix S is underdetermined due to branching and
converging pathways. To resolve the ambiguity in flux predictions caused by such pathways, we employed an equal flux ratio
between fluxes that produce the same glycosylation structure. In addition, we also perform flux uncertainty analysis by
assigning a random ratio to the fluxes above.
Glycosylated protein samples in a CHO-S fed-batch cell culture for the production of IgG monoclonal antibody were analyzed
by Ultra Performance Liquid Chromatography (UPLC) over a period of 14 days together with measurements of antibody
41
Program /
Abstracts
concentrations and cell count measurements.
Results & Conclusion: DGFA could match the measurement data with excellent accuracy. Statistical analysis resulted in
two optimal switching time points on day 7 and 9. A comparative analysis revealed that glycosylation reactions catalyzed by
the same enzyme evolved similarly over time and uncertainty analysis showed that DGFA was robust with respect to the flux
ratios used in the analysis. Hence, the glycosylation flux predictions from DGFA could reveal the time-evolution of enzyme
activities, an important insight that could lead to a better understanding of the regulation of the glycosylation process.
[1] Aebi, M., 2013. Biochim. Biophys. Acta.
[2] Hang, I., 2015. Glycobiology.
[3] Leighty, R.W., 2011. Metab. Eng.
OP 15
Clonal competition in the stem cell niche: New insights from 3D computation
Thalheim, T.1, Buske, P.1, Rother, K.1, Przybilla, J.1, Loeffler, M.1, Galle, J.1
1
Leipzig University, IZBI, Leipzig, Germany
Competition between stem cell (SC) clones represents a mechanism that potentially can eradicate mutant cells and their
progeny from a tissue. In the intestine this competition leads to ongoing monoclonal conversion in all individual crypts
of the tissue. General features of this process have been studied experimentally. However, the impact of the spatial
organization of the intestinal crypts on the fixation of mutations remains largely unknown. The introduced computational
model demonstrates that three-dimensional (3D) crypt simulations are capable of improving the understanding of these
interdependencies.
We exemplify the potential of our model by simulation of a three-dimensional in silico model of mouse small intestinal
crypts. Thereby, we focus on the intestinal SC niche being composed of undifferentiated functional SCs and Paneth cells
(PC). In simulation series we demonstrate that both cell distribution and niche composition have strong impact on the clonal
competition. We show that changes of the PC distribution can nearly abolish monoclonal conversion in the crypt.
In addition we analyze the impact of Notch-signaling on clonal competition in the crypt. Intrinsic activation of Notchsignaling, i.e. autonomous Notch, in SCs suppresses specification of them into the PC lineage and eventually leads to PC
depletion. Accordingly, these SCs can robustly maintain their state and thus gain a competitive advantage compared to cells
that rely on wild type intercellular Notch activation. We find that under autonomous Notch monoclonal conversion in the
crypts is accelerated. Moreover, quantifying the frequencies that Wnt-activating mutations become fixed in intestinal crypts,
we observe that these frequencies are strongly increased in case of an autonomous Notch background compared to a wild
type background. As a consequence early states of tumor development as metaplasia and adenoma can occur more frequently.
Thus, we suggest that Notch plays a key role in clonal competition in the intestine.
OP 16
Heterogeneity and cell fate control in mouse embryonic stem cells
Herberg, M.1, Zerjatke, T.1, de Back, W.1, Kalkan, T. 2, Smith, A. 2, Roeder, I. 2, Glauche, I.1
TU Dresden, Institute for Medical Informatics and Biometry, Dresden, Germany
Cambridge Stem Cell Institute, Cambridge, UK, Germany
1
2
Bistability is a characteristic feature of many molecular switches facilitating cell decision processes as they occur during
differentiation and in response to external stimuli. Undifferentiated mouse embryonic stem cells (ESC) are a typical example,
in which such a bistable situation is caught in an in vitro setting, thus contributing to a heterogeneous albeit dynamically
stabilized cell culture. In particular, it is the expression of pluripotency factors like Nanog and Rex1 that obeys a bimodal
distribution, which is reestablished after cell sorting.
We studied heterogeneity and cell fate control in ESC using a variety of methods including cell culture experiments,
flow cytometry, live-cell imaging, and quantitative image analysis. Complementary we developed a multiscale, spatial
mathematical model of ESC growth, which allows comparing experimental results with our model predictions. The
integrated, multiscale model serves as a framework to link population features, such as proliferation rates and spatial
arrangements. Moreover, it reveals potential transcription factor related cellular and intercellular mechanisms behind the
emergence of observed patterns that cannot be derived from experimental data directly.
42
OP 17
Multiscale Model of Liver Intoxication after APAP overdose.
Boissier, N.1, Celliere, G.1, Ghallab, A. 2, Vignon-Clementel, I.1, Hengstler, J. 2, Drasdo, D.1
1
2
Sorbonne Universités, INRIA, UPMC Univ Paris 06, Lab. J.L. Lions, PARIS, France
IfADo, Systemtoxikologie, DORTMUND, Germany
Question: Paracetamol (acetaminophen, APAP) damage is the main cause of acute liver failure in many countries, including
UK and the United States. Hepatocytes detoxify the blood from the drug. Above a certain threshold concentration, cells
accumulate too high concentrations of NAPQI, a product of intracellular detoxification,and die. With increasing drug doses,
an increasing necrotic lesion is generated. Liver zonation leads to different affinities and sensitivities of hepatocytesto APAP,
depending on their spatial position along the portal-central lobule axis. The cells closest to the central vein of each lobule, the
anatomical unit of liver, are the most sensitive to APAP and therefore the first to die. This impairs the ability of the liver to
detoxify blood from ammonia with the threat of encephalopathy, and eventually death. We here address the question of how
adverse effects of paracetamol can be predicted in a multiscale model that is calibrated with in vitro dose-response data using
paracetamol as an example.
Methods: We consider a spatial-temporal model of APAP intoxication in a liver lobule generated from the image analysis
of mice liver using the software TiQuant[1], [2]. Blood flow is modeled as a resistive network taking into account the effect
of red blood cells by using Pries’ law [3] for viscosity. Transport of APAP, modeled by a 1D transport PDE in the blood, is
linked with its metabolism in the cells. The multi-scale model integrating the APAP detoxification pathway in each individual
hepatocyte takes into account the spatial inhomogeneities within the liver and the impact of the temporal administration.
The drug metabolic pathway of APAP has been calibrated by direct comparison with in vitro data.
Results and Conclusions: Our approach allows us to couple straight forwardly the cells’ positions and the concentration of
APAP that those cells locally experience taking into account cell-cell variability. Animal experiments are being done in order
to validate simulation results for various doses. This work, besides modeling the acute liver intoxication after an overdose
of paracetamol shows how in vitro experimental results and an in silico model of the liver can be combined to simulate the
effect of a drug in vivo.
[1] Hammad S., Hoehme S., Friebel A., et. al. “Protocols for staining of bile canalicular and sinusoidal networks of human,
mouse and pig livers, three-dimensional reconstruction and quantification of tissue microarchitecture by image processing
and analysis.” Archives of Toxicology 88 (5) 1161-1183 (2014)
[2 ] Hoehme S., Brulport M., et al. “Prediction and validation of cell alignment along microvessels as order principle to restore
tissue architecture in liver regeneration.” PNAS 107.23 10371-10376 (2010)
[3] Pries A. R., Secomb T. W., Gaehtgens P., “Biophysical aspects of blood flow in the microvasculature.” Cardiovascular
research. , 32(4), 654-667 (1996)
OP 18
Think Adjoint - Methods facilitating parameter estimation for genome-scale mechanistic
dynamic models
Fröhlich, F.1,2, Shadrin, A. 3, Kessler, T. 3,4, Wierling, C. 3,4, Lange, B. 3,4, Theis, F.1,2, Lehrach, H.4,5, Hasenauer, J.1,2
Helmholtz Zentrum München, Institute of Computational Biology, Neuherberg, Germany
Technische Universität München, Chair of Mathematical Modeling of Biological Systems, München, Germany
3
Alacris Theranostics, Berlin, Germany
4
Max Planck Institute for Molecular Genomics, Berlin, Germany
5
Dahlem Centre for Genome Research and Medical Systems Biology, Berlin, Germany
1
2
Main Objective: The progress of human diseases is influenced by a plethora of different molecular mechanisms. For most
diseases it is difficult to identify key regulatory mechanisms as not all molecular species can be observed experimentally.
Dynamic, mechanistic models can predict the dynamics of experimentally non-observable states, which allows for a more
holistic understanding of the underlying mechanisms. However, for meaningful predictions the parameters of the dynamic
model have to be estimated from experimental data. This process of estimating parameters from experimental data can
be computationally challenging, especially for models describing interactions from pathway-scale to genome-scale where
thousands of parameters have to be estimated. In this project we developed techniques that render parameter estimation for
such large-scale dynamic models tractable.
Methods: In many parameter estimation algorithms gradient-based optimization schemes are employed. Usually, gradients
are computed using forward sensitivity equations or finite differences. These approaches scale poorly with model complexity
and become prohibitively time consuming for genome-scale models. Instead, we propose the use of adjoint sensitivities
analysis as a more efficient way to compute sensitivities for large-scale differential equation models.
Results: We find that by using adjoint sensitivities we can reduce the computational burden of simulating genome-scale
model and respective sensitivities by multiple orders of magnitude compared to finite difference and forward sensitivity
based approaches. Furthermore, we estimated parameters for a genome-scale model from experimental data from multiple
43
Program /
Abstracts
thousand experimental conditions and showed that estimation time could be reduced to a small fraction of the previously
required time.
Conclusion: These results demonstrate that the developed techniques facilitate parameter estimation for pathway-scale
to genome-scale models with thousands of state variables and thousands of parameters. This suggests the feasibility of
predictions from dynamic mechanistic models trained on highly multiplexed data, such as transcriptomics or proteomics
datasets, which were previously deemed to be intractable.
OP 19
Development of an integrative model to improve anemia treatment in non-small cell lung
carcinoma patients
Rodriguez-Gonzalez, A.1,2,3, Schelker, M.4,5, Raue, A. 5,6, Steiert, B. 5, Böhm, M. E.1, Salopiata, F.1,3, Adlung, L.1, Stepath, M.1, Depner, S.1,2,3, Wagner, M.- C.1,
Merkle, R.1,2, Kramer, B. A.1, Lattermann, S.1, Wäsch, M.1,3, Franke, A.7, Klipp, E.4, Wuchter, P. 8, Ho, A. D. 8, Lehmann, W. D.1, Jarsch, M.7, Schilling, M.1,
Timmer, J. 5,9, Klingmüller, U.1,2,3
German Cancer Research Center (DKFZ), Systems Biology of Signal Transduction, Heidelberg, Germany
Bioquant, Heidelberg University, Systems Biology of Signal Transduction, Heidelberg, Germany
3
German Center for Lung Research (DZL), Translational Lung Research Center (TLRC), Heidelberg, Germany
4
Institute of Biology, Humboldt-Universität zu Berlin, Theoretical Biophysics, Berlin, Germany
5
Institute of Physics, University of Freiburg, Freiburg, Germany
6
Merrimack, Discovery Division, Cambridge, United States
7
Roche Diagnostics GmbH, Roche Innovation Center , Pharma Research and Early Development (pRED), Penzberg, Germany
8
Heidelberg University, Department of Medicine V, Heidelberg, Germany
9
University of Freiburg, Centre for Biological Signalling Studies (BIOSS), Freiburg, Germany
1
2
Background: Cancer associated anemia is a common complication affecting 40% of all cancer patients during the course
of the disease. It is particularly prevalent in lung carcinoma, with an incidence of 50-70% during disease development and
reaching ≤90% in the advanced stages. Tumor inflammation and myelosuppressive chemotherapy are two of the major causes
of anemia, reducing the therapeutic response and the quality of life in patients. Erythropoiesis Stimulating Agents (ESAs)
have been widely used to reestablish normal levels of erythrocytes. However, up to 30-50% of cancer patients do not respond
to the ESA treatment in cancer and several clinical trials reported higher mortality risk in ESA treatment. Furthermore,
low levels of erythropoietin receptor (EpoR) protein expression has been found in tumor cell lines, rising concerns on EpoR
activation in tumor context by ESA treatments. The low efficiency of anemia treatment and the safety concerns, indicate that
new strategies are urgently required to improve the treatment options.
Method: To transfer the dynamics of the interaction of the ligand erythropoietin (Epo) and the EpoR in the tumor
and hematopoietic context, we combined mathematical modeling with quantitative data from pharmacokinetic and
pharmacodynamic experiments of ESAs in human subjects, ESAs depletion in human erythroid progenitors and Non-Small
Cell Lung Carcinoma (NSCLC) cell lines and the activation of signal transduction through the EpoR by mass spectrometry.
The experimental data was used to establish an integrative mathematical model utilizing coupled ordinary differential
equations (ODE) that links the cellular scale with the body scale.
Results: The ODE model was able to describe the dynamic interaction of all ESAs at molecular, cellular and systemic level
in the human body. Further, the ODE model facilitated to estimate the binding properties of all tested ESAs, to predict
personalized optimal and safe dosage protocols for each ESA to activate the EpoR in the hematopoietic context but not in the
tumor context.
Conclusion: This model can describe the binding properties, dynamic interaction and the pharmacokineticpharmacodynamics of any EpoR ligand, predicting eficient and safe range of ESA concentrations in Lung cancer patients.
OP 20
An expandable, multi-level, and multi-scale model for drug simulations of weight-loss and
type 2 diabetes
Nyman, E.1, Strålfors, P.1, Cedersund, G.1
1
Linköping University, Biomedical Engineering, Linköping, Sweden
Question: Obesity and the metabolic syndrome are multi-level (intra-cellular to whole-body) and multi-scale (seconds to
years) diseases, and to properly understand the action of associated drugs, a multi-level and multi-scale understanding is
required. Such an understanding can only be obtained using systems pharmacology approaches, combining large amounts
of data with mathematical modelling. However, all data cannot be considered simultaneously, since the complete modelling
problem is too big. We therefore present an iterative approach, where sub-systems first are examined in isolation, and then
integrated into a growing understanding of the whole.
Methods: Models on all levels are described using ordinary differential equations and implemented in matlab. On the wholebody level, the sub-systems (e.g. the organs) are experimentally characterized in terms of their input-output profiles, which
44
allows the sub-systems to be modelled in isolation [1]. On the intracellular level, data include time-series and dose-responses
for cellular metabolism and metabolic regulation, including mass spectrometry data describing the entire phosphoproteome
[2-4].
Results: Through >10 iterations between experiments and modelling, we have unravelled the key mechanistic difference
between normal healthy signaling, and insulin resistance in adipocytes: a feedback between the two proteins mTORC1 and
IRS1 [3]. Apart from this feedback and a few already established protein abundances, the complete model explains both
the normal and diabetic state using the same parameters (Fig 1)[3-4], and can describe all 30 000 phosphorylation sites
measured in response to insulin stimulation. The model has been validated in several independent validation experiments,
without parameter refitting, and can also translate the intracellular metabolic regulation to whole-body glucose homeostasis,
describing both short-term (minutes and hours), and long-term changes (months and years) (Fig 2)[3-4].
Conclusions: We now have a multi-level model skeleton for several of the key changes involved in the development of type
2 diabetes. To this skeleton, new data and insights are continuously being added. The model and the modelling methodology
are already used extensively by big drug development companies.
[1] Nyman et al, J Biol Chem, 286(29):26028-41, 2011
[2] Humphrey et al, Cell Metab, 17(6):1009-20, 2013
[3] Brännmark et al, J Biol Chem, 288(14):9867-80, 2013
[4] Nyman et al, J Biol Chem, 289(48):33215-30, 2014
OP 21
Model-guided target identification for synergistic combination therapies in the DNA
damage response pathway
Raue, A.1, Alkan, O.1, Koshkaryev, A.1, Drummond, D.1, McDonagh, C.1, Schoeberl, B.1
1
Merrimack, Discovery, Cambridge, United States
Cancer cells that are exposed to genotoxic stress activate signaling pathways that give rise to a variety of cellular responses
such as DNA damage repair, cell cycle arrest and apoptosis. The relationships between signaling and these cellular responses
are complex and not fully understood. In this work we present, for the first time, a computational model that quantitatively
links the DNA damage signaling pathway response to cellular responses. In turn, these cellular responses are linked back
to the origin of the DNA damage, creating a closed loop control system. We systematically investigate potentiating drug
combinations in vitro and in vivo between DNA damage inducing chemotherapy and DNA damage signaling modulators and
trained and validated our computational model with an extensive signaling data-set and time-lapse microscopy tracking the
cell fate. The resulting trained computational model can predict the cellular responses based on alteration in DNA damage
signaling pathway. We show how the model can be used to identify drug targets within the DNA damage repair pathway that
are synergistic with different classes of DNA damaging agents.
45
Program /
Abstracts
OP 22
Identification of genetic determinants of immune cellular homeostasis using genetically
diverse mouse strains
Dubovik, T.1, Starosvetsky, E.1, Ziv-Kenet, A.1, Normand, R.1, Asbeh, N.1, Admon, Y.1, Shen-Orr, S.1
Technion-Israel Institute of Technology, Immunology, Haifa, Israel
1
Main objective: The immune system is one of the major life-sustaining systems in advanced organisms, employing dozens if not
hundreds of distinct cell types each with their own function to orchestrate defense mechanisms. The immune system maintains
cells in homeostasis, that is a relatively conserved stoichiometry which is altered during disease, and is returned to following.
Baseline variation in cellular homeostasis exist between individuals which contributes to differences in immune response and is
due both to environmental and complex genetic factors, but exhaustively mapping these to date has proven difficult. Here we set
out to identify the genetic factors effecting individual level differences in baseline cellular immune homeostasis.
Materials and methods: To address this question, we coupled a highly genetically diverse set of mouse models, the
Collaborative Cross, which allow for high resolution genetic mapping; with mass-cytometry (CyTOF) a high dimensional
single cell profiling technology which allows for high dimensional characterization of hundreds of thousands of cells in a
sample. We profiled over 80 mice samples from 38 strains in bone marrow and spleen using CyTOF technology. We analyzed
the data using high resolution clustering, followed by QTL analysis.
Results: We identified a large number of genes that are significantly associated with observed variation in cell subset
frequency across samples. Gene enrichment analysis revealed these are strongly enriched for cell death and survival genes
which is supported by differences in proliferation rates in dividing cells between mice. Knowledge of which genetic variants
of these genes a mouse carries allows prediction of immune cell subset frequency in an untested mouse.
Conclusions: Cellular homeostasis is a complex trait whose maintenance of and fine tuning is performed through a delicate
balance of genetic variants of cell production and turnover.
OP 23
Assessing aneuploidy heterogeneity using single cell sequencing
Taudt, A.1, Bakker B.1, Ferronika, P. 2, Belderbos, M. E.1,3, van den Bos, H.1, Porubsky, D.1, Spierings, D. C. J.1, Saber, A.4, de Jong, T. V.1, Halsema, N.1,
Kazemier, H. G.1, Hoekstra-Wakker, K.1, Hiltermann, T. J. N. 5, Kok, K. 2, van der Wekken, A. J. 5, Timens, W.4, Bradley, A.6, E. de Bont, S. J. 3, Guryev, V.1, H.
Groen, J. M. 5, Lansdorp, P. M.1, van den Berg, A.4, Foijer, F.1, Colomé Tatché, M.7,1
University Medical Centre Groningen, ERIBA, Groningen, Germany
University Medical Centre Groningen, Department of Genetics, Groningen, Netherlands
3
University Medical Centre Groningen, Department of Paediatrics, Groningen, Netherlands
4
University Medical Centre Groningen, Department of Pathology & Medical Biology, Groningen, Netherlands
5
University Medical Centre Groningen, Department of Pulmonary Diseases, Groningen, Netherlands
6
Wellcome Trust Sanger Institute, Hinxton, Cambridge, Great Britain
7
Helmholtz Zentrum München, Institute of Computational Biology, Neuherberg, Germany
1
2
Whole chromosome duplications and deletions (aneuploidies) and copy number variations (CNVs) have been associated with
many diseases, most prominently cancer. In cancer generally multiple aneuploidies and CNVs coexist in the same tumor,
and this heterogeneity level may influence the metastatic potential of cancers and may determine the success of specific
treatments. Quantifying the level of karyotype heterogeneity is therefore crucial for understanding the relationship between
chromosomal instability and disease, and will help in the design of personalized treatments. Unfortunately, classic genomic
profiling by comparative genomic hybridization (CGH) or whole-exome sequencing (WES) of bulk material can only identify
global genomic alterations of a cell population.
Single-cell whole genome Next Generation Sequencing (NGS) techniques offer a powerful way to study CNVs in a highthroughput fashion at a single cell level with high resolution. To analyze this single cell sequencing data we have developed
AneuFinder, a method for the accurate and fully automated mapping of CNVs from single-cell NGS data which explicitly
models the data structure of single cell NGS experiments by using a Hidden Markov Model where every distinct copynumber is assigned to a hidden state, and where read count distributions are modeled as negative binomial distributions. The
resolution of this approach is only limited by sequencing coverage.
We applied this method to map and quantify karyotype heterogeneity in mouse and human cancer samples. We used a mouse
model of chromosome instability and show that the arising tumors exhibit high-grade karyotype heterogeneity, even though
they tend to converge to particular and recurring chromosome combinations. These results indicate that selection forces for
particular chromosome alterations can outcompete high missegregation rates. We also assessed CNVs in human primary
small cell lung cancer tumors and corresponding liver metastasis, and found presence of rare cells in the primary tumor
samples that showed CNV patterns identical to the metastases CNV pattern, suggesting that only specific cells or subclones
of the primary tumor are responsible for the metastatic lesions.
As chromosomal instabilities may drive tumour evolution, karyotype analysis using single-cell sequencing technology could
very well become an essential tool for cancer treatment stratification in the near future.
46
POSTER PRESENTATIONS
PP 1: Image-based Systems Biology
PP 1-01
Label-free cell cycle analysis for high-throughput imaging flow cytometry
Blasi, T.1, Hennig, H. 2, Summers, H. D. 3, Theis, F. J.1, Cerveira, J.4, Patterson, J. O.4, Davies, D.4, Filby, A. 5, Carpenter, A. E. 2, Rees, P. 2,3
ICB Helmholtz Zentrum München, Neuherberg, Germany
Broad Institute, Cambridge, Great Britain
3
Swansea University, Swansea, Great Britain
4
The Francis Crick Institute, London, Great Britain
5
Newcastle Upon Tyne University, Newcastle Upon Tyne, Great Britain
1
2
Imaging flow cytometry combines the high-throughput capabilities of conventional flow cytometry with single-cell imaging.
Here we demonstrate label-free prediction of DNA content and quantification of the mitotic cell cycle phases by applying
supervised machine learning to morphological features extracted from brightfield and the typically ignored darkfield images
of cells from an imaging flow cytometer. This method facilitates non-destructive monitoring of cells avoiding potentially
confounding effects of fluorescent stains while maximizing available fluorescence channels. The method is effective in cell
cycle analysis for mammalian cells, both fixed and live, and accurately assesses the impact of a cell cycle mitotic phase
blocking agent. As the same method is effective in predicting the DNA content of fission yeast, it is likely to have a broad
application to other cell types.
PP 1-02
Cells commit to the cell cycle by rapid and irreversible inactivation of APCCdh1
Cappell, S.1, Chung, M.1, Jaimovich, A.1, Spencer, S.1, Meyer, T.1
1
Stanford University, Chemical and Systems Biology, Stanford, United States
The current model of the mammalian cell cycle distinguishes a single commitment point, when cells can no longer exit to
quiescence, from multiple subsequent checkpoints, when committed cells pause on their path to complete the cell cycle. A
commitment point often consists of a bistable mechanism that confers strong hysteresis, meaning a different sensitivity to
inputs before and after the switch. We used live-cell time-lapse imaging of cell cycle biosensors and single-cell image analysis
to identify the mechanism underlying cell cycle commitment. Here we demonstrate that inactivation of the anaphase
promoting complex/cyclosome (APC-Cdh1) constitutes a bistable switch in late G1-phase that is initiated by cyclin E/Cdk2
and made irreversible by Emi1 with strong hysteresis in respect to stress sensitivity and Cdk2 inactivation. The switch
constitutes a bona fide commitment point, as exposure to stress until but not after APC-Cdh1 inactivation reverted cells to
a mitogen-sensitive quiescent state allowing for later re-entry into the cell cycle. The switch is distinct from activation of
the Rb-E2F pathway earlier in G1, as stress-sensitivity persisted for many hours after Rb hyperphosphorylation and E2F
activation; and is also distinct from the checkpoint at the G1/S boundary, which pauses cells at the onset of S-phase only
after APC-Cdh1 inactivation. Thus, APC-Cdh1 inactivation constitutes a bistable switch with the necessary characteristics to
be the commitment point for cell cycle entry.
PP 1-03
Image-based systems medicine: from mechanistic models to decision-support systems in
the clinic
Sten, S.1,2, Karlsson, M.1,2, Forsgren, M. 2, Lundengård, K.1,2, Lundberg, P. 2, Engström, M. 2, Cedersund, G.1
1
2
Linköping University, Biomedical Engineering, Linköping, Sweden
Linköping University, Medicine and Health, Linköping, Sweden
Question: Clinical diagnosis is today often based on images from magnetic resonance imaging (MRI) cameras. Such
images can characterize e.g. fat infiltration in the liver and areas of activity in the brain (fMRI). However, today,
such characterizations are done using simple phenomenological approaches, such as quantifications of amplitudes,
normalizations, and correlations with basis functions. In this talk, we will demonstrate that data analysis is improved by
usage of mechanistic models.
Methods: Models were formulated as ordinary differential equations in matlab; parameters were inferred from data
within physiological ranges using nonlinear mixed-effects modelling; and predictions were obtained with uncertainty by
considering all feasible parameters. Data are obtained from healthy subjects and patients: liver patients have suspected nonalcoholic fatty liver disease, brain patients have stroke. Time-series measurements for the liver measure the uptake of a liverspecific contrast agent, and brain measurements detect blood oxygenation in response to visual stimuli and tasks.
Results: The models can describe training data and independent validation data without parameter refitting [1-2] (Fig
47
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Abstracts
1A,B). Key parameters estimated with low uncertainty provide new and clinically valuable information: liver transport rate
parameters are able to predict the degree of fibrosis seen in liver biopsies better than any other previously suggested method
for analysis of MRI data, and model-based brain activity and blood-flow parameters are more accurate and reliable estimates
of these entities compared to corresponding estimates obtained using clinically used software (Fig 1C).
Conclusions: We have demonstrated that the mechanistic process understanding that goes into our mechanistic models
allow us to provide a superior image analysis in terms of personalized diagnosis. This demonstrates that some mechanistic
models now are ready for implementation in end-usage decision-support systems. We end by a discussion of the big changes
in our healthcare systems that such implementations will imply, and how we combine competences of widely different
characters to soundly approach these changes (Fig 2). This discussion will also illustrate that our mechanistic models
open the door to multi-level decision-support models and a combination of radiology and omics, sometimes referred to as
radiomics (Fig 3).
[1] Forsgren et al, PLoS One, 9(4):e95700
[2] Lundengård et al, submitted
PP 1-04
Investigation of Bile canaliculi formation and biliary transport in 3D in vitro liver cultures
Damm, G.1, Deharde, D.1, Schneider, C. 2, Hiller, T. 2, Kegel, V.1, Lübberstedt, M. 2, Freyer, N. 2, Seehofer, D.1, Zeilinger, K. 2
Charité - Universitätsmedizin Berlin, Klinik für Allgemein-, Visceral- und Transplantationschirurgie, Berlin, Germany
Charité - Universitätsmedizin Berlin, Bioreactor Group, Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany
1
2
Background: Primary human hepatocytes (PHH) are still considered as gold standard for investigation of in vitro
metabolism and hepatotoxicity. It has been shown that the three- dimensional (3D) cultivation of PHH in a sandwich
configuration between two layers of extracellular matrix (ECM) enables the hepatocytes to adhere three-dimensionally
leading to formation of in vivo like cell-cell contacts and cell-matrix interactions. In the present study the influence of the
matrix composition on hepatocyte morphology and functionality was analysed applying a systems biological approach.
Methods: Human hepatocytes were isolated from human liver resectates using a two-step collagenase perfusion technique.
Freshly isolated PHH were cultured for 6 days between two ECM layers made of collagen and/or Matrigel in four different
combinations. The cultures were investigated by phase contrast microscopy and immunofluorescence analysis with respect to
cell-cell connections, repolarization as well as bile canaliculi formation. Finally, the bile canalicular transport was analyzed
by live cell imaging to monitor the secretion and accumulation of the fluorescent substance CDF in bile canaliculi. Large
image data sets were analyzed using biostatistical and bioinformatic methods.
Results: Investigated sandwich cultured PHH showed ECM dependant differences in morphology and cellular arrangement.
Cultures with an underlay of collagen seem to represent best the in vivo tissue architecture in terms of formation of
48
trabecular cell arrangement. Cultures overlaid with collagen were characterized by the formation of abundant bile canaliculi,
while the bile canaliculi network in hepatocytes cultured on a layer of Matrigel and overlaid with collagen showed the most
branched and stable canalicular network. All cultures showed a time-dependent leakage of CDF from the bile canaliculi into
the culture supernatant with variations of CDF half-life in dependence of the used matrix combination.
Conclusion: In conclusion, the results of this study show that the choice of ECM has an impact on the morphology, cell
assembly and bile canaliculi formation in PHH sandwich cultures. The morphology and the multicellular arrangement were
essentially influenced by the underlaying matrix, while bile excretion and leakage of sandwich cultured hepatocytes were
mainly influenced by the overlay matrix. Leaking and damaged bile canaliculi could be a limitation of sandwich culture
models in long-term excretion studies.
PP 1-05
Imaging the dynamics of the proteome in response to viral infection
Drayman, N.1, Alon, U.1
Weizmann Institute of Science, Cell Biology, Rehovot, Israel
1
Individual cells in a population can show different responses to stress stimuli. We explored how viral infection of human
cells is affected by cell-to-cell variations in protein level and localization over time. To address this, we use a unique assay
developed in our lab to follow ~500 different proteins, fluorescently tagged at their endogenous chromosomal locus. We
determine the proteomic response of the cells to Herpes Simplex Virus 1 infection, a well-studied viral system and a major
human pathogen. We follow the dynamics of proteins localization and level in each individual cell over time by timelapse microscopy, under conditions where some of the cells become successfully infected while others are able to resist the
infection. This allows us to identify proteins whose differential dynamics at early times correlates with eventual infection
success or failure. This study provides the first global view on how initial cell-to-cell variability correlates with the outcome
of infection, allowing us to move from a “virus-centric” view, where each cell is equally likely to become infected, to a more
complex “virus-host” view, where the probability of infection also depends on the cell state.
PP 1-06
Functional intravital imaging of acetaminophen induced liver injury and regeneration
Ghallab, A.1, Reif, R.1, Hassan, R.1, Seddek, A. 2, Hengstler, J. G.1
1
2
IFADo, Dortmund, Germany
Faculty of Veterinary Medicine, South Valley University, Qena, Egypt, Egypt
Background and objective: Acetaminophen (APAP) overdose is one of the most frequent causes of acute liver failure
worldwide. Although the signaling pathways involved in cell death are extensively studied, the sequence of events during
the destruction and the regeneration processes are not completely understood. Here intravital imaging using two-photon
microscopy can play a role. It allows imaging of biological processes in a real time. The purpose of this study is to image in vivo
the cell death and regeneration after APAP challenge.
Materials and methods: Acute liver damage was induced by intraperitoneal administration of APAP (300 mg/kg).
Intravital imaging was done using two-photon microscopy after administration of the following vital dyes: Hoechest, a DNA
marker; Rhodamine (TMRE), a marker of mitochondrial membrane potential; Cholyl-Lysyl-Fluorescein (CLF), a fluorescein
labeled bile salt) and propidium iodide, a marker of cell death. Immune cells infiltration was visualized using cell type specific
reporter mice.
Results: The earliest event, approximately 20 min after APAP injection, was loss of mitochondrial activity of pericentral
hepatocytes as evidenced by TMRE loss. The compromised mitochondrial function was followed immediately by ballooning
of the bile canaliculi. Approximately 1h later the widened bile canaliculi invaginated into the adjacent hepatocytes. 2h after
APAP injection, hepatocytes with bile canalicular protrusions lost their ability to secrete bile salts, leading to irreversible
cell death as evidenced by propidium iodide uptake. Interestingly, this bile salts mediated cell death occurs only at the
outer pericentral hepatocytes. In contrast, hepatocytes which are in close contact to the central vein showed nuclear
fragmentation. This destruction process is followed by a well-orchestrated regeneration process characterized by stellate cells
activation and immune cells infiltration. Our imaging revealed that macrophages infiltrate the dead cell area only after day
two following APAP injection. Although work for a formal proof is still in progress, it seems as if the infiltrating macrophages
interact with and finally phagocytose stellate cells in the dead cell area. This was supported by the prolonged activation of
stellate cells when we depleted macrophages by clodronate administration
Conclusion: The direct observation of cellular and sub-cellular events in the living liver allows insights into the sequence of
pathophysiological events which are difficult to obtain by conventional methods.
49
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PP 1-07
A Systems Survey of Progressive Host Cell Reorganization During Rotavirus Infection
Green, V.1, Pelkmans, L.1
University of Zurich, Institute of Molecular Life Sciences, Zurich, Switzerland
1
Question: Pathogen invasion is often accompanied by widespread alterations in cellular physiology, which reflects the
hijacking of host factors and processes for entry and replication. Although genetic perturbation screens have revealed the
complexity of host factors involved for numerous pathogens, it has remained challenging to disentangle this complexity
along the progression of host cell reorganization during the infection process.
Methods: We here address this by combining the first high confidence, image-based, genome-scale RNAi screening of
rotavirus infection in human intestinal cells with an innovative approach to infer a continuous trajectory of virus infection
progression from fixed cell populations.
Results: All five large-scale RNAi screens performed were reproducible and showed a high degree of consistency. Through
a probabilistic method for the aggregation of multiple datasets, we obtained a gene score based on their likelihood to
confer an on-target infection phenotype upon RNAi, within which known rotavirus host factors were retrieved, validating
our approach. By developing a novel, rank-based analysis for functional annotation enrichment, we provide an unbiased,
systems-wide view of the cellular processes important for rotavirus replication. The result is a wealth of newly identified host
factors in rotavirus infection, providing a rich resource for the field. Combined with our multivariate, single-cell infection
progression trajectory, we uncover a complex, yet ordered host cell reorganisation program during rotavirus infection that
provides a replication-permissive cellular environment for the virus. This includes an alternative mechanism for host protein
synthesis shut-off during early stages of infection involving mTORC1 inhibition, consumption of lipid stores, rearrangement
of mitochondria, and ER shape remodelling throughout infection, modulated by the ER shaping protein REEP2. Finally,
by integrating the large-scale gene perturbation dataset with single-cell trajectories, we propose a model for host factormediated cellular reorganization during rotavirus infection, many elements of which can be linked to calcium-activated
AMPK signalling.
Conclusions: Our work provides a powerful approach to order the complexity of host cellular requirements along a
trajectory of cellular reorganisation during pathogen invasion, an approach that would enhance the analysis of perturbation
screens from any field.
50
PP 1-08
A Qualitative Method of Identifying Stiff Regions in Fibrous Proteins
Horan, S.1, Kurup, A. 2, Botvinick, E. 2
University of California, Irvine, Mathematics/Center for Complex Biological Systems, Irvine, United States
University of California, Irvine, Biomedical Engineering, Irvine, United States
1
2
Fibrous proteins such as collagen and fibrin are ubiquitous in both cells and the extra cellular matrix surrounding them in
complex organisms. Examining them from an engineering perspective gives rise to questions regarding their mechanical
properties, particularly their behavior under stress. We present a method for using MATLab to identify regions of relative
stiffness and flexibility in such proteins by analyzing images from confocal microscopy. This method uses five major
steps: First, a series of images of these fibers is taken over a short time. Second, areas containing fibers are identified and
skeletonized to locate the likely position of the actual fiber. Third, a mask using this skeletonization is applied to each image,
leaving only pixel/voxel data where we believe the fiber to be located. Fourth, each pixel/voxel remaining is considered as a
stack over time and cross correlated with other itself pixel/voxel stacks. Finally, regions of close correlation to the original
stack are identified, and this region size is considered to be an indication of stiffness.
When testing this method on images of fibrin, we found regions of relative stiffness only where fibers were straight, but the
converse did not hold. We consider this to be in support of our method, as one would expect that stiff regions are straight,
but straight regions need not necessarily be stiff. We also found that regions where the fibers are seen to bend are identified
as less stiff, which is again to be expected. Interestingly, while we started with image stacks of 100 frames for analysis, we
found results to be qualitatively similar when using only much smaller Our image analysis current works in two dimensions,
but we hope to have three ready and tested by the time of the conference. In the future, we plan to extend this work by
examining these proteins under various stresses and compare these results to materials with known bending modulus to
make more quantitative judgments.
PP 1-10
Image-based modeling of organogenesis
Lang, C.1, Michos, O.1, Iber, D.1
1
ETH Zurich, D-BSSE, Basel, Switzerland
To achieve a high surface-to-volume ratio, branched organs such as the lung and the kidney undergo a complex, yet highly
stereotyped morphogenesis process. It is an open question how the branching program can be both stereotypic, yet capable
to adjust to the available space. The molecular players underlying bud specification and outgrowth have been extensively
studied. Thus, FGF10 and GDNF signalling are both necessary and sufficient to guide the outgrowth of new branches in the
lung and kidney respectively. Several mechanisms have been proposed to explain how signalling can self-organize into spots
to define new branch points.
We have combined 3D microscopy and image-based spatiotemporal modeling to test the validity of the proposed models. We
find that of all proposed mechanisms only a receptor-ligand based Turing-type model can reproduce the measured embryonic
growth fields and guide the outgrowth of new branches to recapitulate the embryonic 3D branching process (Menshykau
et al, 2014, Development; Georgieva et al, in preparation). The ligand-receptor based Turing mechanism recapitulates all
reported qualitative lung and kidney branching mutant phenotypes (Celliere et al, 2012). Additionally, we have now used 2D
51
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Abstracts
lung and kidney cultures to quantitatively test the ability of the Turing mechanism to recapitulate the branching behaviour
in mutants and in the presence of inhibitors (Menshykau et al, in preparation; Kull et al, in preparation). The ligand-receptor
based Turing mechanism not only recapitulates all data, but also offers an explanation of how stereotyped patterning can
be achieved in spite of molecular noise and how a space-filling tree can be generated (Menshykau et al, 2014, Development;
Georgieva et al, in preparation).
In conclusion, image-based spatiotemporal modeling allows us to identify, test, and refine mechanisms to explain the control
of the complex branching program during mouse lung development.
PP 1-11
Clustering-based classification of autophagy phenotypes in single cell images as a novel
readout of autophagic activity
Marin Zapata, P. A.1, Hamacher-Brady, A.1
1
DKFZ, Lysosomal Systems Biology, Heidelberg, Germany
Autophagy is a cellular degradative pathway in which cytosolic components are engulfed in double membranes vesicles
(autophagosomes) and subsequently delivered to the lysosomes. Quantification of autophagy in single cells is prone to
misinterpretation, since increased autophagosome number could be indicative of increased autophagosome synthesis
(autophagy activation) as well as blocked degradation (autophagy inhibition). Thus, the autophagy field is still in need for
unbiased single-cell readouts. Imaging cytometry provides a platform for high throughput, single-cell image acquisition and
analysis, which is potentially useful for autophagy quantification. However, the statistical power and high-content multiparametric nature of this technique have not been exploited within the context of autophagy. In this work, we developed
a high content analysis for classification of autophagy phenotypes in imaging cytometry data based on agglomerative
clustering of texture features. Our approach was able to automatically identify subpopulations of low, high and blocked
autophagic flux in multiple cell lines. Furthermore, we propose convenient data representations of subpopulation abundances
(denoted as fingerprints) to establish a subpopulation-based characterization of autophagy response. Besides quantifying cell
heterogeneity, fingerprints provided unique identifiers for fully feed, nutrient deprived and lysosome inhibited conditions,
and correctly reported the magnitude and direction of autophagic flux in terms of number of cells. Thus, we propose our
analysis as an unbiased autophagy readout of potential use for the characterization of new compounds.
PP 1-12
MALDI IMAGING OF A PROSTATE CANCER BIOPSY - A PILOT STUDY
Rise, K.1, Tessem, M.- B.1, Nordgård, A. 2, Borgos, S. E. 2, Bathen, T. F.1, Andersen, M. K.1, Drabløs, F.1, Rye, M.1, Bertilsson, H.1
1
2
Norwegian University of Science and Technology, Trondheim, Norway
SINTEF, Trondheim, Norway
Question: Prostate cancer is the most common cancer in men and the number of diagnosed patients increases every year,
both as a result of men living longer and the increased usage of Prostate Specific Antigen (PSA) as biomarker. Since PSA is
an uncertain marker, new methods for stratification at tissue level is in demand to avoid overtreatment of indolent disease.
Matrix Assisted Laser Desorption Ionization (MALDI) Imaging is a novel tool for finding new biomarkers with better
sensitivity and specificity for prostate cancer. By using a suitable matrix, analytes are extracted and a mass spectrum is
generated for each measuring point in a grid system. This creates a high resolution spatial distribution of analytes across
the tissue sample, where the confounding effects on the signal from artefacts and tissue composition can be easily assessed.
MALDI Imaging can be important in three areas of early detection: biomarker identification and validation, improved
interpretation of clinical images obtained from state-of-the-art magnetic resonance imaging, and improved technology with
MALDI Imaging linking in vivo and ex vivo measurements of metabolism.
Methods: Biopsies were frozen prior to embedding in a gelatin-cellulose mixture, and sliced using a cryostat with optimized
conditions. Slices were placed on indium tin oxide (ITO) slides, and MALDI matrix 2,5-Dihydroxybenzoic acid (DHB) was
added. Mass calibration was performed using matrix spots outside the sample area, and Bruker Solarix FT ICR was applied to
obtain mass spectrometric images. Mass spectra were collected in positive mode in mass range 50 - 2000.
Results: The average spectrum shows that most m/z values were observed around 650 - 950. The images showed a variety of
masses, with colour intensity related to ion abundance, and it clearly shows how the masses are unevenly distributed in the
samples. Most compounds have not yet been identified, although there is a possible match for spermine at m/z 203.
Conclusion: The study shows a potential of using MALDI Imaging in the search for new and improved biomarkers and
identification of metabolism in heterogene tissue of cancerous prostates. Further studies will aim to optimize parameters,
including slice thickness, choice of matrix, pre-processing of slices and optimisation of mass spectrometric method. Further
analysis will be performed to identify compounds in the samples.
52
PP 1-13
Complete evaluation of dynamics in an unlabelled live cell from a z-stack of superresolved bright-field microscopic images
Rychtarikova, R.1, Nahlik, T.1, Stys, D.1
University of South Bohemia, Inst. of Complex Systems, Nove Hrady, Czech Republic
1
Question: Cellular dynamics determines key processes in development of organism, tissue healing and cancer invasion. We
proposed an algorithm of processing of z-stacks of images of unlabelled live cells obtained from wide-field bright-field optical
transmission microscopy to achieve the super-resolved volumes of intracellular objects in the order of 100 nm3, including the
classification of pixels according to an intracellular dynamics and spectral properties [1,2].
Methods: This super-resolved method is based
(1) on non-interpolating de-mosaicing of a 12-bit z-stack of raw images obtained from camera chip equipped by a Bayer mask,
which preserves as much information in the image as possible to obtain RGB images.
(2) on searching for pixels of unchanged (or near) intensities between two consecutive images, after either the simple
subtraction of two consecutive images or the calculation of a point divergence gain:
where k is the Renyi coefficient, j is the number of phenomena in the discrete distribution, pi and p k/l are the probabilities of
occurrences of the i-th phenomenon in the original distribution and in the distribution after supplying one element of the
phenomenon i = k by one element of the phenomenon i = l.
The latter step of image processing is responsible for evaluation of the intracellular dynamics. If PDG = 0 (the same intensities
at the same position in two consecutive images), large intensity homogenous non-moving objects are mainly segmented. At
minimal and maximal values of PDG (a pixel of the rarest intensity is replaced by a pixel of the most frequent intensity and
vice versa), we track a large moving organelles. The other values of PDG correspond either to the other intracellular objects or
to other intensities in the course of the sum of point spread functions of the live cell.
(3) the selection and evaluation of each colour channel of the image. In the blue channel, we observe mainly autofluorescence.
In the green channel, the light diffraction is also projected. In the red channel, the light absorption in near infrared region is
further observed.
Results and Conclusion: The method was tested and verified on a few cell lines and pluripotent stem cells and shows also
a potential to describe dynamics in primary cell cultures. The patent application on the algorithm and related microscope is
also pending [3].
References: [1] R. Rychtáriková et al., in ISCS 2014: Interdisciplinary Symposium on Complex Systems (Emergence,
Complexity and Computation 14), edited by A. Sanayei, O.E. Rössler, I. Zelinka (Switzerland: Springer), 2014, pp. 261-267.
[2] Štys D., Rychtáriková R., Náhlík T., Urban J., Císař P.: Method of Microscopic Images Acquisition and Processing and the
Particular Device, Patent Application No. PV 2015-434.
PP 1-14
3D reconstruction and quantitative analysis of liver human samples
Segovia-Miranda, F.1, Belicova, L.1, Morales-Navarrete, H.1, Seifert, S.1, Hampe, J. 2, Kalaidzidis, Y.1, Zerial, M.1
1
Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
2
Medical Department 1, TUD, Dresden, Germany
The liver has a complex 3D structure that can be hardly extracted from the classical 2D histological approaches. 3D analysis
of the liver tissue should be taken into account for a better understanding of liver physiology and physiopathology. The aims
of this study are 1) to establish a staining protocol to perform high-resolution confocal imaging on thick human liver slices
and 2) to quantitatively describe the 3D architecture of human liver tissue. Human liver sections (100 um thick) were stained
for cell borders (LDL receptor), bile canaliculi (CD13), sinusoids (goat anti-human A488), and nucleus (DAPI), optically
cleared and imaged using multiphoton microscopy. The Motion Tracking software was used to segment and quantitatively
describe 1) the bile canaliculi and sinusoidal networks and 2) the hepatocyte morphology. Our data showed that the set of
antibodies and dyes chosen allows the 3D reconstruction of several components of liver architecture, including central vein,
53
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Abstracts
portal vein, bile canaliculi, sinusoids, nuclei and hepatocytes. We provided a quantitative description of the bile canaliculi
and sinusoidal networks (total length, connectivity, volume fraction, diameter, mean branch length and density). We
also extracted precise quantitative morphometric parameters of liver tissue focusing on hepatocyte cells (e.g. hepatocyte
volume, surface area, apical, lateral and basal surface, % mono-nuclear and bi-nuclear hepatocytes, number of neighbors).
We believe the detailed structural information provided by the 3D analysis of liver architecture will reveal new aspects of
organ physiology. This new tool will be used during the LiSyM funding period to image human samples of patients diagnosed
with non-alcoholic fatty liver disease (NAFLD) to describe the morphological changes occurring in the tissue during
disease progression. This will allow exploring new aspects of liver disease that have not been described before due to the
technological challenge, thus contributing to the understanding of NAFLD liver physiopathology.
PP 1-15
QUANTITATIVE ANALYSIS OF TUMOR CELL LOAD BASED ON DIFFUSION-WEIGHTED MRI
AND HISTOLOGY DATA
Yin, Y.1,2, Sedlaczek, O. 3,4,5, Müller, B.6, Lotz, J.7, Warth, A. 3,6, González-Vallinas, M.6, Lahrmann, B.6,8, Grabe, N.6,8, Kauczor, H.- U. 3,4,5, Olesch, J.7,
Breuhahn, K.6, Vignon-Clementel, I. E.1,2, Drasdo, D.1,2,9
INRIA de Paris, Paris, France
Sorbonne Universités UPMC Univ. Paris 6, Laboratoire Jacques-Louis Lions, Paris, France
3
Translational Lung Research Center Heidelberg (TLRC), member of the German Centre for Lung Research (DZL), Heidelberg, Germany
4
Department of Diagnostic & Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany
5
Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University of Heidelberg, Heidelberg, Germany
6
Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
1
2
Fraunhofer Mevis, Lübeck, Germany
Department of Medical Oncology, NCT, University Hospital Heidelberg, Heidelberg, Germany
9
IZBI, University of Leipzig, Leipzig, France
7
8
Tumor cellularity is an important tissue microstructural feature, which is useful for cancer diagnosis and cell number related
treatment such as targeted cancer therapies. Diffusion-weighted MRI (DWI) that indicates the rates of water diffusion in
tissue is used in medical imaging as it is thought to provide an indirect measure of tumor cellularity. Histopathological
examination of tissues reveals the tissue microstructure, but is usually available as a small sample or after resection. How
to quantitatively estimate the total tumor cell load is still a challenging work. In this study, we proposed a pipeline for
tumor cell load estimation based on image processing techniques. The main pipeline steps include: 1) adaptive cell nuclei
segmentation; 2) model-based 3D cell density estimation; 3) correlation identification between the DWI diffusion coefficient
(D value) and the underlying tumor cellularity; 4) cell number calculation based on tumor D values and the identified
correlation between diffusion and cellularity. The proposed cell segmentation algorithm performs detection with a very high
accuracy (0.95) on selected tumor tissue samples. For a specific patient with NSCLC, the estimated total cell number of the
tumor (6.5 cm in diameter) is 3.64×1010. The proposed method translates tumor DWI signals into cell density and quantifies
the whole tumor cell load. The developed method can be applied to other cancer types.
PP 1-16
Investigating Microenvironment-to-cell Signaling in 3D Spheroids through Imaging Mass
Cytometry
Zanotelli, V.1, Georgi, F.1, Schapiro, D.1, Andriasyan, V.1, Yakimovich, A.1, Catena, R.1, Jackson, H.1, Bodenmiller, B.1
University of Zurich, Institute of Molecular Life Science, Zurich, Switzerland
1
Question: In vitro studies using monolayer 2D cell cultures inherently fail to model a physiological 3D setting, including 3D
cell-to-cell contacts as well as nutrient and oxygen gradients. 3D tissue models such as spheroid cell cultures overcome this
drawback and resemble the in vivo situation more faithfully. Consequently these tissue models are currently broadly adapted
in biomedical and pharmaceutical research.
Every cell senses its local 3D environment and adapts its phenotype accordingly. This process is a major driver for the
regulated phenotypic heterogeneity in tissue development and homeostasis. When deregulated it can drive diseases such
as cancer. However, how the interactions of cells with their environment collectively shape cellular phenotypes in tumors
and contribute to tumor heterogeneity is largely unknown. In order to tackle this question we set out to develop a high
throughput setup to quantify the influence of microenvironmental influences and cell-to-cell communication on the
phenotypic heterogeneity in an in vitro 3D breast cancer cell culture system.
Methods: Imaging mass cytometry (IMC) allows the simultaneous quantification of more than 40 phenotypic and functional
markers at subcellular resolution in slices of 3D tissues. This makes this technology suitable to study the spatial relationships
of complex phenotypes and how both, relationships and phenotypes, are perturbed by small molecule inhibitors. To enable
screening approaches using 3D tissue models and imaging mass cytometry, we are developing a workflow based on metal
label based barcoding that allows pooling of spheroids treated with a multitude of conditions before the embedding and
54
cutting steps for subsequent IMC. In the future the presented pipeline will allow for the concurrent embedding and cutting
of a 384 well sphere culture plate with up to 384 different conditions, making a high throughput screen with 3D spheroids
coupled with an IMC readout feasible.
Results: We use this workflow to study the origins of phenotypic heterogeneity of 3D cultured breast cancer cell lines.
Preliminary data assesses the feasibility and challenges a barcoding based approach to combine 3D spheroid cell culture
technology efficiently with IMC. Further, based on data of unperturbed breast cancer spheroids we show how IMC can
capture phenotypic heterogeneity and coordination in the microenvironment. We also explore how such data can be
integrated using mathematical modeling to gain quantitative insights.
Conclusions: We present the development of a broadly applicable, scalable screening approach efficiently combining high
throughput 3D microtissue culture with IMC. The approach will also be applicable to more complex cell culture settings, such
as 3D co-cultures and organoids and to advanced readouts such as 3D IMC. Combined with an inhibitor screen the technology
will be a solid basis to investigate how phenotypes are spatially coordinated in 3D tumor models.
PP 1-17
Analysing the Impact of Errors in Single Cell Tracking Experiments
Zerjatke, T.1, Roeder, I.1, Glauche, I.1
TU Dresden, Institute for Medical Informatics and Biometry, Dresden, Germany
1
Time-lapse video microscopy is an increasingly popular method to study the temporal and spatial behaviour of single cells. It
can be used for a broad range of applications, e.g. the analysis of cell motility and migration, proliferation properties, clonal
composition, or the reconstruction of the complete divisional history of cells, represented as cellular genealogies.
A large number of automated methods has been developed for segmenting and tracking single cells. Although these
methods are increasingly sophisticated to cope with a broad spectrum of situations they inevitably produce errors in the
reconstruction of cellular tracks. The number of errors can be reduced by using post- processing tools for the manual
correction of automatically created tracks. However, ambiguous situations can occur that lead to different subjective
decisions of individual raters in the assignment of cellular objects. The number of these ambiguous situations and hence the
number of differences in the reconstructed cellular tracks depends on cell type specific properties like migration speed or
proliferation rate, as well as on specific properties of the experimental setting like the spatial and temporal resolution of the
image sequence or the density of seeded cells.
Here we study this inter-rater variability exemplarily for in vitro cultures of haematopoietic stem and progenitor cells
and analyse its impact on the reconstruction of cellular genealogies and statistical measures of e.g. migration properties.
Furthermore, we use computer simulations of in vitro cell cultures that allow to comprehensively analyse a broad range of
cell type specific and experimental properties. Specifically, we aim to quantify maximum error rates that are admissible to
reliably measure a particular statistical outcome. These maximum admissible error rates can then be accounted for in the
design of the experimental set-up and the choice of the cell tracking procedure.
PP 2: Single-Cell Systems Biology
PP 2-01
destiny - diffusion maps for large-scale single-cell data in R
Angerer, P.1, Haghverdi, L.1, Büttner, M.1, Theis, F.1, Marr, C.1, Büttner, F. 2
1
2
Institite of Computational Biology, Neuherberg, Germany
European Bioinformatics Institute, European Molecular Biology Laboratory, Cambridge, Great Britain
Diffusion Maps (DMs) are a spectral method for non-linear dimension reduction. destiny is an efficient R implementation of
the diffusion map algorithm, adapted for the visualization of single cell expression data. Those adaptions include a singlecell specific noise model allowing for missing and censored values, an efficient nearest-neighbour approximation that allows
for the processing of hundreds of thousands of cells, and a functionality for projecting new data on existing diffusion
maps. We show the application of destiny to 4 datasets of varying size and complexity to show both the capabilities of the
implementation and the usefulness of the method and its adaptions.
55
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Abstracts
PP 2-02
Sensitive Detection of Rare Disease-Associated Cell Subsets via Representation Learning
Arvaniti, E.1, Claassen, M.1
1
ETH Zurich, Biology, Zurich, Switzerland
Rare cell populations play a pivotal role in the initiation and progression of diseases like cancer. While increasingly
multiparametric single-cell technologies are opening a new perspective to study well-characterized low frequency cell
subsets, the systematic de novo identification of such disease-associated cell subsets remains an unsolved challenge.
This work describes CellCnn, a supervised representation learning approach to infer and detect, possibly rare, diseaseassociated cell types from single cell measurements. Specifically, CellCnn learns an optimal representation of a set of single
cell abundance profiles with respect to an associated phenotype by means of a convolutional neural network. The learned
representation lends itself to identify molecular profiles of phenotype-associated cell subsets. We demonstrate the ability of
CellCnn to correctly identify subsets of peripheral mononuclear blood cells with differential signaling response to various
paracrine agents, and subsets associated with continuous valued clinical parameters such as disease onset in HIV infected
individuals. We specifically assess the CellCnn capability to detect extremely rare leukemic blast cell populations in acute
lymphoblastic leukemia and acute myeloid leukemia. While conventional cell sample classification approaches fail to identify
the leukemia-associated cell subsets for any of the considered cell frequencies, CellCnn is able to faithfully achieve this task
for cell subset frequencies as low as 0.005 %.
CellCnn constitutes a dedicated supervised machine learning approach to identify rare phenotype-associated cell subsets
from single cell measurements. With the dramatic increase in single cell studies of diverse cancers and other diseases, we
expect CellCnn to enable de novo, disease- and possibly also patient-specific discovery of disease-associated cell populations
and thereby contribute to the elucidation of disease mechanisms mediated by rare cell events and the design of cell subset
specific treatment interventions.
PP 2-03
Information theoretic analysis of interleukin-6-induced signalling by multi-colour flow
cytometry
Billing, U.1, Rummel, H.1, Schaper, F.1, Waldherr, S. 2, Dittrich, A.1
Otto-von-Guericke University, Department of Systems Biology, Magdeburg, Germany
Otto-von-Guericke University, Theory of Complex Networks, Magdeburg, Germany
1
2
Question: One of the most important extracellular mediators of inflammation and immunity is Interleukin 6 (IL-6). One
reason for the pleiotropic functions of IL-6 is the complex network of intracellular signalling pathways that are activated
by binding of IL-6 to its receptor complex consisting of IL-6Rα and gp130. However, only robust signal transmission allows
a cell to appropriately respond to extracellular mediators. Robust signal transmission is perturbed by e.g. varying protein
expression levels. In information theory information transmission through noisy channels such as signalling pathways can
be quantified by the calculation of the mutual information. Here, we aim to understand robustness in IL-6-induced Jak/STAT
signalling by combining multi-colour single cell analysis and computation of mutual information.
Methods: To determine mutual information in IL-6-induced signaling the distribution of IL-6Rα, gp130, and STAT3
expression as well as of IL-6-induced phosphorylation of STAT3 were analysed. Ba/F3 cells stably expressing IL-6Rα and
gp130 were stimulated with varying amounts of IL-6. Cells were stained with antibodies against IL-6Rα, gp130, STAT3, and
phosphorylated STAT3 and subsequently analysed by multi-colour flow cytometry. Additionally, DNA content and cell cycle
phase were determined. Based on the multiplexed single cell analysis mutual information between all possible interaction
partners is computed to gain a comprehensive map of information transmission within IL-6-induced signalling.
Results: IL-6 induces a dose-dependent phosphorylation of STAT3. Of note, the mutual information between the expression
of STAT3 and its IL-6-induced phosphorylation is low. This indicates, that the strength of STAT3 phosphorylation is robust
and independent of STAT3 expression, in contrast to what would be expected from a simple model of a phosphorylation cycle.
This robustness probably enables cells to reliably transmit information by different cytokine concentrations albeit varying
STAT3 concentrations.
Conclusion: Mutual information analysis helps to define robust structures within signalling pathways that enables cells to
cope with biochemical noise and varying expression levels.
56
PP 2-04
Refractory states imprinted in the NF- B system regulate encoding of temporal
inflammatory signals
Boddington, C.1, Adamson, A.1, Jackson, D.1, White, M.1, Paszek, P.1
University of Manchester, Manchester, Great Britain
1
Cells must rapidly decode changing environmental signals to make fate decisions and coordinate acute tissue-level responses.
Robust signal processing strategies must reproducibly discriminate between different cues and regulate appropriate gene
expression programs. Here we used time-lapse imaging to show dynamic NF- B system responses to pulsatile cytokine
stimulation. Single, or well-spaced pulses of TNFα (>100 min apart) gave a high probability of NF- B activation. However,
fewer cells responded to shorter pulse intervals (<100 min) suggesting a refractory state induced by initial stimulation. This
refractory state appeared to be imprinted in individual cells, but heterogeneous in the population. Mathematical modelling
predicted that it was governed through multiple cellular states or levels of signalling molecules related to the TNFa signal
transduction pathway. Such encoding enabled robust and reproducible single cell responses and maintained acute tissue-level
signalling. While unresponsive to TNFa, NF- B system was able to encode additional cytokine inputs. This suggested, that
refractory states in the NF- B system might enable robust discrimination of multiple temporal cues and thus substantially
increasing signal processing capabilities. We hypothesise that such utilization of refractory states imprinted in signalling
networks might be advantageous and common to other cellular systems.
PP 2-05
Wnt/Planar cell polarity signaling regulates commitment of intestinal stem cells to the
secretory lineage
Böttcher A.1, Aliluev A.1, Sterr M.1, Büttner M. 2, Sass S. 2, Irmler M. 3, Beckers J. 3, Theis F. 2, Lickert H.1
Helmholtz Zentrum München, IDR, Garching-Hochbrück, Germany
Helmholtz Zentrum München, ICB, Neuherberg, Germany
3
Helmholtz Zentrum München, IEG, Neuherberg, Germany
1
2
Introduction Imbalance in intestinal stem cell (ISC) homeostasis leads to cancer as well as inflammatory and metabolic
diseases. Thus, defining ISC hierarchy and identifying niche signals that control stem cell fate will provide cellular
and molecular targets for therapy. Several ISC populations have been described according to their marker expression,
proliferative behavior and lineage potential. In addition, also differentiated intestinal cell lineages possess stem cell
character under specific conditions. The classical stem cell pool of actively cycling Lgr5+ ISCs ensures homeostatic renewal
and depends on canonical Wnt signaling. A minor population of quiescent, label-retaining cells (LRCs) is commonly viewed
as a reserve stem cell pool but is controversial, in part because we lack markers to properly define this cell population. Recent
studies identify quiescent LRCs as a subpopulation of Lgr5+ ISCs committed to the secretory fate that can regain stem cell
properties following crypt damage. However, the signals that maintain and activate quiescent stem cells, their relationship
to the classical Lgr5+ ISC pool and their contribution to homeostatic renewal remain elusive.
Material and Methods To define ISC hierarchy and to understand early intestinal lineage decisions we combine mouse
genetics with longitudinal single cell imaging and single cell gene expression analysis.
Results Here we establish the Wnt/planar cell polarity (PCP) signaling associated gene Flattop (Fltp) as novel marker for
intestinal LRCs and their distinct secretory lineage, enteroendocrine cells and Paneth cells. We find that two out of 15 Lgr5+
ISCs per small intestinal crypt express Fltp and become lineage restricted. A subset of Lgr5+/Fltp+ cells gives rise to Fltp
Venus reporter-positive cells that are characterized by a combined stem cell and secretory cell gene signature, label-retention
(quiescence), mainly locate at crypt position +4, indicative for quiescent intestinal cells and preferentially give rise to Paneth
cells. Strikingly, the expression of Wnt/PCP genes rather than a canonical Wnt gene signature distinctive for actively cycling
stem cells characterizes Fltp+ cells. In mice with disturbed Wnt/PCP signaling enteroendocrine lineage formation is affected.
Taken together, these findings define Fltp as a marker for intestinal LRCs and connect Wnt/PCP signaling to commitment of
ISCs to the enteroendocrine and Paneth cell lineage and cell-cycle exit.
Conclusion We provide a novel marker to study quiescent intestinal cells and their lineage in homeostatic and diseased
state. Furthermore, manipulation of Wnt/PCP signaling could potentially be of interest for the treatment of metabolic
diseases or cancer.
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PP 2-06
T cell immune responses generate diversity through linear cell-fate progression
Flossdorf, M.1, Buchholz, V. 2, Kretschmer, L. 2, Graef, P. 2, Busch, D. 2, Höfer, T.1
German Cancer Research Center, Theoretical Systems Biology, Heidelberg, Germany
Technische Universität München, Munich, Germany
1
2
Upon infection, naive antigen-specific cytotoxic T cells expand vigorously and give rise to a population of short-lived effector
and long-lived memory cells. Conflicting models have been proposed that suggest either of these subsets to be a precursor
of the other or attribute their generation to asymmetrically dividing naive cells. To gain insight into the mechanism
that underlies T cell diversification we combine stochastic population modeling with large scale model discrimination
based on single cell in vivo fate mapping data. We developed a computational framework that efficiently incorporates
data on single cell dynamics in addition to population mean dynamics. This resulted in significant improvements in both
model discrimination and identifiability. Our framework allows for stochastic differentiation and proliferation decisions
of individual cells and incorporates both symmetric and asymmetric cell division. Building on this, we find, first, that
asymmetric cell divisions of the activated naive T cells play a negligible role and, second, that phenotypic diversity is
instead generated through linear cell-fate progression: Naive cytotoxic T cells give rise to slowly proliferating, long-lived
subsets from which rapidly proliferating, short-lived subsets emerge. Critical predictions of this linear differentiation model
have been validated in subsequent experiments. Third, we find that recall responses initiated by resting memory T cells
recapitulate the primary response. Finally, the mathematical model was utilized to quantify the distinct effects of curtailed
antigen presentation and inflammation on memory versus effector T cell development.
PP 2-07
Estimating single-cell regulatory heterogeneities from cell populations
Fuchs, C.1, Bajikar, S. S. 2, Janes, K. A. 2, Theis, F. J.1
1
2
Helmholtz Zentrum München, Institute of Computational Biology, Neuherberg, Germany
University of Virginia, Department of Biomedical Engineering, Charlottesville, United States
Cell-to-cell variation in gene regulation occurs in a number of biological contexts,
such as development and cancer. Discovering regulatory heterogeneities in an
unbiased manner is difficult owing to the population averaging that is required for
most global molecular methods. Here, we show that we can infer single-cell
regulatory states by mathematically deconvolving global measurements taken as
averages from small groups of cells. This averaging-and-deconvolution approach
allows us to quantify single-cell regulatory heterogeneities while avoiding the
measurement noise of global single-cell techniques. Our method is particularly
relevant to solid tissues, where single-cell dissociation and molecular profiling is
especially problematic.
PP 2-08
Universal time and diffusion pseudotime for single-cell data
Haghverdi, L.1, Buettner, M.1, Wolf, A.1, Buettner, F.1, Theis, F. J.1
1
Helmholtz Zentrum Muenchen, ICB, Munich, Germany
Single-cell gene expression technologies divide into two main categories; time-lapse microscopy and single-cell snapshot
expression data. In the first approach expression of a few genes (usually up to three) is measured in actual time for several
single cell trajectories. The latter approach measures the expression of several genes (up to 10^4) for several single cells (up
to 10^5) sampled at one or few time points but from several developmental stages of the cellular dynamics. We argue that
there exists a common manifold for several single cell trajectories following the same dynamics in the gene expression space.
Defining universal time (and diffusion pseudotime) as the arc length along this common manifold (on a diffusion space map
of the manifold), we established a new framework to bring measurements from these two experimental frameworks together.
We illustrate the method on a toy data set as well as large samples of single cell qPCR and RNAseq / dropseq.
58
PP 2-09
Dynamical Modeling of Clonal Evolution in Primary and Relapsed Follicular Lymphoma
Horn, M.1, Kreuz, M.1, Loeffler, M.1
University of Leipzig, Institute for Medical Informatics, Statistics & Epidemiology (IMISE), Leipzig, Germany
1
Introduction: Germinal centers (GCs) provide signals for B cells to become high affinity antigen-detecting cells. In this
process, somatic hypermutation (SHM) and clonal selection play a pivotal role, resulting in the accumulation of mutations in
IG genes. Malignant transformation of GCs to follicular lymphoma (FL) is associated with genetic lesions. The disease can be
controlled but not cured, with a median survival of approximately 10 years. In the majority of patients relapses are observed,
in some cases additionally associated with transformation to more aggressive diffuse large B-cell lymphoma.
Objective: In order to gain insight into processes underlying clonal evolution and lymphoma relapse, we applied a
mathematical modeling approach. We particularly sought to explain qualitatively different types of lymphoma evolution, as
observed in FL patients, such as divergent, sequential and no evolution.
Materials and Methods: We developed a single-cell based mathematical model of physiological GC reaction to study
the dynamics of GC expansion and B cell affinity maturation. Furthermore, we applied our model to the situation of FL.
We compared our model results to phylogenetic trees reconstructed from clinical measurements (sequences of IGHV
rearrangements) of primary and relapse tumor in FL patients [1].
Results: Based on parameter changes in a single cell, representing malignant transformation, the model is capable of
reproducing typical features of lymphoma emergence and development. Specifically, different types of evolution can be
explained. We identified distinct model scenarios for the “no evolution” case that predict either total absence of SHM in FL or
the existence of extra-follicular niche-like environments in which primary FL cells lie dormant for extended periods of time
before entering a secondary GC. For sequential and divergent evolution, the model predicts the SHM machinery to work at a
significantly reduced rate during the period until relapse as compared to normal GC.
Conclusion: Our model is capable of reproducing both physiological GC reaction and transformation to FL. Different
scenarios to explain lymphoma evolution need to be validated experimentally. Our findings also have potential clinical
implications on e.g. immune therapeutic approaches.
Acknowledgement: We thank Prof Ralf Küppers, University Hospital Essen, for many valuable suggestions. Data have been
provided by the HaematoSys Consortium.
[1] Loeffler M et al. Genomic and epigenomic co-evolution in follicular lymphomas. Leukemia. 29(2):456-63 (2015).
PP 2-10
An Interplay of Determinism and Stochasticity in the Information Flow via STAT3
Huang, X.1, Braun, S.1, Harder, N.1, Vanlier, J.1, Alessandro, L.1, Meichsner, J.1, Börner, K.1, Van der Hoeven, F.1, Grimm D.1, Rohr, K.1, Schilling, M.1
1
DKFZ, Heidelberg, Germany
Interleukin 6 (IL-6) induced activation of Signal Transducer and Activator of Transcription 3 (STAT3) is an important signal
transduction process in liver regeneration and inflammation. Binding of IL-6 to its receptor initiates a cascade of events
leading to the accumulation of STAT3 in nuclei that modulates expression of target genes. It is unknown how differences
in IL-6 dose are encoded in the alteration of the dynamics of nuclear STAT3. To enable the measurements of the STAT3
dynamics in primary mouse hepatocytes, an mKate2-STAT3 knock-in reporter mouse model was generated. Hepatocytes
were stimulated with 12 different doses of IL-6 ranging from 0 to 500 ng/ml and STAT3 dynamics were recorded with time
lapse microscopy. Single nucleus tracks were obtained by an automatic image analysis pipeline and statistical modelling was
performed. Statistical analysis indicated that the amplitude of the first pulse of nuclear STAT3 encodes the most information
about IL-6 dose. Timing of secondary pulses in nuclear STAT3 presented a broad distribution, suggesting that subsequent
pulses are less regulated. The dynamics of nuclear STAT3 in two nuclei from one hepatocyte (twin nuclei) were significantly
more similar than those in two random nuclei, suggesting that the cytoplasm constitutes a major source of the variability
in nuclear STAT3 dynamics. Twin nuclei lose synchrony over time, indicating an increased role of stochasticity at later time
points. Signal transduction from IL-6 to STAT3 is a process subjected to both deterministic regulation by IL-6 dose and
influence by stochasticity in the signalling pathway.
59
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PP 2-11
Dynamic NF- B and E2F interactions control the priority and timing of inflammatory
signalling and cell proliferation
Jones, N.1, Ankers, J. 2, Awais, R. 2, Boyd, J.1, Ryan, S. 2, Adamson, A.1, Harper, C.1, Bridge, L. 3, Spiller, D.1, Jackson, D.1, Paszek, P.1, See, V. 2, White, M.1
University of Manchester, FLS, Manchester, Great Britain
University of Liverpool, Institute of Integrated Biology, Liverpool, Great Britain
3
University of Swansea, Mathematics, Swansea, Great Britain
1
2
One of the most important functions in a cell is the accurate interpretation of environmental signals. This is achieved via
dynamic signal transduction systems, through which gene expression is re-programmed in response to specific extracellular
cues. How cells resolve multiple simultaneous cues without perturbing essential functions is a growing area of importance.
Using physiological gene expression through a BAC expression system, we demonstrate that the dynamics of Nuclear
Factor kappa B (NF- B) signalling and the cell cycle are prioritised differently depending on the timing of an inflammatory
signal. Furthermore Using a combination of computational analyses, and advanced imaging (FRET, FCS) techniques we
show physical and functional interactions between NF- B and the E2 Factor 1 (E2F-1) and E2 Factor 4 (E2F-4) cell cycle
regulators. We demonstrate that these interactions modulate the NF-kB response and the timing of Mitosis. Treatment at
the G1/S boundary resulted in a delayed cell cycle and synchronous NF- B responses between cells. In S-phase, the NF-kB
response was delayed or repressed while cell cycle was unimpeded. We believe this is an example of how signaling systems are
integrated to prioritise important cell fate decisions.
PP 2-12
Inferring cell ensemble models of heterogeneous cell populations by multi-experiment and
multi-data-type fitting
Kallenberger, S.1, Fröhlich, F. 2, Theis, F. 2, Eils, R.1, Hasenauer, J. 2
1
2
DKFZ, Division of Theoretical Bioinformatics, Heidelberg, Germany
Helmholtz Center Munich, Institute of Computational Biology, Munich, Germany
Main objectives: In conventional mathematical models of cellular signal transduction networks cell-to-cell variability
remains mostly unattended. Live cell imaging together with the increasing number of fluorescence based molecular sensors,
however, allows quantitative studies in a variety of biochemical processes at the single-cell level. Therefore, techniques
for modeling heterogeneous cell populations are of growing importance. We developed methods to effectively combine
time-resolved single cell, flow cytometry and western blot data together with information on cellular events for parameter
estimations in a cell ensemble model.
Materials and methods: To estimate means and covariances of single cell parameters, we implemented a sigma-point based
method for the approximation of mean and variance of the population behavior. In a cell ensemble model, experimental
subsets of cells can be included which were exposed to different treatments but can be assumed to have the same covariances
and means of kinetic parameters and initial protein concentrations. To synchronize parameter means and variances across
multiple experiments, we introduced stochastically motivated penalization terms.
Results: We applied our approach to a model of extrinsic apoptosis. This model describes the activation of initiator
caspase enzymes and cleavage of the pro-apoptotic substrate BID to tBID, which irreversibly triggers cell death if a certain
threshold concentration is exceeded. Using our approach, we could assess the distribution parameters of tBID thresholds.
This facilitates quantitative studies of the effects of chemotherapeutics on tBID threshold distributions. The simultaneous
analysis of multiple datasets along with appropriate regularization improved the identifiability of single-cell parameter
60
distributions in cell ensemble models. Finally, we characterized the contribution of subsets of experimental data on the
identifiability of model parameters.
Conclusion: Our novel toolkit enables an efficient and robust inference of cell ensemble models from multiple data types
collected under a variety of conditions. The tools are widely applicable and the results can for instance be used to plan, which
combination of experimental techniques is required to render model parameters identifiable.
PP 2-13
Identifying mediators of disease co-morbidities by integrating omics data.
Kastenmüller, G.1, Zierer, J.1, Spector, T.1, Menni, C.1
1
Institute of Bioinformatics and Systems Biology, München, Germany
Many common diseases share genetic and environmental risk factors and thus occur together. Age is one of the strongest
risk factors for these diseases, hence facilitating comorbidities. In this study we aim to integrate molecular markers of ageing
from four different ‘omics’ datasets with clinical phenotypes to identify mechanisms that underlie age-related diseases and
cause comorbidities amongst them.
To this end, we analysed data from 510 female participants of the TwinsUK study with epigenomics, transcriptomics,
glycomics and metabolomics data available. We selected in total 53 markers from these four datasets that were previously
reported to be independently correlated with chronological age with high statistical significance. We combined these
molecular markers with 92 clinical phenotypes, reflecting amongst others renal function, lung function, body composition
as well as environmental factors such as food intake. We inferred a graphical model from the combined dataset using random
forest variable importance to assess multivariate dependencies between variables and stability selection to select significant
associations while controlling the FWER, as previously described by Meinshausen & Bühlmann (2010).
Our model of ageing related markers and phenotypes consists of 316 edges connecting the 145 nodes, which is considerably
sparser than the number of pairwise significant correlations (83.5%). Most of the nodes are connected in one giant
component (96 nodes), which in advance consists of seven dense modules that represent different physiological functions,
such as renal or liver function. Our model recovers several likely causal relationships of molecular markers with phenotypes such as the association of PDE4 with lung function - that were previously studied in model organisms and RCTs. Additionally,
our model suggests that IgG glycosylation mediates the co-morbidity of lung disease with renal disease. We further observe
that renal disease co-occurs with obesity due to the mediation by the metabolite urate. With a betweenness centrality of
6% the expression of the hormone oxytocin is another central variable in our model. It mediates the association of body
composition with inflammatory IgG glycosylation, suggesting a possible mechanism that promotes chronic low-grade
inflammation due to obesity.
Our model shows that systems biology methods can help to uncover complex relationships between diseases and molecular
markers from observational data while reducing the number of mediated associations. This allows to focus on fewer,
potentially causal relationships and might thus facilitate drug discovery and reduce adverse drug effects.
PP 2-14
Unraveling signaling dynamic patterns with single cell mass cytometry
Kumar, S.1, Lun, X. 2, Bodenmiller, B. 2, Rodriguez Martinez, M.1, Koeppl, H. 3
IBM, Rueschlikon, Switzerland
University of Zurich, Zurich, Switzerland
3
Technische Universität Darmstadt , Darmstadt, Germany
1
2
Extracellular cues trigger a cascade of information flow, in which signaling molecules gain new functions that ultimately
culminate in a phenotypic cellular response. Cells can control this flow of information by controlling the temporal behavior
of their signaling molecules. Understanding the temporal dynamics of these responses requires however collecting and
analyzing high-quality time series data, while taking into account the individual cell molecular heterogeneity that can alter
responsiveness of signaling networks.
Recent developments in single cell technologies enable a deep characterization of individual cell responses to external
stimuli. For proteins, mass cytometry enables measurement of over 50 proteins and protein modifications in millions of
cells. However, unlike experimental approaches that follow single cells over time, mass cytometry destroys cells in each
experiment and thus the reconstruction of temporal patterns requires specialized computational approaches.
The graphical lasso is an algorithm for learning the conditional independence structure of a graphical model that has become
very popular for the reconstruction of undirected graphs. Several available implementations facilitate fast and efficient
model reconstructions. However the graphical lasso assumes time-independent data, and thus it cannot be directly applied to
infer signaling dynamics patterns from time-series perturbation data.
Here we present a generalization of the graphical lasso method to account for structured variations over time. Our
61
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Abstracts
methodology assumes that the topological structure of the network remains unchanged through the perturbation
experiment, and thus a temporal penalty term that forces the model to change smoothly over consecutive time points can be
imposed. Model parameters are selected by implementing a variant of the Bayesian information criterion.
We have tested our model against the graphical lasso in an in silico study and have found that our method outperforms the
graphical lasso in the problem of inferring networks from noisy, biologically smooth data. We have also applied our model
to the analysis of single-cell time series stimulation experiments of the EGFR pathway in breast cancer. We have found that
our model recapitulates known interactions of the EGFR pathway and characterizes the dynamical evolution of the signaling
response, thus providing an efficient and scalable approach to reconstruct the temporal dynamics of signaling responses.
PP 2-15
Too Young to Die: Age Structured Population Models Capture Cell Cycle Dependent
Apoptosis from Snapshot Data
Kuritz, K.1, Müller, F.1, Pollak, N. 2, Allgöwer, F.1
University of Stuttgart, Institute for Systems Theory and Automatic Control, Stuttgart, Germany
University of Stuttgart, Institute of Cell Biology and Immunology, Stuttgart, Germany
1
2
Cyclic processes, in particular the cell cycle, are of great importance in cell biology. Continued improvement in cell
population analysis methods like fluorescence microscopy, flow cytometry, CyTOF or single-cell omics made mathematical
methods based on ergodic principles a powerful tool in studying these processes. Ergodic analysis methods (EA) require the
cell population to be in a quasi-steady state, meaning that the proportion of cells in each stage of the cell cycle is stable. This
restricts the application of cell cycle analysis with EA to unperturbed cell populations where no cell cycle arrest or cell death
occurs. A simultaneously exploration of intracellular signaling and related phenotypes has not been not possible.
By relating EA with age structured population models we overcome this restriction and provide a framework to study cell
populations that deviate from their steady state distribution. We demonstrate the capability of this approach in a study of
cell cycle dependent susceptibility to TRAIL induced apoptotic cell death.
Single cell progression through the cell cycle is therefore modeled by a stochastic differential equation on a one dimensional
manifold in the high dimensional dataspace of cell cycle markers. Given the assumption that the initial cell population is in a
steady state, we derive transformation rules which transform the number density on the manifold to the steady state number
density of age structured population models. This transformation is applied to a cell population under TRAIL treatment to
study cell cycle dependent caspase cleavage. Furthermore, the changing age structure of the cell population is analyzed with
age structured population models to estimate a cell cycle dependent death rate.
The estimated death rate demonstrates a higher susceptibility to TRAIL induced apoptosis for cells which are at the end of
the G1 phase. This observation is in line with the observed cell cycle dependent initial caspase cleavage.
Ergodic analysis can in general be applied to every process that exhibits a steady state distribution. By combining EA with
structured population models we provide the theoretic framework for extensions of ergodic principles to distributions that
deviate from their steady state. Applications range from studying cell cycle related processes to stem cell development.
PP 2-16
GenSSI: Generating Series Structural Identifiability on new Matlab versions
Ligon, T.1, Chis, O.- T. 2, Banga, J. 3, Balsa-Canto, E. 3, Fröhlich, F.4,5, Hasenauer, J.4,5
Ludwig-Maximilians-Universität, Physik, München, Germany
Institute, Santiago de Compostela, Spain
3
Spanish National Research Council, Vigo, Spain
4
Helmholtz Zentrum, Neuhergberg, Germany
5
Technische Universität München, München, Germany
1
2
Objectives: Ordinary differential equation (ODE) models are widely used to study biochemical reaction networks, but their
parameters are often unknown and have to be inferred from experimental data. Structural identifiability analysis tools
make it possible to determine which parameters can in principle be uniquely inferred from data, given the dynamics, and the
observation and perturbation functions. This analysis can also help to adapt either the model or the experiment for those
cases where unique estimation is not possible. However, for most realistic models, these methods are computationally very
demanding.
Methods: In this study, we consider GenSSI (Generating Series for testing Structural Identifiability), which expands
observables in series with respect to time and inputs in such a way that the series coefficients correspond to the successive
directional Lie derivatives of the observation function. GenSSI determines global and local structural identifiability of
parameters of non-linear ODE models, including those frequently used in systems biology. We have modified GenSSI to use
the current version of the Matlab Symbolic Math Toolbox instead of the Maple Toolbox for Matlab. In addition, the code was
modified to improve performance, readability, and maintainability.
62
Results: The new version of GenSSI was evaluated using a series of small- and medium-scale ODE models and compared with
the results of three alternative tools,EAR, DAISY and COMBOS. The comparison revealed thatEAR and GenSSI work best,
but EAR is significantly faster. However, GenSSI provides much more information, distinguishing between local and global
identifiability, and shows information about relationships among parameters.
Conclusion: The updated version of GenSSI runs on the current MATLAB version (R2015B) and is faster than previous
versions (30%). Improved documentation and code are available via GitHub (https://github.com/thomassligon/GenSSI)
facilitating its application and further development.
PP 2-17
Mechanistic modeling of subpopulation structures for multivariate single-cell data
Loos, C.1, Moeller, K. 2, Hucho, T. 2, Hasenauer, J.1
1
2
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
University Hospital of Cologne, Department of Anesthesiology and Intensive Care Medicine, Experimental Anesthesiology and Pain Research, Cologne, Germany
Main objectives of the study: Cell populations exhibit different degrees of heterogeneity. A better understanding of
cellular heterogeneity could advance treatments in a broad range of diseases, since heterogeneity plays an important role
for cells ranging from cancer cells to neurons. Accordingly, a holistic understanding of cellular mechanisms requires the
investigation of heterogeneity and its sources in the analysis of single-cell snapshot data. For this, models need to capture
the subpopulation structures of a cell population and enable the detection of mechanistic differences between the individual
subpopulations.
Methods: We combine mixture modeling and mechanistic modeling of higher-order moments, which facilitates the
detection of differences between subpopulations and experimental conditions and the exploitation of cell-to-cell variability
within individual subpopulations.
Results: We studied the alteration of subpopulation response by different experimental conditions for multivariate singlecell snapshot data of NGF-induced Erk signaling, a process relevant in pain sensitization. We found that our introduced
method provides an improved parameter identifiability and therefore a deeper insight into the mechanisms of the underlying
biological system.
Conclusion: Our results suggest that our method is a promising tool for the analysis of single-cell data, which might help to
achieve a better understanding of heterogeneous cell populations.
PP 2-18
Single cell clone transcriptomics derived from murine brown adipose tissue discloses cell
type heterogeneity of brown preadipocytes
Lutter, D.1,2, Israel, A. 2,3, Ussar, S. 2,3
Institute for Diabetes and Obesity, Helmholtz Zentrum München, Garching, Germany
German Center for Diabetes Research (DZD), München-Neuherberg, Germany
3
JRG Adipocytes and Metabolism, Institute for Diabetes and Obesity, Helmholtz Diabetes Center at Helmholtz Zentrum München, München, Germany
1
2
Activation of energy dissipating human brown adipose (BAT) is considered as an attractive therapeutic approach conquering
the current epidemic of obesity and its associated morbidities, above all type 2 diabetes. Pharmacological targeting of
human BAT is challenged by the the lack of established human BAT cell lines and differences between murine and human
BAT. Adult human BAT is a heterogeneous mixture of different adipocyte subtypes, whereas murine BAT is considered as
a homogenous fat depot derived from a single precursor. Differentiation of immortalized murine brown preadipocytes,
however, suggests heterogeneity also in murine brown adipocytes. To further explore if the morphological and functional
differences of the differentiated adipocytes are based upon a heterogenous population of precursor cells, we established 24
clonal cell lines from this immortalized brown adipocyte precursor pool. All clones showed a principle ability to differentiate
in vitro, quantified by lipid accumulation. However, we observed differences in the amounts of lipids accumulated as well
as the proliferation rates of the precursor cells. Full transcriptome sequencing of these cell lines showed an unexpected
high variation in gene expression patterns further underscroing the hypothesis of different adipocyte sub-types. We used
laplacian eigenmaps, a nonlinear embedding technique, to discover a low-dimensional projection that reveals eight genecluster, each characterized by an individual gene expression pattern. Differential gene expression of the associated genes
revealed a spectrum of overlapping cellular states. Functional analysis of the genes identified distinct biological processes
and pathways associated with the individual clusters. Beside identifying cells in different cell cycle states or with activated
immune response pathways, we show that different cells activate different metabolic pathways and developmental programs.
This unexpected heterogeneity suggests that mouse brown adipocytes, similar to human brown adipocytes, split up into
several functional different subtypes defined by distinct precursor populations.
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Abstracts
PP 2-19
Heterogeneity of TLR4 signalling and robust pathogen sensing
Paszek P.1
University of Manchester, Faculty of Life Science, Manchester, Great Britain
1
Immune cells exist in our bodies to detect and kill pathogens, but their activation must be controlled to avoid harmful
effects. At the population level, activation of toll-like receptor signalling in macrophages, a key pathogen recognition system,
results in analogue signal encoding, where average effector response output is fine-tuned by the level of pathogenic signal.
However, using single cell transcriptomics and live cell imaging we found that individual cell responses exhibit extreme
variability. For example, inflammasome-related IL-1α and IL-1β cytokine shown binary expression patterns such that it
is either high or not observed in individual cells. While the response of these cytokines appear random and uncorrelated
across the population, a second set of effector signalling molecules display ubiquitous highly correlated expression patterns.
These represent distinct regulatory mechanisms of signal integration and generation of cellular heterogeneity via specific
signalling networks. Quantitative smFISH analyses suggested that individual cells might have limited capacity to accurately
encode different levels of pathogenic inputs. It has often been thought that dynamic biological systems may have evolved to
maximize robustness through cell-to-cell coordination and homogeneity. We hypothesize that cellular heterogeneity is an
inherent design motif of the inflammatory response that may avoid out-of-control activation and better pathogen sensing in
cellular populations.
PP 2-20
Cell-to-cell heterogeneity unraveled by computational analysis of single-cell mass
cytometry data: cell cycle patterns and trajectories
Rapsomaniki, M. A.1, Lun, X. 2, Bodenmiller, B. 2, Rodriguez Martinez, M.1
1
2
IBM Research, Zurich, Rüschlikon, Switzerland
University of Zurich, Zurich, Switzerland
Advanced single-cell approaches like mass cytometry (CyTOF) allow the simultaneous quantification of dozens of proteins
at a single-cell level and, with the use of multiplexing techniques, are able to yield high-dimensional data from a variety of
different experimental conditions. When used in cancer research, CyTOF data enable us to study the effect of the inhibition
or activation of essential pathways, to characterize inter- and intra-tumor heterogeneity, or to identify cells subpopulations
associated with different stages of tumor progression. However, as already demonstrated for the case of single-cell RNAseq
data, single-cell technologies are largely influenced by confounding factors, with the cell cycle and the cell volume being
the most dominant ones. For instance, unsupervised clustering of mixed populations of single cells reveals that single cells
cluster preferentially according to their cell cycle stage instead of their signaling status or cell line.
Here, we present a computational approach to account for this hidden source of variability and unravel subpopulations
characterized by different signaling fingerprints. By exploiting measurements of 4 established cell cycle markers (IdU, pHH3,
Cyclin B1 and pRB), our method initially classifies single cells into discrete cell cycle phases through a prediction step based
on decision trees. Then, using a novel trajectory reconstruction technique, information of the 4 markers and the predicted
classes is combined to create a new one-dimensional multiplexed continuous marker that represents biological pseudotime.
Evaluation of the multiplexed marker values at a single cell level allows the ordering of individual cells on a continuum
based on their cell cycle progression. Last, we show how single-cell CyTOF data can be corrected for volume differences using
housekeeping gene measurements, and how cell cycle trajectories across different perturbation time points can be used to
remove the effect of the cell cycle and reveal signaling patterns. Our proposed method, implemented in a simple and intuitive
Graphical User Interface, was tested using CyTOF data from different cell lines (HEK 293T, HCC1937 and THP1) and
different types of stimulation (TNF-α and IFN-γ).
In summary, our approach allows the deconvolution of cell-cycle and cell volume effects from any type of mass cytometry
data, enabling a more accurate classification of cellular populations and analysis of biological processes.
64
PP 2-21
Multiplexed Imaging Cytometry Analysis Toolbox (miCAT) Coupled to Imaging Mass
Cytometry (IMC) Reveals Patterns of Cell Interactions Amongst the Heterogeneity of Breast
Cancer
Schapiro, D.1,2, Jackson, H. 2, Raghuraman, S. 2, Zanotelli, V.1,2, Catena, R. 2, Bodenmiller, B. 2
1
2
Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zürich, Switzerland
University of Zurich, Institute of Molecular Life Sciences, Zürich, Switzerland
Background: Current cancer research focuses on the hallmarks of cancer, such as tumor promoting inflammation, escape
from immune destruction, and sustained proliferative signaling. Central to many hallmarks is a deregulation of cell-to-cell
communication between tumor cells and cells of the tumor microenvironment (TME). An in-depth analysis of the TME
requires spatially resolved and highly multiplexed analysis of cell phenotypes and states at the single cell level. Imaging
Mass Cytometry (IMC) enables such analyses by imaging of up to 120 proteins and phosphorylation sites simultaneously
with sub-cellular resolution. To exploit single cells and their TME for patient stratification, classification and potential
biomarker discovery hundreds or even thousands of patient samples need to be measured by IMC and subsequently analyzed
by computational tools.
Methods: To process, segment, normalize and analyze these (large scale) multiplexed imaging cytometry data, we developed
a novel open-source modular, multiplexed imaging cytometry analysis toolbox (miCAT). This user-friendly Matlab based
software enables explorative data visualization, analysis modules to study cell types and their marker expression, and
several semi- and unsupervised methods for neighborhood analysis within the TME. Finally all of these image cytometry
features can be linked with the corresponding clinical data.
Results: We used miCAT to process hundreds of IMC images. Our main data set consist of ~200 breast cancer samples
including more than 200,000 single cells each with over 50 parameters and dozens of spatial features. The data driven
modeling modules implemented in miCAT highlighted a surprising intra- and interpatient TME heterogeneity in all breast
cancer subtypes. Furthermore, marker expression, cell types and features of the TME correlated with clinical outcomes.
Finally, we found a link between infiltrating CD68+ macrophages and metastatic carcinoma cells based on the expression of
markers of the epithelial mesenchymal transition. Validation experiments are currently ongoing.
Conclusion: Highly multiplexed imaging approaches, including IMC and serial immunofluorescence imaging, coupled to our
computational toolbox are a powerful tool to analyze highly multiplexed single cell tissue data which can be linked to cell
phenotypes, morphological features and clinical data to identify stratifying subpopulations and driving forces of metastasis
and disease outcome.
PP 2-22
Inferring gene regulation using pseudotemporal ordering of single cell snapshots
Wolf, F. A.1, Küffner, R.1, Theis, F. J.1
1
Helmholtz Munich, Neuherberg, Germany
Question: Can we qualitatively improve the inference of gene regulatory networks (GRNs) when using single-cell instead of
bulk snapshots of the transcriptome?
Methods: We use a recently proposed diffusion map-based algorithm for pseudotemporal ordering of single-cell snapshots
and combine it with different methods for causality analysis of time series. Pseudotemporal ordering refers to solving the
“cell ordering problem”, that is to infer - from static snapshot data alone - how cells progress through a certain dynamic
biological processes. This allows to reveal hidden dynamic information about the system.
Results: We show from first principles that exploiting pseudotemporal ordering allows to substantially exceed the predictive
power of state of the art inference algorithms of GRNs. The method obtains a directed GRN, is efficient and scalable, and
uses a clearly defined and practically relevant definition of causality. We illustrate the power of the method for simulated and
experimental data.
Conclusions: The method can be directly applied to single-cell transcriptomic data for developmental processes and we
expect it to be widely useful in this case. We will investigate in the future how it can be applied to experiments based on
perturbations.
65
Program /
Abstracts
PP 2-23
Resolving molecular networks and dynamics involved in CD8+ T-cells function and
differentiation in acute and chronic infections on a single-cell level
Kanev, K.1, Roelli, P.1, Zehn, D.1
Technical University of Munich, Division of Animal Physiology and Immunology, Freising, Germany
1
CD8 T-cells are major players in the adaptive immune defence against intracellular pathogens (including viruses and
intracellular bacteria) as well as in tumor surveillance. In the context of acute viral infections like influenza and yellow
fever, naive antigen-specific CD8 T-cells differentiate into highly functional effector and memory CD8 T-cells. In result,
the generated effector cells swiftly eliminate the viral infections, while the memory cells provide effective protection in
case of secondary infection caused by the same virus. In contrast, some viruses like HIV, HBV and HCV in humans as well
as LCMV clone 13 in mice are able to establish chronic infections associated with the development of CD8+ T-cells with
diminished functional activity, a state often referred as T-cell “exhaustion”. The latter represents a unique state of CD8 T-cell
differentiation which is likely to be effective but hyporesponsive, providing the host with the ability to control the viral load
without causing severe bystander pathology. Otherwise the excess cytotoxic T-cell activity due to persisting antigen presence
can bring the host to even more life-threatening condition compared to chronic viral presence. Some of the general CD8
T-cell hallmarks in different chronic infections compared to acute infection are unique transcriptional program, immune
signaling, migration as well as metabolism. Despite the amount of accumulated knowledge about CD8 T-cell phenotype
and function in chronic infection, we are still far away from establishing successful strategies for therapeutic reactivation
of the immune system in order to eradicate established chronic infections. In our opinion, this might be a reflection of the
usually applied population-based assessment of CD8 T-cell gene expression profiles, which is prone to generating biased
and even false results due to giving only average values. The main question we want to address is which molecular networks
and mechanism are involved CD8 T-cell function and differentiation in acute and chronic infections on a single cell level.
To address that question we decided to generate gene expression profiles of CD8 T-cells from different acute and chronic
infection settings and time points using single-cell mRNA sequencing approach. So far we are optimizing the method and we
have started to obtain single-cell gene expression profiles from different settings. Besides the deep single-cell information on
molecular networks and mechanism, we hope that the single-cell approach we apply will be informative concerning cellular
dynamics and subpopulation taking place in different acute and chronic infections. Finally, the ultimate outcome will be to
exploit that knowledge for generating new therapeutic strategies for successful intervention in immune response against
problematic viral infections.
PP 2-25
Dimensionality Reduction to Networks: Single Cell Sign Consistency Models Enable
Identification of Subpopulations with Distinct Signaling Network States in Mammalian
Cancer Cells
Tritschler, S.1, Schapiro, D.1, Saez-Rodriguez, J. 2, Bodenmiller, B.1,
2
2
University of Zurich, Institute of Molecular Life Sciences, Zürich, Switzerland
RWTH University, Joint Research Centre for Computational Biomedicine Hospital, Aachen, Germany
Background: Alterations in signaling pathways underlie many hallmark capabilities of cancer cells. Due to the high
connectivity of the signaling networks a systems-based approach is needed to unravel how deregulated signaling relates to a
cancer phenotype. This requires highly multiplexed analysis of the activity of signaling components. In addition, to be able to
differentiate cancer cell subtypes in heterogeneous tumor samples we need a single-cell resolution. Integration of such highdimensional single cell data for comprehensive visualization and modeling of subpopulations and their signaling network
states necessitates the development of computational tools.
Methods: Sign consistency models can be used to predict signaling network states from phosphoproteomic data. The key
idea is to map experimental data onto a signaling network based on literature (prior knowledge network) using rules on
signal propagation. We used an established sign consistency modeling tool (IGGY) and developed a novel framework for
single cell multiplexed data. The user-friendly Matlab based toolbox integrates automated data processing and discretization,
sign consistency modeling (IGGY), single cell network visualization and clustering algorithms. 500 cells per minute with up
to 40 simultaneous measured markers can be modeled on a local computer.
Results: Our approach was evaluated with synthetic and real data to show robustness and sensitivity. It was then employed
to a mass cytometry data set of epidermal growth factor stimulation experiments in human embryonic kidney cells using
36 markers simultaneously. The data set includes overexpression experiments of mutant signaling proteins known to be
relevant in cancer. The hypothesis-driven modeling revealed rare and abundant subpopulations with differences in the
activity of major signaling pathways. These distinct network states could be linked to cell cycle and other cellular phenotypes.
Validation experiments are currently ongoing.
Conclusion: Our framework enables the visualization of multiplexed single cell data in the context of major signaling
66
networks and detects rare subpopulations with distinct network states. The approach reduces dimensionality to networks yet
conserves single-cell resolution, which allows to browse large scale signaling data, dissect cell-to-cell variability and thereby
generate new hypotheses and improve experimental design.
PP 3: Signalling Modelling
PP 3-01
Predicting T-helper cell differentiation and plasticity using logical modeling and modelchecking
Abou-Jaoudé, W.1, Grandclaudon, M. 2, Monteiro, P. T. 3, Chaouiya, C.4, Soumelis, V. 2, Thieffry, D.1
Institut de Biologie de l’Ecole Normale Supérieure (IBENS), Paris, France
Institut Curie, Paris, France
3
INESC, Lisbon, Portugal
4
Instituto Gulbenkian de Ciência, Oeiras, Portugal
1
2
Background: T helper (CD4+) lymphocytes play a key role in the regulation of immune responses. Upon presentation of
antigens by antigen presenting cells, naive CD4+ T cells differentiate into various T helper (Th) subsets, which secrete
distinct sets of cytokines. This differentiation process requires the integration of multiple signals triggering the T cell
receptor, co-stimulatory and cytokine receptors. Diverse combinations of these signals lead to the differentiation of naïve
Th cells into diverse subsets, including Th1, Th2, Treg and Th17 subtypes. During the last years, experimental studies
highlighted the diversity and plasticity of Th lymphocytes, challenging the classical linear view of Th differentiation and
raising new questions regarding the mechanisms which underlie the observed diversity and plasticity of Th subsets.
Methods: We investigated the mechanisms underlying this diversity and plasticity using dynamical modeling. In particular,
logical modeling has proven suitable for the dynamical analysis of large signaling and transcriptional regulatory networks.
We have built a comprehensive logical model of the network governing Th differentiation, which integrates 20 signalling
pathways, a dozen transcription factors, and about 30 cytokines secreted by Th cells. To decipher the dynamics of such a large
model, we combined a formal reduction method preserving salient dynamical properties with an algorithm enabling the
identification of all stable states. Finally we applied model checking tools to get further insights into reachability properties
between Th subtypes upon changes of environmental cues and thereby assess the plasticity of Th lineages.
Results: We have assessed the consistency of our model by comparing its behaviour with published experimental
observations on Th differentiation. Our model qualitatively reproduces the polarisation of naïve Th cells into various
canonical subsets (Th0, Th1, Th2, Th17, Treg, Th9, Th22, Tfh) for the corresponding conditions. Moreover, our model predicts
the existence of various hybrid Th cells, which express combinations of transcription factors and cytokines associated
with distinct canonical subsets. Finally, we used our model to analyse the plasticity of canonical subtypes depending on
environmental conditions, as well as to delineate specific Th cell reprogramming strategies. This analysis led to the prediction
of many Th-subtype conversions, which still need to be assessed experimentally.
PP 3-02
Protein abundance of AKT and ERK pathway components governs cell-type-specific
regulation of proliferation
Adlung, L.1, Kar, S. 2,3,4, Wagner, M.- C.1, She, B.1, Bao, J. 5, Lattermann, S.1, Boerries, M. 5,6,7, Busch, H. 5,6,7, Timmer, J. 8, Schilling, M.1, Höfer, T. 2,3,
Klingmüller, U.1
German Cancer Research Center (DKFZ), Systems Biology of Signal Transduction, Heidelberg, Germany
German Cancer Research Center (DKFZ), Theoretical Systems Biology, Heidelberg, Germany
3
University of Heidelberg, BioQuant Center, Heidelberg, Germany
4
Indian Institute of Technology, Department of Chemistry, Mumbai, India
5
IMMZ, ALU, Systems Biology of the Cellular Microenvironment Group, Freiburg, Germany
6
German Cancer Consortium (DKTK), Freiburg, Germany
7
German Cancer Research Center (DKFZ), Heidelberg, Germany
8
Center for Biological Signaling Studies (BIOSS), Institute of Physics, Freiburg, Germany
1
2
Both, the PI3K/AKT pathway and the Ras/MEK/ERK pathway, have been implicated in the control of cell proliferation in
many different cell types. The key components of these pathways are highly conserved, which suggests that they form generic
proliferation-control modules that function in a specialized, cell-type-specific way. However, the integrated regulation of
proliferation as a multistep process, involving signal processing, cell growth and cell-cycle progression, is poorly understood.
In this study, we wanted to identify the main determinants of cell-type-specific proliferation control to rationalize targeted
intervention strategies such as combined inhibitor treatment.
By means of quantitative data generation and mathematical modeling, we investigated signaling dynamics, gene expression
profiles and proliferation responses to Erythropoietin (Epo) stimulation of primary murine erythroid progenitor cells and a
67
Program /
Abstracts
lymphoid cell line.
We found that cell-type-specific protein abundance patterns cause differential signal flow along the AKT and the ERK
pathway. These signaling dynamics can be explained by assuming global, cell-type-independent enzymatic rate constants
that are shared between the cell types and protein abundances of signaling components that are cell-type-specific. With
measured protein abundances and the estimated enzymatic rate constants, the mathematical model correctly predicted
signaling dynamics of a myeloid cell line that has not been used for model calibration.
We were able to link the differentially activated AKT and ERK pathways to downstream regulation of cell growth by S6 and
the expression of cell cycle genes. Control of proliferation was exerted mainly by rate-limiting protein synthesis for faster
cycling cells while slower cell proliferation was controlled at the level of the G1-S transition.
With the integrated mathematical model of Epo-induced proliferation we were able to explain cell-type-specific effects of
targeted AKT and ERK inhibitors and faithfully predict their combined effects. Our findings suggest that static protein
abundance patterns allow inference of signaling and proliferation dynamics for targeted intervention in the case of
individualized cancer therapy.
PP 3-03
A SYSTEM BASED TIME SERIES ANALYSIS UNRAVELS PROLIFERATION
TO DIFFERENTIATION SWITCH IN ERYTHROID PROGENITORS CELLS UPON
ERYTHROPOIETIN STIMULATION
Andrieux, G.1, Chakraborty, S. 2, Adlung, L. 2, Schilling, M. 2, Klingmuller, U. 2, Busch, H. 3,1,4, Borries, M. 3,1,4
IMMZ, Systems Biology of the Cellular Microenvironment Group, Freiburg, Germany
DKFZ, Division of Systems Biology of Signal Transduction, Heidelberg, Germany
3
DKFZ, Heidelberg, Germany
4
DKTK, German Cancer Consortium, Freiburg, Germany
1
2
Main objective(s): Erythropoietin (Epo) is the main hormone controlling the renewal of red blood cells. Following Eporeceptor binding on colony-forming unit-erythroid (CFU-E), proliferation and differentiation phases are involved in an
intricate manner. Although early response pathways, such as JAK-STAT or MAPK are well known, the overall understanding
of the switch between proliferation and differentiation is still in pursuit. Therefore we investigated the cellular proliferation
to differentiation switch after Epo stimulation based on time-resolved transcriptome and proteome data.
Materials and Methods: To this aim, we quantified the gene response and protein abundance using DNA microarrays and
Super-SILAC mass spectrometry up to 24 and 5 hours, respectively. Assuming a constant translation rate, we predicted and
validated the proteome at late time points and employed a mutual information based algorithm (ARACNE) to reconstruct a
regulatory network between transcription factors and Epo-regulated genes based on the predicted gene-proteome data.
Results: From the transcription factor network we identified a time-sequential regulatory pattern, wherein proliferation
and erythropoiesis related regulators switch their time-sequential activity at around 8 hours after Epo stimulation. Early
response genes and transcription factors, such as Cdk4/Cdk9 and Stat5a, are involved in pro-cell cycle processes, whereas late
response genes and TFs, e.g. Tfrc/HBB-b2 and Gata1, correlate with erythropoiesis.
Conclusion: For the first time we accurately identified the timing of the proliferation to differentiation switch based on high
throughput kinetic data. We therefore isolated the main genes regulated in CFU-E upon Epo stimulation and inferred the
regulatory network.
PP 3-04
The sensitivity of oscillatory properties
Baum, K.1, Politi, A. 2, Kofahl, B.1, Steuer, R. 3, Wolf, J.1
MDC Berlin, Berlin, Germany
EMBL , Heidelberg, Germany
3
Humboldt University, Institute of Theoretical Biology, Berlin, Germany
1
2
Oscillations occur at many different levels of biologic processes, in genetic systems, signaling and as metabolic oscillations.
Examples are circadian oscillations, the canonical NF- B-pathway, and calcium signaling. The oscillations differ in particular
in the intensity of their response towards environmental changes, the so-called robustness or sensitivity of the particular
response. For example, the period of calcium oscillations is known to be very sensitive while the period of circadian rhythms
is very robust.
We determine which topological principles render the amplitude or period of oscillatory systems robust by performing
sensitivity analyses for chain models as prototype oscillators. In order to avoid bias towards the employed kinetic parameter
values, we consider many different parameter sets chosen in an approach based on Monte-Carlo random sampling for the
analysis. We examine model properties such as type of feedback, mass conservation relations between species and reaction
kinetics and deduce structural principles leading to robust oscillatory properties. We additionally perform sensitivity
68
analyses for published mathematical models of calcium oscillations and circadian oscillations for a variety of different
parameter sets and explain their period robustness behavior with the help of the structural principles examined for the
prototype oscillator models.
PP 3-05
Mathematical Modelling Suggests Differential Impact of β-TrCP Paralogues on Wnt/βCatenin Signalling Dynamics
Benary, U.1, Kofahl, B.1, Hecht, A. 2, Wolf, J.1
1
2
Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
Albert-Ludwigs-Universität, Freiburg, Germany
The Wnt/β-catenin signalling pathway is involved in the regulation of a multitude of cellular processes by controlling
the concentration of the transcriptional regulator β-catenin. Proteasomal degradation of β-catenin is mediated by two
β-transducin repeat-containing protein (β-TrCP) paralogues, homologous to Slimb protein (HOS) and F-box/WD repeatcontaining protein 1A (FWD1), which are functionally interchangeable and thereby considered to function redundantly in
the pathway. HOS and FWD1 are both regulated by Wnt/β-catenin signalling, albeit in opposite directions, thus establishing
interlocked negative and positive feedback loops. The functional relevance of the opposite regulation of HOS and FWD1 by
Wnt/β-catenin signalling in conjunction with their redundant activities in proteasomal degradation of β-catenin remains
unresolved.
Using a detailed ordinary differential equation model, we investigated the specific influence of each individual feedback
mechanism and their combination on Wnt/β-catenin signal transduction under wild-type and cancerous conditions. We
found that, under wild-type conditions, the signalling dynamics are predominantly affected by the HOS feedback as a
result of a higher concentration of HOS than FWD1. Transcriptional up-regulation of FWD1 reduced the impact of the HOS
feedback. Due to the opposite regulation of HOS and FWD1 expression by Wnt/β-catenin signalling, the FWD1 feedback may
function as a compensation mechanism against aberrant pathway activation that is caused by reduced HOS expression. By
contrast, the FWD1 feedback provides no protection against aberrant activation in adenomatous polyposis coli (APC) protein
mutant cancer cells.
FEBS J. 2015 Mar;282(6):1080-96.
PP 3-06
Bringing Systems Biology to Material Science: Construction of an Information Processing
Material
Engesser, R.1,2, Beyer, H. 2,3, Weber, W. 2,3, Timmer, J.1,2
University of Freiburg, Institute of Physics, Freiburg, Germany
University of Freiburg, BIOSS Center for Biological Signalling Studies, Freiburg, Germany
3
University of Freiburg, Faculty of Biology, Freiburg, Germany
1
2
The rapidly emerging field of synthetic biology is based on utilizing design principles from electrical engineering in biological
systems. Synthetic biological networks are usually modularly constructed using well-characterized biological building
blocks, like switches, sensors or output modules and applying network topologies derived from engineering disciplines. The
characterization of these building blocks is done by mathematical modeling approaches like used in systems biology. In this
work we devise and exemplify a design concept of how such computational circuits can be transferred to materials sciences.
An information-processing material is synthesized by merging materials sciences with synthetic biology and mathematical
modeling methods arising from systems biology. We harness design principles and circuit topologies from electrical
engineering to functionally wire synthetic biological switches into a polymer material framework. This strategy allowed us
to synthesize polymer materials that perceive input signals, process these signals by fundamental computational operations
and produce a corresponding output. We exemplify this design concept by the mathematical model-guided synthesis of an
information-processing material that detects and counts light pulses and releases output molecules specific to the number
of light pulses detected. This system is characterized by a mathematical model which is used to identify optimal parameter
regions to obtain the desired systems output. The here-described concept of merging synthetic biology, mathematical
modeling and materials sciences according to design principles from electrical engineering provides the scope to synthesize
information processing materials for manifold applications in fundamental and applied research.
69
Program /
Abstracts
PP 3-07
Mathematical modelling of drug-induced receptor internalisation in HER2-positive breast
cancer cell-lines
Fehling-Kaschek, M.1, Kaschek, D.1, Peckys, D. 2, Reinz, E. 3, de Jonge, N. 2,4, Korf, U. 3, Timmer, J. 5,6,1
Freiburg University, Physics, Freiburg, Germany
Saarland University, Saarbrücken, Germany
3
German Cancer Research Center DKFZ, Division of Molecular Genome Analysis, Heidelberg, Germany
4
INM – Leibniz Institute for New Materials, Saarbrücken, Germany
5
Freiburg University, BIOSS Centre for Biological Signalling Studies, Freiburg, Germany
6
Freiburg University, Freiburg Center for Systems Biology (ZBSA), Freiburg, Germany
1
2
About 10-20% of breast cancer tumours over-express the HER2 receptor. Two drugs applied in medical treatment,
Trastuzumab and Pertuzumab, directly bind to the HER2 receptor inhibiting cell growth. The goal of this contribution is
to gain a better understanding of the mode of action of these antibodies by mathematical modelling based on ordinary
differential equations.
A central question for the modelling is the effect of drug-induced receptor internalisation. The internalisation of HER2
receptor in drug-treated cells is directly observed by fluorescence microscopy. Data taken for different treatments from the
human HER2-positive breast cancer cell-line SKBR3 is used as input to model the internalisation process.
A second model is deployed to investigate the drug-induced cell-signalling. It is based on time-resolved phosphorylation
data from the SKBR3 cell-line obtained by reverse phase protein arrays. The data reveals that drug-treatment interferes with
cell-signalling at different sites of the downstream pathway. Internalised receptors are a promising explanation for the sitespecific inputs, opening the perspective to finally combine the model of receptor trafficking and signalling.
PP 3-08
Simulation-based parameter estimation for kinetic data for the Raf-Mek-Erk pathway
Fiedler, A.1,2, Raeth, S. 3, Theis, F.1,2, Hausser, A. 3, Hasenauer, J.1,2
Technische Universität München, Mathematics, Garching b. München, Germany
Helmholtz Zentrum München, Institute of Computational Biology, Neuherberg, Germany
3
University of Stuttgart, Institute of Cell Biology and Immunology, Stuttgart, Germany
1
2
Introduction: The signalling pathway built around the Raf-Mek-Erk cascade is known to play a crucial part in many cell
processes. We want to study the explicit pathway structure in the context of cell cycle progression using mathematical
modelling.
Methods: To this end we calibrate candidate models with respect to time resolved dose response data and use model selection
to identify the most suitable model. As we have to ensure that the model is initially in steady state, we developed a tailored
simulation based optimization method that uses the problem structure.
Conclusion: In this study we derived a suitable model that can explain the data and performed the parameter analysis with
the newly developed simulation based method that promises good convergence results.
PP 3-09
Signal transduction analysis reveals key switches in inflammatory signaling during acute
myocardial infarction determining disease recurrence
Huber, H.1, Veltman, D.1, Laeremans, T.1, Martin, V.1, Janssens, S.1,2, Sinnaeve, P.1,2, Passante, E. 3
KU Leuven, Cardiovascular Sciences, Leuven, Belgium
University Hospital Leuven, Cardiolovascular medicine, Leuven, Belgium
3
University of Central Lancashire, School of Biomedical Sciences and Pharmacology, Preston, Great Britain
1
2
Inflammation is a key process during acute myocardial infarction (AMI), clearing cellular debris and initiating post-infarct
cardiac healing and scarring. However, uncontrolled and prolonged inflammation during the initial phase of cardiac repair
can lead to malignant remodeling of the cardiac intracellular matrix, cardiac dilation, and eventually disease recurrence
and death. Inflammasome-related signaling in leucocytes such as monocytes and neutrophils has been recently proposed as
crucial molecular process controlling cardiac inflammation during AMI. The process is initiated by a two stage stimulation by
cellular debris and by purine receptor activation, and culminates in the paracrine secretion of inflammatory cytokines IL-1β
and IL-18. These cytokines then activate further inflammatory cells in the heart to sustain a stable inflammatory response
and activate cardiac fibroblasts to initiate cardiac repair in the subsequent phase.
We here present an Ordinary Differential Equation-based model that investigates the control mechanisms of inflammasome
activation and regulation in monocytes during AMI. We feed this model with gene expression data obtained both from
peripheral blood mononuclear cells of patients from our in house clinics, and using data from gene expression omnibus. We
propose a nested feed-forward loop mechanisms that prevents premature and sub-threshold monocyte activation during
70
their recruitment according to recent molecular findings by other groups. We further investigate mechanisms that control
termination of inflammation by generating a bistable response through paracrine release of interferons in response to Il1β and IL-18. Interestingly, we found that a switch from type I to type II interferon occurs at the end of the inflammatory
phase of AMI, this vastly attenuates inflammasome activation, shifting the on-steady state equilibrium to subthresholdinflammation. We propose that failure of executing this shift in equilibrium leads to incomplete termination of the
inflammatory response, contributing to malignant cardiac remodeling and disease recurrence.
PP 3-10
Investigating and Modulating Apoptosis Sensitisation in Cultured Cardiac Cells after
Exposure to Doxorubicin Using Quantitative Biochemistry and Computational Systems
Biology
Passante, E.1, Huber, H. 2, Kankeu, C. 2, Clarke, K. 2
University of Central Lancashire, School of Pharmacy and Biomedical Sciences, Preston, Great Britain
KU Leuven, O&N I Herestraat 49 , Leuven, Belgium
1
2
Doxorubicin (DOX) is an anthracycline used in the treatment of many human cancer. However its clinical utility is restricted
due to several cardiac side effects. Indeed, 10 to 15 years after chemotherapy, more than 10% of patients treated with DOX
will develop severe cardiac side effects such as dilated cardiomyopathy and congestive heart failure.
Increase of apoptosis in mammalian cardiac cells following DOX exposure has been identified to induce a dilative
cardiomyopathy phenotype. We therefore perform quantitative biochemistry in combination with computational models
based on Ordinary Differential Equations to investigate if DOX-exposure sensitises cardiomyocytes to apoptosis.Firstly
we aim to show that progenitors of cardiomyocytes (cardiomyoblasts) gain protection to apoptosis during differentiation
and subsequently we will show that DOX pre-exposure ablates such resistance. We posit that this gain and loss of apoptosis
resistance during differentiation and DOX exposure can be best understood by analysing quantitative changes of apoptosis
protein levels using a cell culture model and studying the interplay of apoptosis proteins by computational systems models.
Our computer models will provide biochemically testable molecular hypotheses as to how DOX-induced apoptosis sensitivity
can be reverted by gene silencing and pharmacological interventions directed to modify/alter key player in the apoptotic
pathway.
PP 3-11
Linking mechanistic understanding of cellular signalling in gastric cancer and cellular
phenotypes
Hug, S.1, Keller, S. 2, Kneissl, J. 2, Luber, B. 2, Theis, F.1, Hasenauer, J.1
1
2
Helmholtz Zentrum München, Institute of Computational Biology, Neuherberg, Germany
Technische Universität München, Institute of Pathology, München, Germany
Background: Molecular factors play a significant role in determining the outcome in gastric cancer patients in response to
targeted therapy. It is known that the overexpression of several proteins, e.g. EGFR and HER2 are associated with tumour
growth and drug responsiveness, e.g. to the monoclonal anti-EGFR antibody Cetuximab. For predicting a specific outcome,
statistical and/or mechanistic modelling of molecular characteristics and cellular signalling, as well as an analysis of the
signalling-phenotype link have to be performed.
Method: For the analysis of cellular signalling, we set up a mechanistic differential equation based model of the EGFR
signalling pathway and calibrate it with data obtained for gastric cancer cell lines under different treatment conditions in
both known Cetuximab responder and non-responder cell lines. The model can simultaneously handle multiple cell types,
namely known responder and non-responder cell lines. The available Western blot datasets are then further analysed with
statistical methods to identify recurrent deregulation. Furthermore, we include phenotypic data for the two studied cell
lines, where motility, invasiveness and proliferation are measured. These phenotypic data are included into the mechanistic
model via a regression approach. To find significant influences, the respective regression coefficients are estimated using
Laplacian prior distributions, thus enforcing sparsity.
Results: Parameter estimation and model selection provide new insights into the impact of different mutations and
Cetuximab response. More specifically, a good fit to the complete measurement data can be achieved by including the MET
receptor tyrosine kinase as present in the non-responder cell line and acting as a way to bypass EGFR and induce downstream
signaling. Furthermore, preliminary results indicate that phenotypic output is most closely determined by the integral
activation of EGFR.
Conclusion: The link between a mechanistic cellular signalling model and analyses on the phenotypic level could lead to new
insights, such as the possible identification of a predictive biomarker for patient stratification.
71
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Abstracts
PP 3-12
Dynamic pathway modeling of IL-6 signaling to unravel mechanisms of Drug-Induced
Liver Injury
Jünger, A.1, Vanlier, J. 2, v.d. Stel, W. 1, Huang, X.1, Braun, S.1, Oppelt, A.1, Timmer, J. 2, Klingmüller, U.1
1
2
DKFZ, Systems Biology of Signal Transduction, Heidelberg, Germany
Freiburg University, Institute of Physics, Freiburg, Germany
Interleukin-6 (IL-6) is a key effector cytokine with a central role in liver regeneration and inflammatory processes. Recent
findings also link IL-6 signaling to the clinical condition of Drug-Induced Liver Injury (DILI), which is an adverse drug
reaction of a small population of individuals. DILI is the most frequent cause of acute liver failure and a common reason for
the withdrawal of pharmaceuticals from the market. Diclofenac, a regularly prescribed nonsteroidal anti-inflammatory drug,
is one of the ten most frequent causes of idiosyncratic DILI. Due to the high complexity of the signaling pathway a systems
biology approach will be necessary to unravel the possible contribution of diclofenac to DILI.
By applying quantitative immunoblotting and qRT-PCR we were able to demonstrate a synergistic drug-cytokine effect in the
human hepatocellular carcinoma cell line HepG2: In comparison to IL-6 alone, IL-6 and diclofenac in combination increase
the abundance of nuclear phosphorylated STAT3, a central signal transducer of the IL-6 pathway, as well as the expression of
downstream target genes such as SOCS3 and acute phase proteins. Intriguingly, STAT3 target genes show different patterns
of responsiveness to the combined drug-cytokine treatment.
By integrating quantitative data on pathway components and signaling dynamics, our previously established dynamic
pathway model of IL-6 signaling, based on mouse experiments, is continuously adapted to human hepatocellular carcinoma
cells. With this mathematical model we intend to elucidate and quantify the crosstalk of diclofenac-induced responses
on IL-6 signaling. By identifying the putative targets of the compound, an improved quantitative prediction of DILI and
therefore a better risk management will be possible.
PP 3-13
Auto-correlation of high-precision NFκB oscillation data for dynamic mean population
models of TNFα signaling
Kaschek, D.1, Oppelt, A. 2, Huppelschoten, S. 3, Klingmüller, U. 2, van de Water, B. 3, Timmer, J.1,4,5
University of Freiburg, Institute of Physics, Freiburg, Germany
German Cancer Research Center (DKFZ), Heidelberg, Germany
3
Leiden University, Academic Centre for Drug Research (LACDR), Leiden, Netherlands
4
University of Freiburg, BIOSS Centre for Biological Signalling Studies, Freiburg, Germany
5
University of Freiburg, Freiburg Center for Systems Biology (ZBSA), Freiburg, Germany
1
2
Dynamic mean population models constitute a frequently utilized model class in systems biology. Those models describe
cellular processes deterministically by ordinary differential equations (ODEs) and their predictions can be interpreted as the
mean behavior of a cell population. Some dynamic systems exhibit the property that variations in the model parameters, e.g.
due to biological variability, lead to qualitatively different predictions. The mean prediction is not a solution of the ODE any
more. Consequently, fitting the model to a hypothetical noise-less observation of the mean yields non-zero residuals that
might be highly correlated.
Here we discuss a TNFα model that is calibrated based on densely sampled, precise NF- B oscillation data and sparse
immunoblot data for targets like nuclear factor-kappaB inhibitor (IκBα), IκB kinase (IKK) and NF- B phosphorylation. We
develop a strategy to estimate the expected covariance structure and correlation length of residuals from repeated timecourse measurements. Judging from the covariance structure, we conclude that the combination of precise and imprecise
data leads to over-fitting of the NF- B oscillations. Conversely, the residual correlation can be employed to adjust the
weighting or sampling width of the time-series data.
Funding: This work was supported by funding from the MIP-DILI project, a European Community grant under the Innovative
Medicines Initiative (IMI) Programme (Grant Agreement number 115336).
72
PP 3-14
Case Study for Attractor Detection of Asynchronous Logical Networks
Klarner, H.1, Siebert, H.1
1
Freie Universität Berlin, Mathematik und Informatik, Berlin, Germany
In this presentation we first recapitulate a model for the bladder tumorigenisis in humans, which was introduced by E. Remy
et al. in [1], and then present new results obtained by applying our attractor-detection algorithm to it, a method published in
[2].
The bladder tumorigenisis model is based around the E2F-activating transcription factors and their response to EGFR
and FGFR3 stimuli as well as to DNA damage. Its construction includes a previously published model as well as literature
research, in particular the Reactome database and the Atlas of Cancer Signaling Networks. It predicts different phenotypes
by the asymptotic activities of three read-out nodes: Proliferation which is represented by the activities of CyclineE1 and
CyclinA, Apoptosis which is represented by TP53 and E2F1 and Growth Arrest which is represented by RB1, BL2 and p21CIP.
Mathematically, the model is a discrete, dynamical network that consists of 30 components and 84 interactions together
with respective logical statements that formalize processes like synthesis, degradation and phosphorylation. Such networks
are capable of producing two types of asymptotic (or long-term) behaviors: steady states, in which the activity levels of all
network components are kept at a fixed value, and cyclic attractors in which some components are unsteady and produce
sustained oscillations in the network. To associate possible phenotypes with a given model requires the computation of all
possible attractors, a computationally challenging problem. Recently we developed an 0-1 optimization problem to predict
the attractors of a network and a model-checking-based algorithm to assess the quality of such predictions. We observed that
in practice these predictions agree very well with the real attractors.
If time permits we will present some additional results regarding the decision process that must implicitly exist in nondeterministic models capable of producing several different attractors. The goal here is to try to understand the dynamics of
logical networkd in terms of two opposing regimes: the decision-making regime in which a subnetwork stabilizes its values
independently of the remaining network, and the percolation regime in which state transitions are predetermined and
caused by previous decisions. The whole process is depicted in an “attractor decision diagram” and we discuss its possible
value for answering questions regarding network control.
[1] Remy, E., Rebouissou, S., Chaouiya, C., Zinovyev, A., Radvanyi, F., and Calzone, L. (2015). A modeling approach to explain
mutually exclusive and co-occurring genetic alterations in bladder tumorigenesis. Cancer research, 75(19), 4042-4052.
[2] Klarner, H., and Siebert, H. (2015). Approximating attractors of Boolean networks by iterative CTL model checking.
Frontiers in bioengineering and biotechnology, 3.
PP 3-15
Towards genome-scale mechanistic models of signal transduction
Krantz, M.1
1
Humboldt-Universität zu Berlin, Berlin, Germany
We can build, validate and simulate genome scale metabolic models. In contrast, we still have not managed to create
comprehensive signal transduction models: Information transfer networks pose new technical and conceptual challenges,
which we never needed to tackle in metabolic modelling. Indeed, it is often difficult to directly apply the methods for
metabolic networks on signalling networks. In particular, validation and simulation methods based on mass conservation
cannot be used. Furthermore, when we model signal transduction network as mass transfer, we need to enumerate the state
combinations the network components can assume. This works well for small models, but rapidly leads to a virtual explosion
in model states - and/or simplifying assumptions. As neither is desirable in a knowledge database, we wanted to develop
a method to map and model signalling networks at the resolution of signalling data. With this in mind, we developed the
reaction-contingency (rxncon) language. It reconciles the need for mechanistic accuracy with the scalability required to
tackle large networks. Based on this language we developed a toolbox including generation and simulation of Boolean models
uniquely defined by the network. We integrated these methods to create the first workflow that enables reconstruction,
validation and gap-filling of signal transduction networks. These methods are analogous to those for metabolic networks,
but tailored for signal transduction and information flow. We are currently benchmarking these methods by developing large
scale regulatory networks in baker’s yeast. However, these methods are organism independent and could also be applied for
mammalian systems.
73
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Abstracts
PP 3-16
Therapeutic Response Modeling Towards Personalized Medicine
Li, J.1,2, Mansmann, U.1,2
1
2
Institute for medical informatics, biometry, and epidemiology, Medical Department, Munich, Germany
Deutsche Krebsforschungszentrum, Heidelberg, Germany
In order to be able to reflect diverse traits of the human tumorigenesis including sustained proliferation, evading growth
suppressors, resisting cell death and other malignancies, we construct a genome-scale molecular signaling map (MSM). By
integrating gene expression profiles into the MSM, we could understand why the proliferation of different types of cancer cell
lines varies by facing multiple drug treatments. For instance, mTor-, WNT- and Hedgehog pathways greatly contribute to the
sustained proliferation in overian cancer cells by the combination inhibition treatment of COX-2 with ERBB3, EGFR, FGFR,
PDGFR.
By investigating the overall survival of AML patients, we have applied the MSM to propose one of the major reasons of
resistance of FLT3 inhibition in AML could be the high activity of JAK-STAT signaling pathway combined with the high
expression of ERBB family members including EGF, BTC, TGF-alpha, and HB-EGF. Furthermore, the result of the application
of MSM also shows that AML patients with high expression of mir-10a, mir-196a and mir-155 tends to have low overall
survival when different phosphatases such as PTPN6 and PTPN11 are not expressed or have significant low expression in
patients.
Moreover, the MSM possesses the potential to identify the treatement-related biomarkers for individuals. Our current study
also identify key biomarker miRNAs inclduing mir-181b, mir-20a, mir-21, mir-31, mir-96, mir-224, mir-374 and mir-503
that might play major roles as oncogenes or tumor-suppressor contributing to tumorigenesis. In sum, we have collected
different study-daten including Cancer Genome Atlas and tested the MSM in a systematic way for its therapeutic potential.
The results are promising.
PP 3-17
Analysis of the TRAIL and cisplatin induced apoptosis and MAPK-PI3K/AKT signal
transduction pathways in melanoma with a probabilistic Boolean network approach
Lucarelli, P.1, del Mistro, G. 2, de Landtsheer, S.1, Trairatphisan, P.1, Kulms, D. 2, Sauter, T.1
University of Luxembourg, Systems Biology, Belvaux, Luxembourg
Center of Regenerative Therapies, Experimental Dermatology, Dresden, Germany
1
2
Despite remarkable scientific and clinical efforts in melanoma research, the incidence of malignant melanoma strongly
increased over the last decades. While metastatic melanoma is often characterized by mutation of the kinase BRAF, the large
number of different mutations represent the biggest challenge in our understanding of the disease. These mutations strongly
increase the aggressiveness of the tumor offering a poor prognosis for the patient. The response to conventional therapeutic
treatment usually lasts for a few months only, until drug resistance occurs, leading to tumor relapse which often coincides
with increased proliferation and migration rates.
The goal of this study is to identify the critical mechanisms leading to drug resistance in different melanoma cell lines. For
an in-depth analysis of signal transduction pathways that might confer therapy resistance of melanoma, we apply a systems
biology approach, which integrates steady-state time-course data into a mathematical model, to identify the optimal drug
combination for a successful treatment.
A mathematical model analyzing the crosstalk interactions in death ligand TRAIL- and cisplatin-induced apoptotic signal
transduction has been established and refined based on newly acquired experimental time-course data (series of steady
states). We employ a probabilistic Boolean network (PBN) modeling approach to analyze the molecular interactions and
regulations among signaling molecules with minimal parameterization. This modeling approach was applied to identify the
influence of targeted molecules via crosstalk interactions between signaling pathways inducing apoptosis or survival at a
quantitative scale. Furthermore, a sensitivity analysis was performed to identify the most sensitive molecular targets in the
network which render melanoma cells sensitive to apoptosis and thus allow the formulation of new hypothesis for alternative
treatment strategies.
Currently, we were able to dissect the critical crosstalk mechanisms of the TRAIL and cisplatin signal transduction pathways
by a combination of PBN modelling and sensitivity analysis. In addition, we were able to show the influence of the different
pathways for each steady-state.
In the next step, we aim to identify which drug combinations could be applied to render resistant melanoma cells sensitive
towards apoptotic cell death. The newly identified drug targets and combinations will consequently be validated in selected
melanoma in vitro models.
74
PP 3-18
Post-transcriptional regulation by sRNA in Synechocystis PCC 6803
Mader, W.1, Malenka, M.1, Georg, J. 2, Hess, W. 2, Timmer, J.1
University of Freiburg, Physics, Freiburg, Germany
University of Freiburg, Institute of Biology III, Freiburg, Germany
1
2
Non-coding small RNA (sRNA) are important regulators in prokaryotes, such as cyanobacteria. While mRNA is decoded
in the process of protein translation, sRNA enables quick regulation of gene expression. To this end, sRNA either binds to
mRNA and regulates translation, or binds to protein, modifying its function.
For the cyanobacterium Synechocystis PCC 6803, the sRNA IsaR1 has been found to regulate RNA expression depending
on the availability of iron[1]. Iron is necessary for fundamental mechanisms as respiration and photosynthesis. It is thus
essential in many living beings. We investigated the iron regulatory system of Synechocystis PCC 6803 in a network
containing the RNAs SufC and SufR and their proteins, as well as the two protein-iron complexes SufC-Fe and SufR-Fe, the
sRNA IsaR1 and its complex with SufC-RNA, IsaR1-SufC. Two mutants of Synechocystis PCC 6803 were created. In the first
mutant, the sRNA IsaR1 can not be expressed. In the second, the expression of IsaR1 can be triggered by copper pipetted to
the medium. The RNA of SufB, SufR, and IsaR1 was measured by micro-arrays, the protein of SufB and SufR was measured by
mass spectrometry. Data of the wildtype and the two respective mutants was recorded in an iron-depletion condition, and a
condition in which cooper was present in the medium.
Synechocystis and its mutants were modeled by differential equations. By maximum-likelihood estimation, we determined
relative rate-parameters in the model based on the recorded data. Non-identifiabilities in the models were resolved by model
reduction based on profile likelihoods.
[1] M. A. Hernandez-Prieto, V. Schön, J. Georg, L. Barriera, J. Varela, W. R. Hess, and M. E. Futschik. Iron deprivation in
Synechocystis: Inference of pathways, non-coding RNAs, and regulatory elements from comprehensive expression profiling. Genes,
Genomics, Genetics 2:1475-1495, 2012.
PP 3-19
A new logic of unified classification of intracellular processes
Vatlitsov D.1, Mintser O.1
1
Shupyk National Medical Academy of Postgraduate Education, Medical Informatics, Kyiv, Ukraine
Introduction: The basis of living systems is the direct passage of the certain reactions. The systematic study of reactions and
processes occurring in certain time at a certain organizational level forms the understanding of the life laws.
Therefore, the determination of levels of reactions and concept of creation the integrated classification of processes
organization provides the opportunity to study the each pool of reactions separately as integrated in the model that involved
the holistic approach process. It is impossible without regulation of relevant knowledge.
The aim of study: To formulate a new concept of integrative model of processes classification as a basis of systems biology
ontology model and conduction of researchs on intracellular processes regulation.
Methods: Were used a content analysis and collocate analysis also were used the data from the databases STITCH, STRING
and PANTHER.
Results: Were suggested a concept of integrative case organization model that initiate the triggers with 9 different levels
of interactions: quantum; ions; molecular; macromolecular; the genetic basis; supramolecular; structural elements of the
cell; macrostructural; intercellular. This was the base for understanding the principles of the certain processes compilation
in the pools. But the next phase of the present research had defined a problem of interlevels interactions classification. Thus
we had proposed universal identifiers for each processes even interlevels interaction. The first step of unified classification
is the understanding of the type of processes: automated (which runs without the involvement of intracellular energy) and
energydependend processes. Each type of process could be classified at scalable grade on the principle of the amount of
involving energy.
This classification based on the knowledge that each substance represented as the thermodynamic system at Standard
conditions which characterized by a certain margin of internal energy. It could be detect only the changes of internal energy
ΔU during the changing of matter in chemical reaction. And the changes of the internal energy involved in reaction at
constant volume is the internal energy of the reaction.
Conclussions: Was formulated the unified integrative model of living systems based on classification of processes that based
on the number and type of reactions’ internal energy and its hierarchical position in the levels of cases interactions for live
self-organized systems.
75
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Abstracts
PP 3-20
Dynamic pathway modelling of TNFα signalling to unravel mechanisms of Drug Induced
Liver Injury
Oppelt, A.1, Kaschek, D. 2, Huppelschoten, S. 3, Malkusch, S.4, Dörffler, V.4, Herrmann, F.1, Merkt, B. 2, Sison-Young, R. 5, Zhang, F. 5, van de Water, B. 3,
Heilemann, M.4 Goldring, C. E. 5 Timmer, J. 2, Klingmüller, U.1
DKFZ, Systems Biology of Signal Transduction, Heidelberg, Germany
University of Freiburg, Institute of Physics, Freiburg, Germany
3
Leiden University, Leiden Academic Centre for Drug Research (LACDR), Leiden, Netherlands
4
Johann Wolfgang Goethe-University, Institute of Physical and Theoretical Chemistry, Freiburg, Germany
5
University of Liverpool, MRC Centre for Drug Safety Science, Department of Molecular and Clinical Pharmacology, Liverpool, Great Britain
1
2
Deciphering drug induced liver injury (DILI) not only needs to consider toxic effects caused by a compound on hepatocytes,
but also the impact of synergistic drug-cytokine interactions. Studies on diclofenac, a drug ranked among the top 10 causes
of DILI, showed that the combined treatment with TNFα caused an increase in apoptosis in vitro. However, the mechanism of
how TNFα enhances diclofenac-induced toxic effects remains elusive. Dynamic pathway modelling is a powerful approach to
investigate these complex and multifactorial interactions.
Quantitative immunoblotting and live-cell imaging was performed in HepG2 cells and primary human hepatocytes upon
TNFα treatment and in combination with diclofenac. Subsequently, we developed and validated an integrated dynamic
pathway model based on ordinary differential equations (ODEs), which combines the densely sampled live-cell imaging data
with the rather sparse immunoblotting data.
Our studies reveal immediate, early effects of diclofenac on the TNFα-induced NF- B signalling pathway. Nuclear
translocation of NF- B is delayed and the phosphorylation levels of several NF- B signalling components are affected
upon the combined treatment of TNFα with diclofenac. Experimental results as well as modelling simulations indicate
that diclofenac not only has one single target but is rather affecting several components of the TNF receptor complex and
the NF- B signal transduction cascade. Dynamic pathway modelling is used to systematically approach these complex
processes leading to enhanced hepatotoxicity. Our aim is to improve the understanding of how drug toxicity interacts with
inflammatory signalling to eventually contribute to better test systems for DILI in humans.
PP 3-21
Autocrine TGF-beta/ZEB/microRNA-200 signal transduction drives epithelialmesenchymal transition: Kinetic models predict minimal drug dose to inhibit metastasis
Rateitschak, K.1, Kaderali, L.1, Wolkenhauer, O. 2, Jaster, R. 3
University Medicine Greifswald, Bioinformatics, Greifswald, Germany
University of Rostock, Systems Biology and Bioinformatics, Rostock, Germany
3
University Medical Center Rostock, Gastroenterology, Rostock, Germany
1
2
Question: The epithelial-mesenchymal transition (EMT) is the crucial step that cancer cells must pass before they can
undergo metastasis. We aim to develop mathematical models describing the complex changes in signal transduction driving
epithelial-mesenchymal transition and how signal transduction can be influenced by drugs that should inhibit metastasis.
Methods: In this work we develop four kinetic models that are based on experimental data and hypotheses describing how
autocrine transforming growth factor-beta (TGF-beta) signal transduction induces and maintains an EMT by upregulating
the TFs ZEB1 and ZEB2 which repress the expression of the miR-200b/c family members.
Results: After successful model calibration we validate our models by predicting requirements for the maintenance of the
mesenchymal steady state which agree with experimental data. Finally we apply our validated kinetic models for the design
of experiments in cancer therapy. We demonstrate how steady state properties of the kinetic models and data from tumorderived cell lines of individual patients can be combined to set up a calibration curve such that the minimal amount of an
inhibitor to induce a mesenchymal-epithelial transition (MET) can be determined.
Conclusions: We show that kinetic models are able to describe complex molecular biology experiments to study the EMT.
Our approach to predict the minimal amount of an inhibitor to induce a MET could be of significant interest for optimizing
treatment protocols in the clinics.
76
PP 3-22
Tailored Steady-State Constraints for Parameter Estimation in Non-linear Ordinary
Differential Equation Models
Rosenblatt, M.1, Timmer, J.1,2,3, Kaschek, D.1
University of Freiburg, Institute of Physics, Freiburg, Germany
BIOSS Centre for Biological Signalling Studies, Freiburg, Germany
3
Freiburg Center for Systems Biology (ZBSA), Freiburg, Germany
1
2
Signaling pathways and chemical reaction networks in systems biology are frequently modeled by ordinary differential
equations. Model parameters are often unknown and have to be estimated from experimental data, e.g. by maximumlikelihood estimation. To reduce the dimensionality of the parameter space, steady-state information is incorporated in the
parameter estimation process where possible.
For non-linear models, analytical steady-state calculation typically leads to higher-order polynomial equations for which
closed-form solutions are hard to obtain. Even if available, the solutions are not unique and several steady-state expressions
have to be considered. This can be circumvented by solving the steady-state equations for kinetic parameters which leads to a
linear equation system with a unique solution. When solved for kinetic parameters, however, steady-state constraints tend to
become negative for particular model specifications, thus, inducing optimization aborts due to divergent model trajectories.
Here, we present an algorithm based on graph theory that derives non-negative, analytical steady-state expressions by
stepwise removal of cyclic dependencies between dynamical variables. Our method is applicable to most common classes
of biochemical reaction networks comprising mass-action, Hill-type kinetic, inhibition and catalyzation. We show that our
approach is especially well-tailored to guarantee a high success rate for optimization.
PP 3-23
Dynamics of IL-6-induced classic and trans-signaling
Rummel, H.1, Billing, U.1, Christen, H.1, Schliemann-Bullinger, M. 2, Rudolph, N. 2, Bullinger, E. 2, Dittrich, A.1, Findeisen, R. 2, Schaper, F.1
Otto-von-Guericke-University Magdeburg, Department of Systems Biology, Magdeburg, Germany
Otto-von-Guericke-University Magdeburg, Institute for Automation Engineering, Magdeburg, Germany
1
2
Main objective of the study: Interleukin-6 (IL-6) is one of the most important inflammatory mediators and currently the
target of several therapeutic strategies for the treatment of inflammatory diseases. Two different signaling modes of IL-6
that differ in their biological outcome have been described. Classic signaling via the membrane bound IL-6 receptor alpha
(IL-6Rα) acts primarily anti-inflammatory whereas trans-signaling via the soluble IL-6Rα (sIL-6Rα) is responsible for the
pro-inflammatory activities. In the past, different strength and dynamics of classic- and trans-signaling have been proposed
to be responsible for these different physiological outcomes. Here, we test this hypothesis by analyzing possible differences in
the dynamics of signaling induced by classic and trans-signaling. Based on these experimental data a dynamic mathematical
model of IL-6-induced classic- and trans-signaling will be developed.
Materials and methods: Ba/F3 cells stably expressing glycoprotein 130 (gp130) either with or without IL-6Rα serve as a
model for classic and trans-signaling, respectively. Dynamics, dose-dependency and variation of classic- and trans-signalinginduced Jak/STAT- and MAPK-signaling were extensively analyzed by semi-quantitative western blotting and multi-color
flow cytometry. Additionally, the influence of multiple inhibitors of classic and trans-signaling-induced signal transduction
on cell growth was measured.
Results: In contrast to the general idea that IL-6-induced pro-inflammatory trans-signaling is stronger than antiinflammatory classic signaling we found that there are no differences in the strength and dynamics of IL-6-induced signaling
between classic and trans-signaling. Additionally, these results are supported by comparable inhibition of the two pathways
by thirteen different inhibitors of IL-6-induced Jak/STAT-, MAPK- and PI3K-signaling.
Conclusion: Our quantitative data indicates that there are in contrast to former hypotheses no differences in the strength
and dynamics of the two signaling modes of IL-6. Therefore, diverse physiological outcomes to IL-6-induced signaling are
probably not caused by different signaling dynamics, but more likely by different target cells of classic- and trans-signaling.
The results lay the basis for an in depth analysis and mathematical modeling.
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Abstracts
PP 3-24
Selective Control of Upregulated and Downregulated Genes by Temporal Patterns and
Doses of Insulin
Sano, T.1, Kawata, K.1, Ohno, S.1, Yugi, K.1, Kakuda, H.1 Kubota, H. 2, Uda, S. 2, Fujii, M.1, Kunida, K.1, Hoshino, D.1, Hatano, A.1, Ito, Y.1, Sato, M.1,
Suzuki, Y.1, Kuroda, S.1
The University of Tokyo , Tokyo, Japan
Kyushu University , Fukuoka, Japan
1
2
Insulin shows postprandial transient secretion with high doses, and fasting sustained secretion with low doses, selectively
controlling multiple functions. However, how temporal patterns and doses of insulin selectively control gene expression
remains unknown. Here, we analyzed the temporal patterns and doses of insulin-dependent gene expression. We performed
transcriptomic analysis of insulin-stimulated hepatoma FAO cells and identified 14 upregulated insulin-responsive genes
(IRGs) and 16 downregulated IRGs. The upregulated IRGs responded more quickly to pulse and ramp insulin stimulation,
whereas the downregulated IRGs showed higher sensitivity to insulin doses. Mathematical modeling revealed that signaling
from insulin to transcription of the downregulated IRGs is more rapid and sensitive to insulin stimulation, whereas
transcription of the upregulated IRGs is more rapid. Furthermore, some of the IRGs were consistently upregulated and
downregulated by insulin injection in vivo. Thus, transient high-dose insulin selectively regulates the upregulated IRGs,
whereas the sustained low-dose insulin regulates the downregulated IRGs.
Fig.1. Selective control of the insulin-responsive genes (IRGs) by temporal patterns of insulin.
PP 3-25
Sensing and antiviral signaling by RIG-I
Schweinoch, D.1, Clausznitzer, D. 2, Frankish, J. 3, Binder, M. 3, Kaderali, L.1
University Medicine Greifswald, Institute for Bioinformatics, Greifswald, Germany
Technische Universität Dresden, Institute for Medical Informatics and Biometry, Dresden, Germany
3
German Cancer Research Center (DKFZ), Heidelberg, Germany
1
2
The Retinoic acid inducible gene I (RIG-I) detects intracellular viral RNA (vRNA) and activates the antiviral innate immune
response via a mitochondrial signaling pathway. The RIG-I induced signaling cascade triggers the production of type 1
interferons and stimulates antiviral gene expression to interfere with virus infection. Despite intensive efforts, the dynamics
of signal transduction within the RIG-I signaling pathway and particularly its regulation are not yet fully understood.
Main Objectives: The aim of this study is to develop a mathematical model for the RIG-I pathway using ordinary differential
equations (ODEs), which quantitatively describes the innate immune signaling response to RNA virus infection. Thereby,
we address the hypothesis of vRNA length dependent activation of the RIG-I pathway and aim at providing insights into the
vRNA binding mechanism. Furthermore, we use measurements of the activation of downstream signaling molecules (i.e.
interferon regulatory factor 3, IRF3) to analyze the activation dynamics of the mitochondrial adapter protein MAVS, an
essential signaling adapter within the RIG-I pathway, which forms protease resistant prion-like aggregates.
Materials and methods: The final model will be based on a variety of experimental set-ups. Phosphorylation of
intermediate signaling proteins over time has been measured using western blotting techniques, and tracking of pIRF3-eGFP
transport into the nucleus has been performed to quantify the rate of transcription factor recruitment. In addition, binding
of RIG-I to vRNA of different lengths has been studied by measuring the ATP hydrolysis by the helicase domain of RIG-I
upon RNA binding. Moreover, luciferase reporter assays have been used to measure the gene expression induced by the RIG-I
pathway for varying vRNA concentrations and lengths.
78
Results & conclusion: Here we present a first model, which is able to reproduce experimental results. Thereby, two
independent mechanisms for the initial binding of RIG-I to the vRNA and sequential cooperative attachments of additional
RIG-I proteins were able to describe the vRNA binding experiments and are in good agreement with current knowledge.
However, additional work will be needed to quantitatively describe the signal transduction mechanisms involved in the RIG-I
pathway.
PP 3-26
CASPA: A tool for automating the reconstruction of PPI Sub-Networks
Sevimoglu, T.1, Arga K. Y.1
1
Marmara University, Bioengineering, Istanbul, Turkey
An important objective of systems biology is to enable quantitative prediction of the dynamics of biological pathways. This
can be done through the reconstruction of the network structure via the determination of protein-protein interactions (PPI).
Nonetheless the ever increasing amount of data provided by genomics and proteomics resources is hard to keep up with. It
becomes difficult to analyze and make sense of these data manually most of the time. Consequently the reconstruction of
PPI networks with the integration of gene annotation data to select the most appropriate candidate proteins that represent
a network is a great challenge. We present here CASPA: a computer aided program to automate Selective Permissibility
Algorithm. Our method automates the integration of PPI data with GO annotations to reconstruct a protein interaction
network, which is composed of candidate proteins for signal transduction mechanisms in humans and model organisms. The
modularity of the workflow allows further extension and additional selection criteria to be incorporated. We have developed
a simple user interface where the user provides the selection criterion. CASPA improved protein selection by eliminating
human error considering extensive amount of data and resulted in faster candidate protein selection.
PP 3-27
One model to rule them all
Steiert, B.1, Merkle, R. 2,3, Salopiata, F. 2,3, Depner, S. 2,3, Raue, A.1, Kreutz, C.1, Schelker, M.1, Hass, H.1, Wäsch, M. 2,3, Böhm, M. E. 2, Lehmann, W. D. 2,
Schilling, M. 2, Klingmüller, U. 2,3, Timmer, J.1
University of Freiburg, Institute of Physics, Freiburg, Germany
German Cancer Research Center (DKFZ), Division Systems Biology of Signal Transduction, Heidelberg, Germany
3
Member of the German Center for Lung Research (DZL), Translational Lung Research Center (TLRC), Heidelberg, Germany
1
2
A major goal in systems biology is to reveal potential drug targets for cancer therapy. Signaling pathways triggering cell-fate
decisions are often altered in cancer resulting in uncontrolled proliferation and tumor growth. However, addressing cancerspecific alterations experimentally by investigating each node in the signaling network one after the other is difficult or even
not possible at all. Here, we combined quantitative time-resolved data from different cell lines with non-linear modeling
under L1 regularization, which is capable of detecting cell-type specific parameters. To adapt the least-squares numerical
optimization routine to L1 regularization, sub-gradient strategies as well as truncation of proposed optimization steps were
implemented. Likelihood-ratio tests were used to determine the optimal penalization strength resulting in a sparse solution
in terms of a minimal number of cell-type specific parameters that is in agreement with the data. The uniqueness of the
solution was investigated using the profile likelihood. Based on the minimal set of cell-type specific parameters experiments
were designed for improving identifiability and to validate the model. The approach constitutes a general method to infer an
overarching model with a minimum number of individual parameters for the particular models.
79
Program /
Abstracts
PP 3-28
Systematic Analysis of Time-Resolved Transcriptional Signature of the Cross-Talk
Between HGF and IL6 Reveals Genetic Program of Hepatocyte Proliferation Control
Vlaic, S.1, Hoppe, A. 2, N. Müller, S. 3, D’Alessandro, L. A.4, Braun, S.4, Müller, S.4, Meyer, R.4, Bohl, S.4, Kondofersky, I. 3, Muckenthaler, M. U. 5, Gretz, N.6,
Theis, F. J. 3,7, Guthke, R.1, Holzhütter, H.- G. 2, Klingmüller, U.4, Boerries, M.4,8,9, Busch, H.4,8,9
Hans-Knöll-Institute (HKI) Jena, AG Systemsbiology and Bioinformatics, Jena, Germany
Charité University Medicine Berlin, Institute for Biochemistry, Berlin, Germany
3
Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt, Institute of Computational Biology, Neuherberg, Germany
4
German Cancer Research Center (DKFZ), Heidelberg, Germany
5
University of Heidelberg, Department of Pediatric Oncology, Hematology and Immunology, Molecular Medicine Partnership Unit, Heidelberg, Germany
6
University of Heidelberg, Medical Research Center, Medical Faculty Mannheim, Mannheim, Germany
7
Technische Universität München, Department of Mathematics, Garching, Germany
8
Albert-Ludwigs-University, Institute of Molecular Medicine and Cell Research, Freiburg, Germany
9
German Cancer Consortium (DKTK), Freiburg, Germany
1
2
Liver regeneration is characterized by a sequence of inter- and intra-cellular signaling events starting with an inflammatory
phase followed by rapid hepatocyte proliferation until the liver mass is restored. Interleukin 6 (IL6) and hepatocyte growth
factor (HGF) play a pivotal role during this process, enhancing and driving proliferation, respectively. While the individual
effects of HGF and IL6 have been studied comprehensively, their cross-talk in control of hepatic proliferation is yet largely
unknown.
Here, we performed time-resolved transcriptome profiling of primary murine hepatocytes stimulated individually or in
combination with HGF and IL6 to quantify and model the synergistic actions of these
factors on the gene response. A novel statistical approach based on general additive models and systematic gene response
categorization selected representative cross-talk genes for inference of a dynamic gene-regulatory network. The in silico
model reproduced the time-sequential transcriptional program in the primary hepatocytes initiated by the interplay of HGF
and IL6 and was confirmed by independent in vitro experiments as well as in vivo literature data.
The analysis highlighted the chemokine Cxcl10 as well as the dehydrogenase Hsd3b4 as putative regulatory hubs in the
temporal regulation of proliferation. While both genes are known to be involved in liver regeneration, our study refines their
roles within the temporal sequences of events in the early phases of liver regeneration.
In conclusion, we showed that a combinatorial stimulus elicits many non-trivial cellular gene responses, highlighting the
synergistic nature of gene response regulation. Our combined approach of gene response categorization and subsequent
modeling can be applied in general to understand the downstream pathway dynamics arising from combinatorial stimuli in
complex biological processes.
PP 3-29
Mathematical modeling of the impact of acetaminophen on the HGF-induced cellular
responses in primary mouse hepatocytes
Vlasov, A.1, Huang, X.1, Schilling, M.1, Klingmüller, U.1
German Cancer Research Center (DKFZ), Division of Systems Biology of Signal Transduction, Heidelberg, Germany
1
Acetaminophen (paracetamol, APAP) is a widely used analgesic compound. However, overdose with APAP can lead to the
development of drug-induced liver injury and thereby is one of the most frequent causes of acute liver failure. Up to date,
the hepatotoxic effect of APAP is believed to be primarily mediated by its secondary metabolites. However, the molecular
mechanisms underlying APAP- induced liver injury remain unresolved. Upon liver injury regenerative responses are activated
that involve hepatocyte growth factor (HGF) induced proliferation of hepatocytes and ensure tissue regeneration. We
hypothesize that perturbation of HGF-induced signal transduction by APAP could lead to impaired cellular responses and
enhanced liver damage.
To test this hypothesis we examined the direct effect of APAP on HGF-induced activation of the HGF-receptor Met, the
mitogen-activated protein kinase (MAPK) and the phosphatidylinositol 3 kinase (PI3K) signaling pathways and on cell cycle
progression in primary mouse hepatocytes. The analysis by quantitative immunoblotting revealed that the activation of
both signaling pathways in response to HGF stimulation was reduced upon APAP administration. Furthermore, the extent
of HGF-induced DNA synthesis in primary mouse hepatocytes was decreased by APAP in a dose-dependent manner. Live cell
imaging followed by single cell analysis of hepatocytes expressing the Fucci2 cell cycle sensor showed that APAP induced a
prolonged S/G2-phase, suggesting a G2 arrest.
A highly simplified mathematical model of HGF induced signaling based on ordinary differentiation equations (ODEs) was
established and calibrated with the obtained quantitative experimental data. This model is currently used to test multiple
hypotheses to identify potential intervention points of APAP in the HGF- induced signaling network. It is planned to extend
the model with a cell cycle module. Thus, this model will provide new mechanistic insights into regulatory mechanisms
controlling hepatocytes proliferation that are impaired during APAP-induced liver injury.
80
PP 3-30
Modeling signaling dynamics with differential equations: are single-cell data and their
analysis useful?
Wade, J.1,2, Bodenmiller, B. 2, Voit, E.1
Georgia Institute of Technology and Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, United States
University of Zurich, Institute of Molecular Life Sciences, Zurich, Switzerland
1
2
Main objective: Ordinary differential equations (ODEs) are often used to study dynamic cellular processes such as
signaling. Classically, experimental data and ODE simulations both represent a population average, yet population averages
have also been shown to obscure meaningful biological information. Single-cell experimental methods are continuously
increasing to address this issue, and the benefits have been well demonstrated. The purpose of this study is to investigate
potential advantages of using single-cell data to model signaling dynamics with ODEs.
Materials and methods: We use an ODE representation of a small illustrative signaling system with an artificially created
dataset, as well as a small biological dataset, to systematically compare modeling population-average- versus single-cell-type
data.
Results: Multidimensional single-cell data are empirical distributions and contain information on the relationships among
variables in cellular systems (e.g., through correlation). Fitting a model to selected characteristics of the marginal projections
of the distribution (or subsets of the distribution) ignores information on this relationship structure. By contrast, one
should expect that explicit consideration of the relationship structure improves model fitting. Here, we formally test this
expectation.
Conclusion: Single-cell measurements represent semi-independent observations of the feasible states of a cellular system.
Such information is largely encoded in the complex relationship structure among system variables. Our results show that
considering the relationship structure of cellular states revealed by multidimensional single-cell data can be used to improve
parameter inference in ODE models of signaling. This result holds independently of subpopulations in the data.
PP 4: Metabolism
PP 4-01
New Standard Resources for Systems Biology: BiGG Models and Visual Pathway Editing
with Escher
Dräger, A.1,2, King, Z. A. 2, Lu, J. S. 2, Ebrahim, A. 2, Sonnenschein, N. 3, Miller, P. C. 2, Lerman, J. A.4, Palsson, B. O. 2,5,6, Lewis, N. E.6
University of Tuebingen, Center for Bioinformatics Tuebingen (ZBIT), Tübingen, Germany
University of California, San Diego, Bioengineering, La Jolla, CA, USA, United States
3
Technical University of Denmark, Novo Nordisk Foundation Center for Biosustainability, Hørsholm, Denmark, Denmark
4
Total New Energies USA, Inc., Amyris, Inc., Emeryville, CA, USA, United States
5
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark, United States
6
University of California, San Diego, Pediatrics, La Jolla, CA, USA, United States
1
2
Background: Genome-scale metabolic network reconstructions enable the simulation and analysis of complex biological
networks, thus providing insights into how thousands of genes together influence cell phenotypes. Accuracy in systems
biology research requires standards in model construction, a variety of specific software tools, and access to high-quality
metabolic networks.
Results: To meet these needs, we present the BiGG Models database and a collection of software solutions for model
building, curation, visualization, and simulation. BiGG Models contains more than 75 high-quality manually-curated
genome-scale metabolic network reconstructions, which can be easily searched and browsed and include interactive pathway
map visualizations. These visualizations have been generated with the web-based Escher pathway builder. Escher allows
users to draw pathways in a semi-automated way and can visualize data related to genes or proteins that are associated to
pathways. An export function facilitates storing Escher maps in the community formats SBML and SBGN-ML. These features
make Escher an ideal interactive model development tool. In order to make all models in BiGG Models MIRIAM compliant,
BiGG Models itself has become part of the MIRIAM registry and provides links a plethora of external databases for each
model component. This rich annotation enables rapid comparison across models. New Systems Biology Ontology terms have
been defined that are used to better highlight the role of model components. A comprehensive web API for programmatically
accessing the database content enables interfacing with diverse modeling and analysis tools.
Conclusions: With these features and tools, BiGG Models provides a valuable database, structured for easy access and to
help improve the quality, standardization, and accessibility of all genome-scale models. The development of this resource has
boosted the development of community standards for constraint-based modeling.
Availability: http://bigg.ucsd.edu, https://escher.github.io
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Abstracts
PP 4-02
Combination of Mass Spectrometry-Based Proteomics and Mathematical Modelling
Predicts Therapy Targets of Liver Cancer
Erdem, M.1, Egners, A.1, Berndt, N. 2, Mastrobuoni, G. 3, Vvedenskaya, O. 3, Bielow, C. 3, Kempa, S. 3, Holzhütter, H.- G. 2, Cramer ,T.1
Uniklinikum Aachen, Molecular Tumor Biology, General Surgery, Aachen, Germany
Charité – Universitätsmedizin Berlin, Biochemistry, Berlin, Germany
3
Max-Delbrück-Center for Molecular Medicine, BIMSB, Berlin, Germany
1
2
Main objectives: Hepatocellular carcinoma (HCC) is becoming a major health problem in western societies due to constantly
rising incidence and loss of effective longterm treatment options. Using a combination of proteomics and mathematical
modeling we have performed a detailed analysis of the tumor-specific metabolism of HCC in a murine model system (ASV-B
mice).
Materials and methods: Mass spectrometry-assayed protein abundance values were incorporated into a mathematical
model of the central carbon metabolism (CCM) of primary hepatocytes that had been established earlier by the Holzhütter
group. This model is based on published kinetic parameters, external metabolite concentrations as well as hormonal (e.g.
insulin, glucagon) signaling and enables computational modeling of product generation of various CCM-associated metabolic
pathways.
Results: The model predicted significant changes of various metabolic pathways in murine HCCs. As expected, glucose uptake
and lactate production (the Warburg effect) were higher in tumors compared to benign hepatocytes. In addition, the model
predicted lower oxygen consumption and higher mitochondrial membrane potential of cancer cells, arguing for cancerassociated mitochondrial dysfunction. Finally, urea synthesis was predicted to be less effective in cancer cells. Blocking
glycolysis with D-glyceraldehyde or 3-bromo-pyruvate resulted in significant inhibition of tumor progression, a functional
validation of one model prediction. Furthermore, a significant proportion of mice succumbed to the feeding of a protein-rich
diet, validating the predicted loss of urea synthesis efficacy.
Conclusions: We present the applicability of mathematical modeling of dynamic metabolic pathways important for tumor
biology. Our aim is to model the metabolic response to specific perturbations (e.g. chemotherapy) in order to identify new
therapy targets and possibilities for effective combination therapies.
PP 4-03
Glucose is not the only source for lactate production by hepatocellular carcinoma cells
Haanstra, J.1, Tinneveld, T.1, Merino Tejero, E.1, Bruggeman, F.1, Teusink, B.1
Vrije Universiteit Amsterdam, Systems Bioinformatics, Amsterdam, Netherlands
1
Aberrant signalling and changes to metabolism are both hallmarks of cancer. We aim to understand the interplay between
growth factor signalling and metabolism in healthy and cancerous liver cells. For energy metabolism, a well-known
phenomenon in cancer is the Warburg effect: the finding that even in the presence of oxygen, lactate is the major end-product
of glucose breakdown instead of CO2 (aerobic glycolysis). Lactate production is often used as a measure for aerobic glycolysis.
However, even if virtually all glucose ends up in lactate, this does not exclude that other carbon sources like amino acids
contribute to lactate formation in cancer cells. Amino acids may be needed for biomass formation but could also fuel parts
of the tricarboxylic acid cycle and thereby contribute to ATP synthesis and lactate production. To understand how signalling
affects and possibly redirects metabolic fluxes we first need quantitative and time-resolved information on which parts of
the metabolic network of hepatocellular carcinoma cells are active under different nutrient conditions.
Main objective: Quantification of carbon metabolism in hepatocellular carcinoma cells in the presence and absence of
different carbon sources and foetal calf serum (FCS).
Materials and methods: We cultivated HepG2 hepatocellular carcinoma cells under different nutrient conditions and
followed growth by cell counting. We used HPLC to quantify glucose consumption from the medium and lactate production
into the medium during cultivation.
Results: Our time-resolved metabolite measurements in the culture medium revealed that HepG2 cells made more lactate
than can be explained from the glucose that was consumed. In the absence of glucose as a carbon source, growth of HepG2
cells was only moderately affected and lactate was still produced. Removing glutamine or FCS from the culture medium
affected growth more strongly but also under these conditions the glucose that was consumed could not account for all the
lactate that was produced. Interestingly, in all these conditions lactate production stopped after some time and was followed
by the consumption of lactate.
Conclusion: Part of the lactate that is produced by HepG2 cells comes from other sources than glucose, potentially from the
amino acids in the medium or FCS. Over time, lactate production stops and the lactate is consumed. Our results show that
lactate production alone is not a good measure of aerobic glycolysis in liver cancer cells.
82
PP 4-04
Metabolic profiling of CHO-K1 cells adapted to glutamine-free media
Hanscho, M.1,2, Ruckerbauer, D. E.1,2, Galleguillos, S.1,2, Borth, N.1,2, Zanghellini, J.1,2
1
2
Austrian Centre of Industrial Biotechnology, Wien, Austria
University of Natural Resources and Life Sciences, Department of Biotechnology, Vienna, Austria
Chinese hamster ovary (CHO) cells are preferential production hosts for the pharmaceutical industry due to their ability
to produce human like product glycosylation. In order to optimize cell growth and cell viability, growth media are typically
supplemented with glutamine. However, excess glutamine leads to high ammonium secretion, a toxic metabolite for CHO
cells.
Main objectives: Here we aim to analyze, model and understand the response of CHO cells to glutamine supplementation
versus glutamine free media based on a multi-omics characterization.
Methods: Batch fermentations using CHO-K1 cell lines adapted to grow in glutamine free and glutamine supplemented
media provided the samples for transcriptomics, proteomics, metabolomics analyses as well as metabolite concentration
profiles of culture supernatants at different time points. These data were analyzed based on the integration into a
comprehensive community driven genome scale metabolic reconstruction of CHO cells, called iCHO.
Results: iCHO is a highly curated community resource, which combines the knowledge of eight laboratories around
the world. Here we show that iCHO was able to correctly predict the growth-rates for the glutamine free and glutamine
containing media. These observations allowed us to gain direct insight into the variability of the energy and amino acid
metabolism in these differentially adapted CHO cells.
Conclusion: iCHO successfully reproduced metabolic alterations upon media modifications and thus highlighted systemwide changes.
PP 4-05
The relative importance of kinetic mechanisms and variable enzyme abundances for the
regulation of hepatic glucose metabolism - Insights from mathematical modeling
Holzhütter, H. - G.1, Berndt, N.1, Bulik, S.1
Charité - University Medicine Berlin, Institute of Biochemistry, Berlin, Germany
1
Background:Adaptation of the cellular metabolism to varying external conditions is brought about by regulated changes in
the activity of enzymes and transporters. Hormone-dependent reversible enzyme phosphorylation and changes of reactants
and allosteric effectors are the major types of rapid kinetic enzyme regulation whereas on longer time scales also changes
in protein abundance may become operative. Here, we used a comprehensive mathematical model of the hepatic glucose
metabolism of rat hepatocytes to decipher the relative importance of these different regulatory modes and their mutual
interdependencies in the hepatic control of plasma glucose homeostasis.
Results:Model simulations reveal significant differences in the capability of liver metabolism to counteract variations of
plasma glucose in different physiological settings (starvation, ad libitum nutrient supply, diabetes). Changes in enzyme
abundances adjust the metabolic output to the anticipated physiological demand but may turn into a regulatory disadvantage
if sudden unexpected changes of the external conditions occur. Allosteric and hormonal control of enzyme activities allow
the liver to assume a broad range of metabolic states and may even fully reverse flux changes resulting from changes of
enzyme abundances alone. Metabolic control analysis reveals that control of the hepatic glucose metabolism is exerted by
only seven enzymes, which are differently controlled by alterations in enzyme abundance, reversible phosphorylation and
allosteric effects.
Conclusion:In hepatic glucose metabolism, regulation of enzyme activities by changes of reactants, allosteric effects and
reversible phosphorylation is equally important as changes in protein abundance of key regulatory enzymes.
PP 4-06
Significance test for difference between paired temporal observations
Kondofersky, I.1, Erdmann, T.1, Theis, F. J.1, Fuchs, C.1
1
Helmholtz Zentrum, Neuherberg, Germany
We introduce a novel statistical significance test for the difference of paired time-resolved observations. A test statistic is
constructed similar to a univariate t-test as location, variability and size of the tested data are taken into account. This is
done by approximating the time courses with smoothing splines and then calculating and integrating over the functional
mean and the functional standard deviation. The formulated test statistic has an unknown distribution, thus for assessing
its significance we sample from the null hypothesis with preservation of the functional variability. It is the first statistical
test of its kind which is suitable for assessment of global differences in time-resolved and paired data.
The developed test is applied on a large number of different artificially created datasets and its dependence on several
83
Program /
Abstracts
influencing factors such as noise, number of time points per sample, number of samples per group and fraction of missing
time points per sample are investigated. Furthermore, the test is compared in terms of receiver operator characteristic (ROC)
curves to several other commonly available methods. Finally, the test is applied to real-world data where metabolomics
datasets from nutritional challenges are investigated. The results suggest almost no metabolic differences between
standardized and non-standardized meals prior to a nutritional challenge and thus provide evidence for unnecessary meal
standardization in nutritional pilot studies.
PP 4-07
A mathematical model of the bile enterohepatic circulation
Żulpo, M.1, Balbus, J.1, Wrona, A.1, Kubica, K.1
Wroclaw University of Technology, Department of Biomedical Engineering, Wroclaw, Poland
1
Objectives:. High blood cholesterol has been identified as a classic coronary risk factor. Low cholesterol balanced diet
is recognized as a major prevention route of cardiovascular diseases (CVD). However, the connection between dietary
cholesterol and CVD remains ambiguous and short term responses to high cholesterol meals are not fully understood. It
is not surprising that disruption of the bile enterohepatic circulation affect the cholesterol homeostasis. Also individual
features are crucial in these processes. Majority of patients exhibit elevated blood cholesterol level after meal (hyperresponders) but also significant group exhibit the inverse relationship (hypo-responders). Many processes and disorders
demonstrate undeniable role of the gallbladder in enterohepatic cycle and therefore in our three-compartment model we pay
a special attention to its modelling.
Methology: We have examined cholesterol homeostasis using a mathematical model, which consists of a set of the three
differential equations and is solved by Runge-Kutta method. The solutions of the model are time-concentration dependences
of cholesterol in the liver, peripheral blood and bile in the gallbladder. Our model allows to account for the synthesis of
cholesterol in liver, lipoprotein transport from and to the liver, portal circulation, demand of the body tissue and diet
cholesterol. This model allows to analyze the impact of the inhibitors of synthesis of cholesterol, as well as drugs being a bile
acid sequestrants. It is also possible to show the effect of increased intestinal peristalsis, abnormal amounts of lipoprotein
cell receptors and dietary cholesterol on cholesterol homeostasis.
Results: The developed three-compartment model allows to study the influence of bile circulation on cholesterol
homeostasis. Basing on the model analysis we have found that:
a) the influence of the dietary cholesterol on blood cholesterol level is similar for gallstone and gallstone free patients,
b) gallstone patients (GP) are more sensitive to the bile acid sequestrants. which regulate the amount of removed bile. Lower
bile level decreases total blood cholesterol level, increases the rate of cholesterol and cholic acid synthesis. However, the ratio
of the rate of cholesterol synthesis to the rate of cholic acid synthesis drops,
c) inter-individual features of bile circulation might result in different response to the dietary cholesterol (hyper and
hyperresponders).
Acknowledments: This work was partially supported by a research grant from the National Science Centre (DEC2011/03/B/NZ4/02390)
PP 4-08
New insights in the relation of liver and adipose tissue via Hedgehog Signalling
Matz-Soja, M.1, Rennert, C.1, Gebhardt, R.1
1
Institute of Biochemistry, Faculty of Medicine, Leipzig University, Leipzig, Germany
Hedgehog (Hh) and Wnt/ß-catenin signalling pathways are morphogenic cascades and essential for embryonic development
and tissue differentiation. On the other hand, both signalling pathways regulate elementary, metabolic functions in adult
tissues, despite a significantly reduced activity compared to embryogenesis. From our previous work, we know that in the
adult liver lipid metabolism and the IGF-homeostasis is regulated by the Hh pathway and kept in balance. Interruptions in
the activity of the Hh pathway lead to steatotic livers in mice which resembles the picture of non-alcoholic fatty liver disease
(NAFLD). In addition, we observed that the modulation of Hh signalling pathway in hepatocytes appears to cause a dramatic
effect on the adipose tissue.
To investigate the link between Hh signalling in hepatocytes and adipose tissue, various hepatocyte-specific knockout
mouse models have been bred for activation and inactivation of this cascade. The inactivation was carried out by deletion
of Smoothened (Smo), which is an important receptor protein in the signaling cascade. The Hh activation was achieved by
inhibition of the receptor protein Patched 1 (Ptch1). To take in account the influence of nutritional factors, a high fat diet
(HFD) was administered for 4 and 10 weeks to the transgenic animals. Subsequently, the various adipose tissue species
(brown, subcutaneous and visceral) were collected and used for further analysis.
The results of our studies showed that there is a, hitherto completely unknown, relation between the activity of Hh signalling
84
pathway in hepatocytes and adipose tissue. The mice with hepatocyte-specific inactivation of Hh cascade showed a significant
increase in white adipose tissue (WAT), whereas the mass of brown adipose tissue (BAT) does not change. In contrast, we
observed that a hepatocyte-specific activation of Hh signalling cascade leads to a significantly lower fat deposition in the
WAT, after administration of a HFD for 10 weeks. On the transcriptional level, the results showed that the expression of
thermogenic genes were altered in the hepatocytes due to the modulation of the Hh signalling pathway. From the present
results it can be concluded that the amount of the WAT is inversely correlated with the activity of Hh signaling pathway in
hepatocytes. The crucial question is, which Hh-dependent mechanism are used for the liver/adipose tissue communication.
Answering this question is the goal of further work.
PP 4-09
Why Respirofermentation? Explaining the Warburg effect in tumour (and other) cells by a
minimal model
Möller, P.1, Boley, D. 2, Kaleta, C. 3, Schuster, S.1
Friedrich Schiller University Jena, Bioinformatics, Jena, Germany
University of Minnesota, Computer Science & Engineering, Minneapolis, United States
3
Christian-Albrechts-University Kiel, Research Group Medical Systems Biology, Kiel, Germany
1
2
Main objectives: Tumour cells mainly rely on glycolysis leading to lactate rather than on respiration to produce ATP. This
phenomenon is known as the Warburg effect (named after German biochemist Otto Warburg) and also occurs in several
other cell types such as striated muscle cells, activated lymphocytes, microglia, and endothelial cells. It seems paradoxical at
first sight because the ATP yield of glycolysis is much lower than that of respiration. An obvious explanation would be that
glycolysis allows a higher ATP production rate, but the question arises why the organism does not re-allocate protein to the
high-yield pathway of respiration.
Materials and Methods: We model this by a linear programming problem in which not only the rates but also the maximal
velocities are variable and tackle this question by a minimal model only including three combined reactions. We consider the
case where the cell can allocate protein on several enzymes in a varying distribution.
Results: Here, we present a minimal model for explaining the higher ATP production in the Warburg effect. The model
predicts pure respiration, pure fermentation or respirofermentation, depending on protein costs and substrate availability. A
reallocation of protein to the high-yield pathway only pays if the synthesis costs for that pathway are low enough.
Conclusion: We focused on the explanation of the Warburg effect in terms of ATP production rate. It is worth noting that
this explanation is consistent with the argument in terms of precursor supply. Whenever the glycolytic rate is high while
respiration is limited, much pyruvate can be used for building biomass.
A useful feature of our model is that it can be treated analytically. It is linear in the reaction rates, while the underlying rate
laws can be nonlinear.
PP 4-10
Context-specific metabolic modelling reveals cell-type specific epigenetic control points of
the macrophage metabolic network
Pires Pacheco, M.1, Nikos, V. 2, John, E. 3, Pfau, T.1,4, Kaoma, T. 5, Heinäniemi, M.6, Nicot, N. 5, Vallar, L. 5, Bueb, J.- L.1, Sinkkonen, L.1,3, Sauter, T.1
University of Luxemburg, Esch-sur-aLzette, Luxembourg
Adobe Research, San Jose, United States
3
Luxembourg Centre for Systems Biomedicine, Esch-sur-Alzette, Luxembourg
4
University of Aberdeen, Aberdeen, Luxembourg
5
Luxembourg Institute of Health, Luxembourg, Luxembourg
6
University of Eastern Finland, Finland, Luxembourg
1
2
Objectives: 1) Enable the reliable building of context-specific algorithms that could potentially be used in a near future as
tool for the routinely diagnosis and analysis of metabolic disease. This requires fast algorithms with a high level of predictive
power, robustness to noise and sensitivity.
2) Establish benchmarking procedures that allow assigning context-specific algorithms in a standardized and unbiased
manner.
3) In the application to the macrophage metabolic network, identify epigenetic control points in the metabolism of
macrophages.
Material and Methods: FASTCORE (Vlassis et al. 2014) and FASTCORMICS (Pacheco et al. 2015), are devoid of heuristic
parameter settings and allows building of context-specific models in the time order of seconds and minutes respectively.
FASTCORE requires as input a set of reactions that are known to be expressed in the context of interest (core reactions set)
and a genome-wide reconstruction. FASTCORE uses an approximation of the cardinality function to force the core reaction
set to carry a flux above a user-defined threshold ε. Then applies L1-minimization to penalize the activation of reaction
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with low confidence level while constraining the set of core reactions to carry a non-negative flux. Whereas FASTCORMICS
includes additionally a discretization step based on BARCODE (Zilliox & Irizarry 2007; McCall et al. 2011) to determine
active and inactive reactions based on solid statistical evidences, in order to cope with probe effects and noise affecting
microarray experiments.
The prediction power, robustness and sensitivity of FASTCORE and FASTCORMICS along with 5 competing algorithms were
tested within benchmarking procedures using real and artificial data. This was based among others on cross-validations
in order to assess how the models varied when 10% to 50% of missing input data and on the assessment of the expression
confidence level of the genes included in the model via Barcode z-scores and the Human Protein Atlas and confidence scores
(Pacheco et al. 2016).
Results and conclusion: FASTCORE and FASTCORMICS were shown to outperform their competitors in speed and in the
capacity to capture small metabolic variations between tissues (Vlassis et al. 2014; Pacheco et al. 2015; Pacheco et al. 2016).
Further, the FASTCORE family models showed the highest enrichment in genes with high confidence levels. Surprisingly the
benchmarking procedures revealed that the models reconstructed by the tested algorithms when fed with the same input
shared for the more distinct models only around 30% of the reactions (Pacheco et al. 2016).
In an application, the mapping of ChIP-Seq experiments for histone H3 lysine27 acetylation (H3K27ac) on the macrophage
metabolic model built by FASTCORMICS via the integration of microarray data showed that genes under high regulatory
load have the most cell type-restricted and abundant expression profiles within their respective metabolic pathways and are
associated to reactions significantly enriched for transport reactions and other pathway entry points, suggesting that they
are the critical regulatory control points for cell type-specific metabolism (Pacheco et al. 2015).
PP 4-11
How Hedgehog signaling pathway activity controls steroidogenesis in the liver
Rennert, C.1, Matz-Soja, M.1, Gebhardt, R.1
1
Institute of Biochemistry, Faculty of Medicine, Leipzig University, Leipzig, Germany
The liver is one of the largest organs in our organism with multiple anabolic and catabolic functions which can be
carried out simultaneously only by zonation. Recent studies showed that Hedgehog signaling, a morphogenic pathway
commonly associated with embryogenesis, development and cancer is active in adult hepatocytes and acts as master
regulator of zonation in the adult liver (Gebhardt & Matz-Soja, WJG, 2014). Our goal is the investigation of the gender
dimorphism of gene regulation in the liver. So far, we surprisingly found a regulation of steroidogenesis associated genes
in hepatocytes of mice with aberrant Hedgehog signaling; yet it was assumed that steroidogenesis occurs in the liver only
during embryogenesis and is down-regulated afterwards. A prominent gene we found is Cyp17a1, a central regulator of
steroidogenesis.
We generated two transgenic mouse strains with an inactivated and constantly-activated Hedgehog signaling pathway in
hepatocytes, respectively. The first mouse strain has a hepatocyte-specific knockout of Smoothened (Smo), whereby the
inhibition complex of the GLI transcription factors is continuously active, which results in an inactivated signaling cascade
(SAC mice). The second strain has a hepatocyte-specific knockout of Patched1 (Ptch1) which lead to a permanently active Smo
receptor whereby the inhibition complex of the GLI factors is inactivated and the signaling cascade is continuously active
(Ptch1LC1 mice).
We found, that a down-regulated Hedgehog pathway results in the up-regulation of some of the steroidogenic genes and vice
versa. Cyp17a1 is up-regulated in SAC knockout mice and decreased in expression in Ptch1LC1 knockouts in comparison to
wild types. Furthermore we observed an analogous regulation of estrogen receptor (Esr1) expression, relevant for steroid
hormone mediated signaling, with a clear correlation to the regulation of Cyp17a1 expression in both transgenic mice
strains. A transcription factor binding site analysis revealed GLI binding sites in the promotor region of Esr1. For Cyp17a1
those binding sites were not found. It needs to be further investigated how the regulation of steroidogenesis is influenced by
Hedgehog signaling and how Cyp17a1 and Esr1 expression is regulated by each other.
Collectively, the experiments showed a clear influence of the morphogenic Hedgehog pathway on the regulation of
steroidogenesis in the liver. These unexpected findings are promising for future studies to improve our understanding of the
gender dimorphism of regulatory mechanisms in liver.
86
PP 4-12
Cause and Cure of Sloppiness in Ordinary Differential Equation Models
Tönsing C.1, Timmer J.1, Kreutz C.1
University of Freiburg, Institute of Physics, Freiburg, Germany
1
For the purpose of mathematical modeling of biochemical reaction networks by the frequently utilized nonlinear ordinary
differential equation (ODE) models, parameter estimation and uncertainty analysis is a major task.
In this context the term sloppiness has been introduced recently for an unexpected characteristic of nonlinear ODE
models. In particular, a broadened eigenvalue spectrum of the Hessian matrix of the objective function covering orders of
magnitudes is observed, although no such hierarchy of parameter uncertainties was expected a priori.
In this work, it is shown that sloppiness originates from structures in the sensitivity matrix arising from the properties of
the model topology and the experimental design. It will be clarified that the intensity of the sloppiness effect is controlled
by the design of experiments, i.e., by the data. Thus, we conclude that the assignment of sloppiness to a model as a general
characteristic is incomplete without discussing experimental design aspects. Furthermore, we validate this proposition
by presenting strategies using optimal experimental design methods in order to circumvent the sloppiness issue and show
results of non-sloppy designs for a benchmark model.
Reference:
Tönsing C., Timmer J., Kreutz C., Cause and cure of sloppiness in ordinary differential equation models, Phys. Rev. E 90, 023303
(2014)
PP 4-13
Sexual dimorphism during development of cyp51 liver conditional knockout mice
Urlep, Z.1, Lorbek, G.1, Juvan, P.1, Perse, M. 2, Jeruc, J. 2, Björkhem, I. 3, Matz-Soja, M.4, Gebhardt, R.4, Rozman, D.1
Medical Faculty, University of Ljubljana, CFGBC, Institute of Biochemistry, Ljubljana, Slovenia
Medical Faculty, University of Ljubljana, Medical Experimental Centre, Institute of Pathology, Ljubljana, Slovenia
3
Karolinska Institute, Karolinska University Hospital, Huddinge, Department of Laboratory Medicine, Division of Clinical Chemistry, Huddinge, Sweden
4
Medical Faculty, University of Leipzig, Institute of Biochemistry, Leipzig, Germany
1
2
Main objectives: With the advancement in personalized medicine, differences between sexes have been gaining increasing
attention. Liver is a tissue with one of the highest levels of sexual dimorphisms. Studies focusing on NAFLD, NASH and
HCC show male predominance, although the results can vary with respect to age, race and type of disease (e.g. lean NASH).
Cholesterol is inherently important for cell function and survival. Blocking its synthesis in the liver causes severe histological
and metabolic changes. In the present study we examined the role of sex during development of Cyp51 liver knockout (LKO)
mice with a defect in hepatocyte cholesterol synthesis.
Methods: From 704 mice of different genotypes, 46 died or had to be euthanized between 4 - 10 weeks of age. All were of the
KO genotype, termed runts (KOR). They were evaluated histologically, biochemically and genetically in order to assess the
phenotype, developmental stage and changes to the liver. Affymetrix microarrays were used to evaluate global expression
with special attention on sexual dimorphism.
Results: Few differences separate sexes in Cyp51 liver KOs during development, however the emergence of KO runts after
4 weeks is clearly male predominant with a 2:1 ratio. Histologically, there is no difference between sexes in KO runts, but
microarrays revealed 23 differentially expressed genes. Upregulation of caspase 12 (Casp12) points to male susceptibility
towards ER stress. KEGG pathway enrichment revealed better conservation of metabolism in KOR females, while
inflammatory processes were stronger in males. Similarly, male enrichment of NFY transcription factors, which play a role
in unfolded protein response, points to female-based stress protection. Surprisingly, the results in adult KOs contrast the
observations in KORs, with females exhibiting decreased metabolism and increased inflammation.
Conclusion: In light of increasing gender awareness in liver pathologies, we show that liver cholesterol ablation by Cyp51
knockout progresses differently in males and females. During development, the female gender seems to offer some stress
protection, however the higher requirements for cholesterol in adult females may put them to greater risk thereafter.
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PP 5: Multi-Scale Approaches
PP 5-01
Tissue architecture representation in pharmacological models. Insights from liver.
Boissier, N.1, Celliere, G.1, Friebel, A. 2, Hoehme, S. 2, Hengstler, J. 3, Vignon-Clementel, I.1, Drasdo, D.1
Sorbonne Universités, Inria, UPMC Univ Paris 06, Lab. J.L. Lions, PARIS, France
University of Leipzig, Interdisciplinary Centre for BioInformatics, LEIPZIG, Germany
3
IfADo, Systemtoxikologie, DORTMUND, Germany
1
2
Question: Mathematical models of drug clearance and organ metabolism are increasingly used to obtain quantitative
predictions of drug kinetics or metabolite levels in the body. The tissue architecture therefore needs to be taken into
account. Yet, its influence on tissue function remains unclear. We propose to study the impact of the tissue architecture
representation on the predictions obtained from pharmacokinetic models. Because liver is the main detoxifying organ, we
have taken the liver tissue architecture as the example for this study.
Methods: We compared steady-state outlet concentrations as well as dispersion induced by the space inhomogeneities for
several spatial models, from one or several compartments, different spatial alignment of cells along blood vessels to an imagebased representative liver lobule (anatomical unit of liver). In the blood vessel network, blood is assumed an incompressible
Newtonian fluid in a series of connected cylinders that flows according to Poiseuille’s law and mass conservation at junctions.
The variation of apparent viscosity with diameter is following Pries’ law derived for in-vivo conditions [1]. Transport of the
compound in the vasculature is then modeled and coupled with the metabolism inside the cells. The representative liver lobule,
generated by Hoehme et al [2], is based on geometrical statistics extracted from confocal image analysis using TiQuant [3].
Results and Conclusions: The simulations reveal that the impact of the precise representation of space depends on the
order of the reaction kinetics. A space-discretization along the direction of the compound concentration gradient leads to a
higher extraction of the compound whereas considering the flow inhomogeneities leads to a lower extraction. We show that
the choice of how space will be taken into account in the model is crucial when the results are used quantitatively, to explain
data or to predict drug effects.
[1] Pries A. R., Secomb T. W., Gaehtgens P., “Biophysical aspects of blood flow in the microvasculature.” Cardiovascular
research. , 32(4), 654-667 (1996).
[2] Hoehme S., Brulport M., et al. “Prediction and validation of cell alignment along microvessels as order principle to restore
tissue architecture in liver regeneration.” PNAS 107.23 10371-10376 (2010)
[3] Hammad S., Hoehme S., Friebel A., et. al. “Protocols for staining of bile canalicular and sinusoidal networks of human,
mouse and pig livers, three-dimensional reconstruction and quantification of tissue microarchitecture by image processing
and analysis.” Archives of Toxicology 88 (5) 1161-1183 (2014)
PP 5-02
Agent-based modelling characterises the effect of localized versus spread damage
among mitochondrial population
Dalmasso, G.1, Hamacher-Brady, A.1
German Cancer Research Centre (DKFZ), Lysosomal Systems Biology, Heidelberg, Germany
1
When a cell is subjected to metabolic or environmental stresses, mitochondrial fusion and fission are fundamental in
conserving a population of healthy and operative mitochondria. In particular, fusion reduces cellular stress rescuing partially
dysfunctional mitochondria by redistributing damaged constituent, whereas fission allows the segregation and removal
of damaged parts. Moreover, fission is essential in the generation of new mitochondria. A fine balance between fusion and
fission machineries is therefore needed for cell survival, and disorders in these processes can lead to neurodegenerative
diseases, e.g. Parkinson’s. In order to investigate how mitochondrial damage affects cellular and mitochondrial population
homeostasis, here we present a systems biology approach, using agent-based modeling (ABM), to investigate the link between
mitochondrial damage and stress response within single cells. Specifically, we modeled a population of fusion-and-fission
capable mitochondria which can undergo different damage levels and are able, accordingly, to discard damaged parts or
being rescued by fusion. Moreover, in order to profit from the intrinsic spatial component of ABM we analyzed the effect
of localized versus spread damage among mitochondrial population. As a result we efficiently (1) discerned which is the
natural emerging damage threshold the cell is able to handle in localized versus spread damage scenarios, (2) estimated
the role of degradation, fusion, fission and biogenesis on the cellular damage response, and (3) determined the maximum
amount of stress a cell can handle comparing fixed and dynamic probabilities of degradation, fusion, fission and biogenesis.
This modeling approach represents a novel framework to combine in vitro and in vivo experimental results and could be
used to bring new insight in understanding the link between mitochondrial dynamics and cell behavior, crucial requisite for
developing new treatments strategies of mitochondrial diseases.
88
PP 5-03
Morpheus 2.0: an open-source framework for multi-scale multicellular systems biology
de Back, W.1,2, Starruß, J. 2, Brusch, L. 2, Deutsch, A. 2
TU Dresden, Faculty of Medicine “Carl Gustav Carus”, Dresden, Germany
TU Dresden, Center for Information Services and High Performance Computing, Dresden, Germany
1
2
Morpheus is a modeling and simulation environment for multicellular systems biology that provides tools for the simulation
and integration of differential equations, reaction-diffusion systems and cell-based models. It facilitates the creation of
single- and multi-scale models by describing them in a SBML-like markup language rather than by programming.
Modelers interact with Morpheus through a user-friendly graphical interface (fig. 1) that facilitates the entire workflow
from model construction to simulation, batch processing, visualization and analysis (Starruß et al., 2014). This enables also
users with limited programming experience to construct and simulate models of multicellular systems. Its usability manages
modeling workflows, enables rapid model development and makes Morpheus well-suited for use in education.
Since the recent release of Morpheus 2.0 as an open-source framework, programmers can customize and extend the
Morpheus framework. A flexible plugin interface allows a wide range of features to be added including new rules for cell
shapes, behaviors and interactions as well as tools for visualization, analysis or data export.
With these distinct interfaces, Morpheus separates modeling from programming, providing both usability for the modeler
and flexibility for the programmer to create innovative models of multicellular systems. Source code and binary packages of
Morpheus for Linux, Windows and Mac OSX are available from: http://imc.zih.tu-dresden.de/wiki/morpheus.
Reference:
J. Starruß, W. de Back, L. Brusch and A. Deutsch. Morpheus: a user-friendly modeling environment for multi-scale and
multicellular systems biology. Bioinformatics, 30(9):1331-1332, 2014.
Morpheus 2.0
Simulation browser
Modeling editor
PP 5-04
Modelling And Simulation Of Tumour Growth Compared With Xenograft Models And DbscTRAIL Therapy In 2D Cell Culture Population.
Galliani, S.1, Shchekinova, E.1, Divine, M. 2, Reuss, M.1
University of Stuttgart, SRCSB - Stuttgart Research Center Systems Biology, Stuttgart, Germany
Eberhard Karls Univeristy of Tübingen, Werner Siemens Imaging Center - Department of Preclinical Imaging and Radiopharmacy, Tübingen, Germany
1
2
High metabolic activity is the main trait that the majority of the tumours share. Functional imaging techniques, as
Positron Emission Tomography (PET), are used in clinical oncology to identify tumours and metastasis in the body. A
radiolabelled tracer as Fluoro-Deoxy-Glucose - FDG, injected i.v. in the patient, identifies areas of high glucose uptake, thus
translating with PET scans the higher metabolic activity of tumour areas. Combining PET-FDG with MRI-ADC (Magnetic
resonance imaging - Apparent Diffusion Coefficient) images, it is possible to localize inside the tumour areas of necrosis
and proliferation. Once the tumour has been identified, the treatment with drugs or proper therapy are administered to the
patient.
We built a multiscale agent based model of 2D vascular tumour growth based on Perfahl et al. 2011 to investigate the role
played by glycolysis (Pasteur and Warburg effect) on tumour development and to analyse the impact of the quiescent
population. We then qualitatively compare the glucose uptake in our simulations results with the FDG-PET xenograft model
images provided by Mathew Divine (Department of Preclinical Imaging and Radiopharmacy - Eberhard Karls University of
Tübingen) identifying areas of proliferation and necrosis in the tumour. We accounted for necrotic areas formation with an
age-dependent vessel collapse. Finally, given the experimental results of the effect of Db-scTRAIL molecule on cancer cell
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cultures, we built a dose dependent function to model the apoptotic process in cells intercalated with the drug, together with
randomly assigning, from a uniform distribution, the drug resistance to cells.
We show that the shortage of glucose plays an important role in tumour development and subsequent angiogenesis. As
soon as the tumour grows, the pressure exerted by the growing tumour damages the vessels infiltrated in the tumour
tissue, thus leading to the vessels collapse and necrotic areas formation. Once applied the treatment with Db-scTRAIL, the
initial population of cells develop a drug resistant ability. The subsequent population born after cell division show different
resistance, thus effecting the overall growth/death dynamics.
We observe that necrosis takes place in areas with insufficient delivery of nutrients leading to subsequent formation of
stratified layers of necrotic, quiescent and proliferative tissues. The high heterogeneity of shape and size in mice’s tumour,
despite their identical genetic background, show us the stochasticity of the tumour formation. A clear developing behaviour
cannot be identified. The effect of Db-scTRAIL on 2D population of cancer cells is a function of dose-dependent apoptosis and
drug resistance.
References: Perfahl H., Byrne H.M., Chen T., Estrella V., Alarcón T., et al. Multiscale Modelling of Vascular Tumour Growth in
3D: The Roles of Domain Size and Boundary Conditions PLoS ONE, 6(4): e14790 (2011).
PP 5-05
Data Needs Structure: Data and Model Management for Distributed Research Networks in
Systems Biology and Systems Medicine
Golebiewski, M.1, Krebs, O.1, Nguyen, Q.1, Owen, S. 2, Stanford, N. 2,3, Van Niekerk, D.4, Wolstencroft, K. 5, Bacall, F. 2, Kania, R.1, Rey, M.1, Weidemann, A.1,
Wittig, U.1, Snoep, J. 2,3,4, Mueller, W.1, Goble, C. 2
HITS gGmbH, Heidelberg, Germany
University of Manchester, School of Computer Science, Manchester, Great Britain
3
University of Manchester, Manchester Institute for Biotechnology, Manchester, South Africa
4
University of Stellenbosch, Department of Biochemistry, Stellenbosch, South Africa
5
Leiden University, Leiden Institute of Advanced Computer Science, Leiden, Netherlands
1
2
Systems Biologists need a data management infrastructure that enables collaborating researchers to share and exchange
information and data as and when it is produced, throughout the entire iterative cycle of experimentation and modelling.
We develop and offer integrated data management support for research in the fields of systems biology and systems medicine
within and across research consortia. This comprises a whole package of solutions and is applied to geographically dispersed,
interdisciplinary and large-scale research initiatives in which we are responsible for the scientific data management, like
the German systems biology network Virtual Liver (http://www.virtual-liver.de/), from 2010 to 2015, and the newly
established German research initiative ‘Systems Medicine of the Liver’ (LiSyM), as well as some European research
networks like ERASysAPP (ERA-Net for Systems Biology Applications), the former SysMO initiative (Systems Biology of
Microorganisms) or NMTrypI (New Medicines for Trypanosomatidic Infections). Parts of these solutions are also applied to
smaller projects with a more local focus as the German project ‘Systems Biology of Erythropoietin’ (SBEpo) or the Synthetic
Biology Centres at Manchester (SynBioChem) and Edinburgh (SynthSys).
Our data management concept aims at bundling, storing and integrating research assets like data, models and description of
processes and biological samples in a Findable, Accessible, Interoperable and Reusable (FAIR) manner (http://fair-dom.
org). It consists of 4 major pillars:
1) Infrastructure backbone: The SEEK platform as registry and a commons for data, models, samples, processes and resulting
publications or presentations, at the same time yellow pages for projects, people and events
2) Terminology: Tailored use of controlled vocabularies and ontologies to describe the data and its metadata (data describing
the data)
3) Modelling support:Seamless handling and simulation of models by integrated modelling platforms (JWS-Online,
SYCAMORE, Cytoscape)
4) Social support:Facilitators within the projects for gathering requirements and dissemination
This concept is applied in our own research networks, but also used by other systems biology consortia.
Unlike the majority of data management systems, we specifically support the interaction between modelling and
experimentation. Datasets can be associated with models and/or workflows or biological samples, and model simulations can
be compared with experimental data.
90
PP 5-06
An integrated temporal molecular response of vascular endothelial cells exposed to
ionizing radiation
Guipaud, O.1, Heinonen, M. 2, Buard, V.1, Tarlet, G.1, Jaillet, C.1, Chan, P. 3, Vaudry, D. 3, Vinh, J.4, d’Alché-Buc, F. 5, Milliat, F.1
IRSN, Human health radiation protection unit, Fontenay-Aux-Roses, France
Aalto University, Department of computer science , Espoo, Finland
3
Rouen University, PISSARO Proteomic Platform, Rouen, France
4
ESPCI ParisTech, Spectrométrie de Masse Biologique et Protéomique, Paris, France
5
Paris-Saclay University, Télécom ParisTech, Paris, France
1
2
Question: Used in more than half of patients with tumors, radiation therapy (RT) plays a crucial role in the cure of cancers.
The therapeutic index of RT depends on both tumor control and normal tissue tolerance. The vasculature participates in
tumor progression and is required for normal tissue homeostasis orchestrating wound healing after radiation injury. We and
others postulate that the vascular endothelium is a key compartment involved in the response of both normal and tumor
tissues after RT and is the best clinical target to improve the differential effect of RT in the future. However, the response of
vascular endothelial cell (EC) to radiation is not fully known and we lack an integrated view including consequences for both
tumor and normal tissues. Here, we propose to use a systems biology approach to understand the dynamic response of ECs
after irradiation and to decipher temporal biological networks involved in this response.
Methods: We conducted exploratory experiments on primary human umbilical vein endothelial cells (HUVECs) under
control, under a radiotherapy dose fraction (2 Gy) and under a high single dose (20 Gy). We measured proteomic profiles
with 2D-DIGE and iTRAQ quantitative mass spectrometry, and transcriptional profiles of about 500 genes with real time
quantitative PCR with measurements at 12 h, 1, 2, 3, 4, 7, 14 and 21 days following radiation exposure. We used the novel
Bayesian likelihood ratio test we previously developed [1] to estimate the differential expression time periods, and we
performed gene ontology and functional pathway analyses of the differential genes and proteins.
Results: We applied the novel non-stationary Gaussian process (GPR, Gaussian Process Regression) as the underlying
expression model, with major improvements on model fitness on perturbation and stress experiments. The method is robust
to uneven or sparse measurements along time. Applying the ratio test to systems of genes and proteins provides the temporal
response timings and durations of expression following irradiation. As a result, using the cascade of differential expression
periods, domain literature and gene enrichment analysis, we gain insights into the dynamic response of endothelial cells to
irradiation.
Conclusions: The method estimates a temporal cascade of differentially expressed genes and proteins providing a largescale view on the expression progression of the irradiation response. Using statistical learning tools to unravel parts of the
dynamics of biological networks involved in ECs in response to ionizing radiation, our global approach will tentatively help to
bring together the problems of tumor destruction and protection of healthy tissues.
[1] Heinonen, M. et al. Detecting time periods of differential gene expression using Gaussian processes: an application to
endothelial cells exposed to radiotherapy dose fraction. Bioinformatics 31, 728-35 (2015).
PP 5-07
Systems analysis of the structural and molecular changes along the dynamics of liver
fibrosis development
Hammad, S.1, Telfah, A. 2, Othman, A. 2, Hengstler, J. 3, Hergenröder, R. 2, Dooley, S.1
Medical Faculty Mannheim, Heidelberg University, Molecular Hepatology - Alcohol Associated Diseases, Department of Medicine II, Mannheim, Germany
Dortmund University, Germany, ISAS-Institute for Analytical Sciences, Dortmund, Germany
3
Dortmund University, Germany, Leibniz Institute for Work Physiology and Human Factors (IfADo), Dortmund, Germany
1
2
Chronic liver disease (CLD) is a major health problem and it often leads to hepatocellular carcinoma (HCC). CLD progression is
associated with altered tissue microarchitecture, gene expression and metabolite secretion. To study the progression of CLD,
we used carbon tetrachloride (CCl4)-induced fibrosis and non-alcoholic steatohepatitis (NASH) -HCC model systems in mice.
Adult male C57Bl6 mice were treated with CCl4 for 10 weeks (1 ml/kg body weight mixed with olive oil to 50 μl; twice per week
i.p.). Then, 3-4 mice were sacrificed for plasma and liver analysis every week. For NASH-HCC model, on the second day after
birth, male C57Bl6 mice were subjected to a single subcutaneous injection of 200 µg streptozotocin. Four weeks after injection,
mice were fed high fat diet ad libitum until sacrifice at 6, 8, 12 and 20 weeks. At predefined time-points, blood and liver samples
were collected for analysis. We have investigated the fingerprints of CLD progression by biochemical, histopathological and
metabolites as well as gene expression analysis in a time-resolved experiment in both models. Biochemical parameters e.g. ALT
and AST were increased during CLD progression in CCl4 model. This alteration was correlated with accumulation of extracellular
matrix (ECM) as indicated by picro-sirius red and hematoxylin and eosin staining. Deposition of collagen, collagen-producing
cells and lipid droplets in case of NASH-HCC are well correlated with CLD progression. Gene expression and metabolite analysis
as well as three-dimensional reconstruction of liver microarchitectures are ongoing. Here we could show that our approaches to
study liver systems medicine using CCl4 and NASH-HCC models developed CLD comparable to the human situation.
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PP 5-08
Multiscale modeling of liver regeneration
Hoehme, S.1, D’Alessandro, L. A. 2, Hengstler, J. G. 3, Klingmueller, U. 2, Drasdo, D.4,5
University of Leipzig, Institute of Computer Science, Leipzig, Germany
German Cancer Research Center, Heidelberg, Germany
3
IfADo, Dortmund, Germany
4
INRIA Paris, Paris, France
5
University of Leipzig, IZBI, Leipzig, France
1
2
During the last years, modeling of different physiological and pathological aspects of the liver advanced significantly with the
development of increasingly realistic models on molecular, cellular, tissue and whole organ scale. Nevertheless, model driven
liver research is still hampered by a lack of techniques that allow robust integration of these different scales into unifying
frameworks. We here present a novel multiscale spatio-temporal 3D model of liver tissue that is based on in-vivo 3D imaging
and that may serve as such unifying framework.
We use this model to study liver regeneration upon damage or tissue loss which depends on intracellular and tissue scale
processes, which interplay with tissue mechanics. In order to capture all these processes at their respective scales, the
presented multiscale modelling framework integrates sub-models at all relevant scales from intracellular signalling to
body level. It thereby allows predictions on a wide range of possible regeneration scenarios and helps to identify on one
hand particularly informative experiments permitting to distinguish between alternative mechanisms, on the other
hand impossible scenarios that should not be pursued experimentally. In this way, the model predictions can guide the
experimental strategy. The multiscale tissue model is able to simultaneously reproduce all experimental observations
including the regeneration process kinetics on the tissue scale.
The presented study is an example for how the tight systems-biological integration of experimentation and modelling, both
covering multiple scales, can facilitate understanding of complex multi-scale processes as liver regeneration.
PP 5-09
Quantification of the effect of mutations using a global probability model of natural
sequence variation
Hopf, T.1,2, Ingraham, J.1, Poelwijk, F. 3, Springer, M.1, Sander, C.1, Marks, D.1
Harvard Medical School, Systems Biology, Boston, United States
TU München, Informatics, Garching, Germany
3
UT Southwestern Medical Center, Green Center for Systems Biology, Dallas, United States
1
2
Modern biomedicine is challenged to predict the effects of genetic variation. Systematic functional assays of point mutants of
proteins have provided valuable empirical information, but vast regions of sequence space remain unexplored. Fortunately,
the mutation-selection process of natural evolution has recorded rich information in the diversity of natural protein
sequences. Here, building on probabilistic models for correlated amino-acid substitutions that have been successfully applied
to determine the three-dimensional structures of proteins, we present a statistical approach for quantifying the contribution
of residues and their interactions to protein function, using a statistical energy, the evolutionary Hamiltonian. We find that
these probability models predict the experimental effects of mutations with reasonable accuracy for a number of proteins,
especially where the selective pressure is similar to the evolutionary pressure on the protein, such as antibiotics.
PP 5-10
Modeling Tumor-Immune System Interaction
Jagiella, N.1, Schmidt, G.1
1
Definiens AG, München, Germany
Question: New cancer immunotherapies aim on (re)enabling the bodies immune system to fight the cancer. As many of
those therapies are expensive and linked to side-effects it is especially important to identify only those patients which they
might be successful for. At the example of immune suppressor protein PDL1 we study the interactions between tumor and
immune cells by a data-driven modeling approach with the goal of discriminating patient types by reinterpreting the data by
parameter inference.
Methods: PDL1 is expressed by immune cells to slow down the immune response, but can also be found on certain tumor
cells allowing them to escape the immune system. As such it is target of anti-PDL1 immunotherapies.
In a first step, we compare a compartment model to immunohistochemistry (IHC) and gene expression (GE) data from
patients. The model describes CD8+ T cells and PDL1 expression in tumor cells by a system of moment equations derived
from a master equation assuming the cellular processes to be stochastic. Then, by Bayesian methods we infer the model
parameters from patient population snapshot data.
92
In a second step, we study the effects of the spatial arrangement of cell types using a hybrid multiscale model representing
cells by an agent-based model and molecules by partial differential equations.
Results: We found that the PDL1 expression rates inferred from the patient data helped to stratify the patients in respect
to clinical outcome. Crucial was the assumption that PDL1 expression in tumor cells depends on IFNg secreted by CD8+ T
cells, rather than direct contact with CD8+ T cells where no such effects could be seen. The spatial model was able to predict
the ratio of CD8+ to PDL1+ cell densities found in the patient data by only one control parameter determining the patientspecific situation: the CD8+ T cell influx into the local tumor environment.
Conclusions: From snapshot data of different sources we were able to identify the mechanisms controlling tumor-immune
system interactions, specifically between CD8+ and PDL1+ cells. The interplay between short (PDL1) and long-range (IFNg)
signaling was found to play a key role and might permit to identify potential responders to anti-PDL1 therapy.
PP 5-11
Can in vivo 31P MRS assay of myocardial PCr/ATP ratio homeostasis test model
predictions?
Bakermans, O.1, Bazil, J. 2,3, Nederveen, A.1, Strijkers, G.1, Boekholdt, M.1, Beard, D. 3, Jeneson, J.4,1
Academic Medical Center University of Amsterdam, Radiology, Amsterdam, Netherlands
Michigan State University, Physiology, East-Lansing, United States
3
University of Michigan, Molecular and Integrative Physiology, Ann-Arbor, United States
4
University Medical Center Groningen, Neuroimaging Center, Groningen, Netherlands
1
2
B
PCr/ATP
/ATP ratio
myocardial PCr
Main Objective: To integrate experimental Magnetic Resonance Spectroscopy (MRS) and numerical simulation platforms
for the clinical investigation of human myocardial ATP free energy homeostasis. Here, we investigated if current standards
of accuracy of in vivo 31P MRS assay of myocardial phosphocreatine (PCr) and ATP levels are adequate to test computational
model predictions of myocardial PCr/ATP ratio homeostasis during metabolic stress in healthy versus diseased phenotypes.
Materials and Methods: In vivo 31P Magnetic Resonance Spectroscopy (31P MRS) at 3 Tesla was used to measure the steadystate concentration ratio of PCr and ATP in the myocardium of 7 healthy subjects in resting state (heart rate ~60 bpm) using
a linear 31P surface coil in combination with standard cardiac-triggered localization sequences1. Numerical simulations of
myocardial PCr/ATP ratio homeostasis in response to metabolic stress resulting from increased cardiac work were performed
using a biophysical model of myocardial oxidative metabolism 2. The model features a closed-loop homeostatic control design
of mitochondrial oxidative ATP synthesis2. Altered myocardial ATP metabolism in sarcomeric cardiomyopathy (SCM) was
modeled by a reduction in energetic efficiency of contraction. A Monte Carlo approach was applied to compute model output
uncertainty using the empirical accuracy of the in vivo 31P MRS assay.
Results: Using a 3D ISIS volume localization scheme1 to measure the in vivo basal myocardial PCr/ATP ratio in the healthy
human heart (Figure 1A and B), a mean value of 1.33 ± 0.14 (± SD) was found. Alternatively, using 2D ISIS slice selection
in combination with 1D SI read-out1, the myocardial PCr/ATP ratio was 1.97 ± 0.38 (mean ± SD). Bland-Altman analysis
of repeatability yielded values of 42% and 44%, respectively. Numerical simulation showed that this level of accuracy is
insufficient to test the validity of the metabolic network model and its central hypothesis that a closed-loop control design
underlies myocardial ATP free energy homeostasis in the human heart (Figure 1C). Furthermore, detection of failing
metabolic homeostasis in SCM may only be achieved if the cardiac work rate in patients is increased to levels associated with
heart rates above 150 bpm (Figure 1C).
Conclusions: A significant improvement in accuracy of in vivo 31P MRS assay of the myocardial PCr/ATP ratio in the human
heart is needed towards validation of a computational model of myocardial oxidative metabolism and diagnosis of failing
ATP free energy homeostasis in cardiomyopathy. This will require upgrading the MRS hardware (i.e., to a multi-element
quadrature cardiac 31P coil and/or a 7 Tesla human MR scanner).
References
C
1. Lamb HJ et al. NMR Biomed 9: 217-27, 1996.
2. Wu F et al. J Physiol 586, 4193-4208, 2008.
healthy
SCM
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PP 5-12
Multiscale modelling of hepatocellular carcinoma and transasrterial chemoembolisation
therapy
Jeneson, T.1, Lapin, A.1, Horger, M. 2, Bitzer, M. 3, Reuss, M.1
Stuttgart Research Center Systems Biology, Stuttgart, Germany
University of Tuebingen, Diagnostic Radiology, Tuebingen, Germany
3
University of Tuebingen, Internal Medicine I, Tuebingen, Germany
1
2
Aim of study: To develop a multiscale model to mirror the development of advanced hepatocellular carcinoma (HCC) under
transarterial chemoembolisation (TACE) therapy, propose improvements to the current therapy and predict their outcomes.
TACE therapy: HCCs are characterised by high arterial blood flow unlike a healthy liver which receives majority of its blood
from the portal vein [1]. In TACE therapy, drug eluting beads (DEBs) are injected through a catheter selectively into tumour
vessels. The beads occlude these vessels thereby obstructing blood flow to the tumour and slowly release the drug into the
tissue in a controlled manner. Selective injection of the beads and obstruction of vessels reduces washout of the drug from
the tumour and damage to the surrounding healthy liver tissue.
Modelling framework: A hybrid cellular automaton spanning the intracellular, cellular and tissue scales is used to model
the development of vascular tumours [2]. In the model, cells are modelled as discrete quantities that move, proliferate
and die. The cell cycle is governed by an ordinary differential equation. Predefined rules decide how cells move and die.
Additionally, diffusible transport of nutrients like oxygen and glucose and growth factors like vascular endothelial growth
factor (VEGF) are modelled using partial differential equations. Blood vessel network forms the vascular layer of the model.
The vessels undergo structural adaptation at each time-step based on factors like the radius, flow, haematocrit, intra-vascular
pressure etc.
Results: We have extended the model to account for the dual blood supply (from the artery and the portal vein) as seen in a
human liver. As expected, we observe, in our simulations, high arterial flow in the presence of a tumour. The location of DEBs
is determined depending on the vessel radius, type of blood flow (arterial or venous) and the microenvironment. The blood
flow in vessels with DEBs is obstructed and drug release is modelled as a slow release and diffusion process. Low blood flow
leads to hypoxia and eventual necrosis due to collapse of blood vessels and apoptosis of quiescent tumour cells. Post therapy,
we observe either a total eradication of the tumour or a revascularisation and relapse due to surviving tumour cells.
Conclusions: We have been successful in modelling the development of HCC and the administration of TACE therapy.
Model results showing either a complete response with no tumour cells post therapy or a partial response with some
surviving tumour cells that lead to relapse of the tumour are consistent as seen in the clinical studies. Our future work
aims at determining possible improvements to the current therapy, like administration of anti-angiogenic drug to prevent
revascularisation post TACE and target the surviving tumour cells to avoid relapse or use of hyperbaric chambers to
overcome hypoxia and increase the efficacy of the drug released from DEBs.
[1] Breedis et al. The blood supply of neoplasms in the liver. The American Journal of Pathology, 30(5):969, 1954.
[2] Perfahl et al. Multiscale modelling of vascular tumour growth in 3D: the roles of domain size and boundary conditions.
PloS One, 6(4):e14790, 2011.
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PP 5-13
Personalized liver function tests: A Multiscale Computational Model Predicts Individual
Human Liver Function From Single-Cell Metabolism
König, M.1,2, Marchesini, G. 3, Holzhütter, H. 2
Humboldt University, Institute for Theoretical Biology, Berlin, Germany
Charité Berlin, Institute of Biochemistry, Berlin, Germany
3
University of Bologna, Department of Internal Medicine, Bologna, Italy
1
2
Understanding how liver function arises from the complex interaction of morphology, perfusion, and metabolism from single
cells up to the entire organ requires systems-levels computational approaches. We report a multiscale mathematical model of
the Human liver comprising the scales from single hepatocytes, over representation of ultra-structure and micro-circulation
in the hepatic tissue, up to the entire organ integrated with perfusion. The model was validated against data on multiple
spatial and temporal scales. Herein we describe the model construction and application to hepatic galactose metabolism
demonstrating its utility via i) the personalization of liver function tests based on galactose elimination capacity (GEC), ii)
the explanation of changes in liver function with aging, and iii) the prediction of population variability in liver function
based on variability in liver volume and perfusion. We conclude that physiology- and morphology-based multiscale models
can improve the evaluation of individual liver function. Personalized prediction of liver function (GEC) was implemented in a
proof-of-principle app available at https://www.livermetabolism.com/gec_app/
A) Overview of detailed kinetic model of hepatic galactose metabolism in SBGN.
B) Tissue-scale model of the sinusoidal unit comprising diffusion and convection based transport of substances in the
sinusoid, diffusion-based transport of substances in the space of Disse and description of cellular metabolism via kinetic
models of individual hepatocytes. Blood coming from the hepatic artery and portal vein enters the sinusoidal unit periportal
and leaves pericentral. Transport between the sinusoid and the space of Disse occurs via fenestrations in the endothelial
cells. Parameters and references are provided in thesupplement.
C) Region of interests of the liver are modeled via the integration of multiple sinusoidal units based on the observed
heterogeneity of structural parameters and microcirculation within the lobulus.
D) Based on anthropomorphic information of subjects like age, gender, bodyweight and height the region of interests are
scaled to the observed distributions of liver blood flow and liver volume. Reference values of galactose clearance (GEC) are
calculated and the experimental value of GEC can be evaluated in this reference context. Based on available data on the
distribution of anthropomorphic features the population variability can be evaluated.
PP 5-14
Model-based predictions of inflammatory patterns in the breast lobular epithelium in
relation to epithelial cell turnover
Lopez Alfonso, J. C.1, Schaadt, N. 2, Wemmert, C. 3, Schönmeyer, Brieu, R.4, N.4, Forestier, G. 5, Feuerhake, F. 2, Hatzikirou, H.1
Center for Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Innovative Methods of Computing, Dresden, Germany
Institute of Pathology, Hannover Medical School, Hannover, Germany
3
Engineering science, computer science and imaging laboratory (ICube), Université de Strasbourg, Strasbourg, France
4
Definiens AG, Munich, Germany
5
Modelling, Intelligence, Process and Systems (MIPS), Université de Haute Alsace, Mulhouse, France
1
2
Question: Most of the factors and mechanisms that regulate the inflammatory microenvironment in breast lobular
epithelium are only partly understood. For instance, those related to lymphocytic lobulitis (LLO), a characteristic pattern of
inflammation characterized by lymphoid cells infiltrating lobular structures. Interestingly, abnormal inflammatory patterns
are not only detected adjacent to clinically manifest hereditary breast cancer, but can also be observed in prophylactically
removed non-neoplastic breast tissues from patients without or with familial genomic aberrations associated with a high
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cancer risk. This led to the hypothesis that LLO could be associated with genomic aberrations that impair DNA repair. Thus,
understanding the biological and functional role of inflammation is critical to evaluate whether alterations in the breast
function could associate with the development of cancer.
Methods: To gain new insights into the role of inflammation in the breast epithelium, we developed a dynamical multiscale
agent-based model for the interactions between immune and epithelial cells in lobular epithelium during the menstrual
cycle. Physiological model parameters were calibrated on the basis of spatial data extracted from digital whole-slide images
of immunohistochemical epithelial, vascular and immune cell markers. These images were acquired on clinical annotations
from a cohort of healthy patients who underwent reduction mammoplasty. To better align the mathematical model with
microscopic observations, we developed a modular workflow combining a convolutional neural network to detect regions of
interest with an auto-adaptive random forest pixelwise classifier to detect nuclei (“nucleus container” module; Definiens,
Germany). This immune cell analysis provided the quantitative read-out required as input data of the proposed model.
Results: We found that the immunological context as defined by the density, functional orientation and spatial distribution
of immune cells, provides valuable information to predict potential pathological scenarios. In addition, while epithelial
damage due to DNA repair defects was predicted to induce clustering patterns of immune cells, high hormone levels result
in spatial distributions where clusters were not present. The length of the menstrual cycle was found to be a crucial patientspecific factor shaping inflammatory responses to malignant changes in breast tissue.
Conclusions: Modeling results evidence that spatial correlations between infiltrating lymphocytes and personalized clinical
data may contribute to the extension of criteria for biopsy evaluation, supporting the development of novel prognostic
markers and opening new perspectives for immunomodulatory therapeutic interventions.
PP 5-15
Modeling disease Progression in Myeloproliferative Neoplasms, a Systems Medicine
Approach
Montazeri, M.1, Brehme, M.1, Schuppert, A.1, Koschmieder, S. 2, Brümmendorf, T. 2
1
2
Joint Research Center for Computational Biomedicine, Aachen, Germany
Department of Hematology, Oncology, Hemostaseology, and Stem Cell Transplantation, faculty of Medicine, RWTH Aachen, Aachen, Germany
Myeloproliferative neoplasms (MPN) are molecularly well-defined stem cell disorders, which can be studied to investigate
multi-step malignant transformation. Different MPN subtypes such as polycythemia vera (PV), essential thrombocytosis
(ET), and primary myelofibrosis (PMF) are characterized by eventual disease progression from a “benign” chronic state
towards myelofibrosis and leukemic transformation. The identification of biomarkers predictive of the individual disease
progression risk is crucial for therapy success, but challenged by the heterogeneity of the cell populations involved, the
complexity of the underlying mechanisms and their mutual interactions.
Our recent study on disease progression modelling in CML revealed that combining two patient-derived genomic scores,
CD34+ similarity and gene expression entropy, creates a direct link between patients and mechanistic disease models.
This link allowed estimating patients’ disease progression state with respect to disease evolutionary time as well as risk of
transition from chronic to malignant phase. We are extending this concept to MPNs in order to develop predictive models
for the onset of instability during early-stage disease, which leads to progression to more developed stages. To develop such
prognostic biomarkers we are studying the differences between genetic and epigenetic properties in early vs. late MPNs,
including the direct comparison to clinical parameters. Our goal is to translate these findings into a predictive mathematical
model of disease progression.
We will perform comparative omics analyses, including gene expression array, NGS, Chip-seq and genome-wide DNA
methylation screens using clinical biopsies obtained from patients in the MPN-SAL registry. Preliminary analyses of publicly
available data comparing patient whole blood vs. peripheral blood neutrophil gene expression by principle component
analysis (PCA) resolved differential gene expression between ET and PV, as surrogates for “early” MPN, versus PMF as a “late”
MPN. Comparing PMF vs. controls in whole blood, > 400 genes are separated from residual space of PCA compared to only
~150 in neutrophils. We hypothesize that these differences indicate putative population dynamic effects reminiscent of those
observed during CML progression. In parallel we are investigating clinical parameters to find a set of variables capable of
classifying different MPNs and time-wise prediction of transformation from chronic to acute stages.
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PP 5-16
Development of a Multiscale Systems Biology Approach to Study Atherosclerotic Plaque
Progression in WTLdlrKO and M-S196ALdlrKO mice: Integrating Mathematical and Murine
Models
Pichardo-Almarza, C.1,2, Gage, M. 3, Pineda-Torra, I. 3, Diaz-Zuccarini, V.1,2
UCL, Mechanical Engineering, London, Great Britain
UCL, Institute of Biomedical Engineering, London, Great Britain
3
UCL, Centre for Clinical Pharmacology, London, Great Britain
1
2
Main objective of the study. Risk factors for atherosclerosis (ATH) have been extensively studied over the last two decades
using murine models in order to understand disease mechanisms. The aim of this work is to develop a Systems Biology
approach to understand atherosclerotic plaque (ATHP) progression in WT LDLR-KO and M-S196A LDLRKO mice. A multiscale
modelling approach is proposed, where different biological scales are used to dynamically simulate and understand different
aspect of the disease.
Materials and Methods. Diet induced ATH: Mice were fed Breslow Western diet (21% fat from milk, supplemented with
0.15% wt/wt cholesterol) for 12 weeks from 8 weeks of age to induce ATH. Metabolic tests: Plasma total cholesterol was
determined via colorimetric assay. Plasma HDL and LDL/VLDL levels were measured via quantitative colorimetric assay.
Systemic inflammation: Plasma MCP-1 was determined by enzyme-linked immunoassay. ATH: Mice were perfusion-fixed
with phosphate-buffered paraformaldehyde (4% [wt/vol.], pH 7.2) under terminal anaesthesia. The entire aortic tree was
dissected free of fat and other tissue. Aortae were stained with oil red O and mounted on a glass slide before imaging under
a dissection microscope. Aortic root (AR): Hearts were paraffin embedded and 5 μm AR sections were stained with H&E.
Mathematical Modelling and Simulation: The multiscale mathematical model used is based on previous work proposed
for humans [1] where temporal evolution of inflammatory processes and ATHP progression are modelled using differential
equations. Parameter fitting was made via literature search and data provided by the experimental team. The model was
implemented in Matlab®.
Results. Preliminary results from the model match reasonably well the data reported from experiments: AR of
M-S196A LDLRKO mice showed higher plaque coverage (around 14%) with respect to WT LDLR-KO mice (11% coverage) after 20
weeks. Two set of parameters were used to simulate different types of mice, allowing to introduce new variables in the model
related to diet, lipid levels (e.g. LDL-cholesterol) and how the ATHP growth is finally affected by these variables.
Conclusion. These first results are encouraging as this approach helps to quantitatively understand biological mechanisms
in ATHP progression in WT LDLR-KO and M-S196A LDLRKO mice. The disease was presented as a dynamic process (i.e. evolving
in time) and the model helped understanding how different physiological mechanisms and parameters are related in the
organism as a whole. New in-silico experiments are being designed in order to help to design new experiments with the
murine models.
References
[1] Pichardo-Almarza C, Metcalf L, Finkelstein A, Diaz-Zuccarini V. Using a Systems Pharmacology Approach to Study the
Effect of Statins on the Early Stage of Atherosclerosis in Humans. CPT Pharmacomet Syst Pharmacol. 2014 Dec 30;4(1):4150.
PP 5-17
3D multiscale modeling of vascularized tumor development inside colon
Shchekinova, E.1, Galliani, S.1, Joshi, T.1, Reuss, M.1
1
Stuttgart Research Center Systems Biology, Stuttgart, Germany
Main objectives: The aim of our study was to model the growth of tumour and vasculature within colon tissue using
particular setup of vasculature and an initial configuration of the miscroscopic tissue that resembles the polyp structure
within colonic cript.
Methods: We used three-dimensional multiscale model [Perfahl2011,Galliani2015] of vascular tumour growth. To study the
impact of choice of initial vasculature and density of tumour cells on the subsequent evolution of tumour within tissue we
performed a set of stochastic simulations with variable intial density and uniform distibution of vessels within tissue. In
particular, the angiosenesis and vasculature formation were compared for simulations using Gaussian density distribution
of initial tumour cells within heathly tissue. Modeling of tumour development inside the colon tissue was performed using
colon polyps associated with tubular and villous structure.
Results: To get an idea about vasculature initiation we neglected epithelial dynamics but looked at the development of
vascularized tumour on a larger scale. For this purpose a small tumour was implanted into tissue with developed vasculature
network. The initial vasculature network was modelled by taking different initial three--dimensional geometry that resemble
the colon polyp configuration. We investigated the sensitivity of vasculature development on chemotaxis coefficient for
the edothelial sprout. The higher density of the vascular network in this case provided better conditions for the subsequent
tumour spread after the tumour implantation. The tumour stimulates new sprouting and migration of endothelial cells inside
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it. Initially the degradation of a healthy tissue was observed, at a later time the model predicted fast development of tumour
with a final invasion into muscularis mucosa layer.
Conclusion: Simulation results for two different initial setups showed similar behavior of tumour proliferation. Stochastic
simulations for 3D slices of tissue showed that while proliferation rates of tumour cells were similar across different
realizations, large variability was observed in propagation of the tumour at boundary layer.
Reference
Galliani et al., 2015. From preclinical biological models to human colon cancer: A contribution from mathematical
modeling. Poster at the 2nd International Conference on Computing, Mathematics and Statistics (iCMS2015) doi: 10.13140/
RG.2.1.4415.3686
Perfahl et al, 2011. Multiscale Modelling of Vascular Tumour Growth in 3D: The Roles of Domain Size and Boundary
Conditions. PloS ONE 6(4),e14790. doi:10.1371/journal.pone.0014790.
PP 5-18
Interplay of nucleosome positioning, covalent modifications and transcription factor binding
Teif, V.1
University of Essex, Colchester, Great Britain
1
Binding of transcription factors (TFs) to the genome is a central determinant of gene regulation. In higher eukaryotes, it is
dependent on both the DNA sequence and the chromatin environment. On the other hand, differential binding of special
TFs, such as a CCCTC-binding protein (CTCF), can dramatically affect 3D chromatin structure. Thus, two cells of the same
organism, containing the same genome, can have very different TF binding depending on the cell state, which determines
differences in their gene expression. In several ongoing projects we have narrowed down the problem of predicting celltype specific TF binding by focusing at a single protein CTCF. CTCF represents a prototypic case of a context-dependent
DNA-interacting protein. While it can act as a tumour suppressor, its overexpression can also favour tumour progression in
breast cancer. In many cases, CTCF exerts its function by demarcating boundaries between thousands of chromatin domains
and establishing 3D chromatin structures through DNA loop formation. Large fraction of cell-type specific variability in
CTCF binding to the several thousands of its potential binding sites in the human genome is linked to changes in DNA
methylation and nucleosome repositioning. However, in most cases differential DNA methylation is not the cause, but rather
a consequence of the differences in CTCF binding, while the cause of CTCF binding differences in some cases is linked to
covalent modifications of CTCF itself. I will provide an update about our recent results and current understanding of this
system.
References
Pavlaki I. , Docquier F., Chernukhin I., Teif V. B., Klenova E. (2016). Poly(ADP-ribosyl)ation dependent changes in CTCF DNA
binding and gene expression patterns. In preparation
Teif V.B., Beshnova D.A., Marth C., Vainshtein Y., Mallm J.-P., Höfer T., Rippe K. (2014). Nucleosome repositioning links DNA
(de)methylation and differential CTCF binding during stem cell development. Genome Research 24, 1285-1295.
Beshnova D.A., Cherstvy A.G. Vainshtein Y., Teif V.B. (2014). Regulation of the nucleosome repeat length in vivo by the DNA
sequence, protein concentrations and long-range interactions. PLoS Comput. Biol. 10(7):e1003698.
Teif V.B., Vainshtein Y., Caudron-Herger M., Mallm J.-P., Marth C., Höfer T., Rippe K. (2012) Genome-wide nucleosome
positioning during embryonic stem cell development. Nature Struct. Mol. Biol. 19, 1185-92.
PP 5-19
Modelling of tertiary lymphoid organ development in the context of kidney transplant.
Uvarovskii, A.1, Schaadt, N. 2, Feuerhake, F. 2, Meyer-Hermann, M.1,3
Helmholtz Centre for Infection Research, Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Braunschweig, Germany
Institute for Pathology, MHH, Hannover, Germany
3
Institute for Biochemistry, Biotechnology and Bioinformatics, TU Braunschweig, Braunschweig, Germany
1
2
Question: Tertiary lymphoid organs (TLO) are organized lymphoid structures which develop at locations other than
lymphoid tissue. A matured TLO consists of adjacent T and B cell zones, follicular dendritic cell (FDC) network and may
even include developed germinal centres. TLOs may be found at sites of chronic inflammation, for example, in transplanted
organs. Mouse transplantation models showed that the presence of TLO in skin grafts increased the risk of rejection. Herein,
we consider TLO development in the context of transplanted kidney, where we observed lymphocyte clusters adjacent to
blood vessels in human biopsy samples and would like to understand dynamic implications of the snapshots seen in the
biopsies for the long-term graft outcome and treatment decisions.
Methods: Since key processes in the system are guided by molecule interactions, what includes diffusion and binding
kinetics, they may be described by mathematical modelling. We developed spatio-temporal agent-based model which consists
of several levels of abstraction: cell movement and collision detection, chemokine diffusion and receptor dynamics at the cell
98
surface. B cells are represented as spheric agents performing chemotactic migration towards sources of CXCL13, a chemokine
secreted by activated stroma cells (FDCs). FDCs were shown to derive from perivascular precursors upon lymphotoxin
receptor interaction, probably, with B lymphocytes. In our model stroma cells measure an hypothetic signal based on
contacts with B cells and start to secrete CXCL13 as soon as they sense the threshold signal value. CXCL13 binds to receptors
on B cells, which are being internalized, resulting in chemokine consumption and change in surface receptor number. Hence,
CXCL13 distribution is described by the diffusion equation coupled to receptor dynamics.
Results: We estimated receptor densities and secretion rates necessary for B cell attraction for cases of a single FDC and for
an FDC cluster and what maximal lymphocyte cluster size can be generated by this systems. We show that for rates in the
literature range, chemokine degradation does not influence its gradient at the distances of TLO size order.
Conclusion: The simulations may be used in adjustment of anti-chemokine therapy for destabilizing cell clusters. Biopsy
comparison may reveal critical B cell density for stroma activation and will help to estimate probability of outcomes.
PP 5-20
Parallel analysis of the proteome, phosphoproteome and N-terminome to characterize
altered platelet functions in the human Scott syndrome
Solari, F.1, N. Mattheij, J. A. 2, Burkhart, J. M.1, Swieringa, F. 2, Sickmann, A.1, Heemskerk, J. W. M. 2, Zahedi, R.1
1
2
ISAS , Dortmund, Germany
CARIM, Maastricht, Netherlands
The Scott syndrome is a very rare and likely under-diagnosed bleeding disorder associated with mutations in the gene of
anoctamin-6. Platelets from Scott patients are impaired in various Ca 2+-dependent responses including phosphatidylserine
exposure, membrane ballooning and intracellular protein cleavage, resulting in an impaired procoagulant response. Owing
to its central role in thrombosis and hemostasis, we aimed to improve our understanding of the molecular mechanisms of
the procoagulant response by studying Scott platelets using quantitative proteomics. We hypothesized that alterations in
protein expression, protein phosphorylation, and proteolytic cleavage may contribute to this complex phenotype. Thus, we
applied a multipronged iTRAQ-based proteomics to quantify changes between healthy controls and Scott patients (i) in the
platelet proteome, as well as upon platelet activation in (ii) the phosphoproteome and (iii) N-terminome to identify Calpain
substrates.
Only a limited number of proteins had decreased (70) or increased (64) expression in Scott platelets. Parallel reaction
monitoring confirmed that anoctamin-6 was absent, while interestingly aquaporin-1 was highly upregulated. Quantification
of 1,574 phosphopeptides revealed major differences between Scott and control platelets after stimulation with thrombin/
convulxin or ionomycin. In the patient platelets phosphorylation levels were increased in multiple proteins regulating the
cytoskeleton or signaling events. Moreover, 1,596 terminal peptides were quantified in activated platelets, 180 of which were
confirmed to be calpain-regulated, whereas a distinct set of 23 neo-N-termini was identified as caspase-regulated. Notably, in
Scott platelets calpain-induced cleavage was down-regulated, including cytoskeleton-linked and signaling proteins, which is
in accordance with the increased phosphorylation state.
Thus, sensitive multi-pronged quantitative profiling of the proteome, phosphoproteome and N-terminome provided multilayer insights into aberrant signaling in Scott syndrome that can be related to the partial protection of Scott platelets to
the detrimental Ca 2+-dependent cytoskeleton and membrane changes that are normally associated with phosphatidylserine
exposure.
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PP 6: Systems Medicine & Systems Pharmacology
PP 6-01
SIMPLEX: a combinatorial multimolecular omics approach for systems biology
Ahrends, R.1, Coman, C.1
1
ISAS, Lipidomics, Dortmund, Germany
Question - Interconnected molecular networks are at the heart of signaling pathways that mediate adaptive plasticity of
eukaryotic cells. To gain deeper insights into the underlying molecular mechanisms, a comprehensive and representative
analysis demands a deep and parallel coverage of a broad spectrum of molecular species.
Methods - Therefore, we introduce SIMPLEX (SImultaneous Metabolite, Protein, Lipid EXtraction procedure), a novel
strategy for the quantitative investigation of lipids, metabolites and proteins. Compared to unimolecular workflows,
SIMPLEX offers a fundamental turn in study design, since multiple molecular classes can be accessed in parallel from one
sample with equal efficiency and reproducibility.
Results - Application of this method in mass spectrometry based workflows allowed the simultaneous quantification of
360 lipids, 75 metabolites and 3327 proteins from 10 6 cells. The versatility of this method is shown in a model system for
adipogenesis - PPARG signaling in mesenchymal stem cells - where we utilized SIMPLEX to explore cross-talk within and
between all three molecular classes and identified novel potential molecular entry points for interventions, indicating that
SIMPLEX provides a superior strategy compared to conventional workflows.
Conclusions - The identification of a close collaboration between the PPARG signaling on the protein level and TAG
metabolism on the metabolite level proves our initial motivation to investigate the system within the cellular context rather
than a restricted single-sided way and allowed for the elucidation of a further level of feedback based PPARG control derived
from the lipid level. We established a protocol that can deliver a representative picture of an entire biological system by
extending the detection capabilities to more than one molecular class. As mass spectrometry instrumentation continuously
advances regarding sensitivity and speed, we expect that this novel workflow will soon accommodate more quantitatively
accessible molecular species and will deliver new exiting perspectives into metabolic disorders.
PP 6-02
Baseline Matters: the Effect of Initial Immune State on Outcome of Septic Patients
Alpert, A.1, Mansour, A. 2, Starosvetsky, E.1, T. Sweeney, E. 3, Menck,K. 2, Hinz, J. 2, Khatri, P. 3, Shen Orr, S.1
Technion, Haifa, Israel
Georg-August-University, Anaesthetics, Goettingen, United States
3
Stanford, School of Medicine, Stanford, United States
1
2
Sepsis is a severe life-threatening systemic inflammatory response caused by infection that is responsible for more than
250,000 deaths annually in the US. The immune response in sepsis is composed of two opposing components, namely
immune activation followed by immune suppression, which is dominated by T-cell exhaustion. In spite of wide variation in
the immune system between individuals, the effect of the initial immune state on outcome in sepsis is still unknown.
Using CyTOF, we monitored at high resolution the peripheral-blood dynamics of septic patients, including abundances of
cell subsets and functional signaling responses to IL-6 stimulation. We identified major shifts in the T-cell compartment
which occur gradually during sepsis, including an increased expression of inhibitory receptors and a decreased expression
of co-stimulatory molecules, both hallmarks of T-cell exhaustion. Exhausted CD8+ T-cell subsets responded poorly to IL-6
stimulation, reflecting a functional defect of exhausted T-cells in sepsis, and suggesting baseline levels of exhausted cells
may affect outcome. To test this, we computed a T-cell exhaustion enrichment signature at baseline hospitalization and over
time in a large gene expression dataset of trauma patients that contracted nosocomial infections. We identified a significant
correlation of severity and baseline exhaustion enrichment, measured less than one day following trauma. Our findings
highlight the role of initial immune state on determination of clinical outcome and suggest that the baseline immune-state
variability may be a useful diagnostic tool for identification of septic patients at risk.
100
PP 6-03
Dynamics of the tumor-infiltrating lymphocyte repertoire in melanoma and pancreatic
cancer
Appel, L. M.1,2, Poschke, I. 3, Faryna, M.4, Hassel, J. 5, Strobel, O.6, Diken, M.7, Kranz, L. M.7, Offringa, R. 3, Höfer, T.1,2, Floßdorf, M.1,2
German cancer research center (DKFZ), Theoretical Systems Biology, Heidelberg, Germany
BioQuant Center, University of Heidelberg, Heidelberg, Germany
3
German cancer research center (DKFZ), Molecular Oncology of Gastrointestinal Tumors, Heidelberg, Germany
4
BioNTech Diagnostics, Mainz, Germany
5
Heidelberg University Hospital, Department of Dermatology, National Center for Tumor Diseases (NCT), Heidelberg, Germany
6
Heidelberg University Hospital, General, Visceral and Transplantation Surgery, Heidelberg, Germany
7
Institute for Translational Oncology (TrOn), Mainz, Germany
1
2
In recent years immunotherapeutic approaches with the goal to harness the patient’s T cell response have become the
standard therapy for many cancers. Still, the unpredictability of the individual response to a particular immunotherapeutic
approach calls for a better understanding of the underlying mechanisms informing the identification of biomarkers for
patient stratification. To further our mechanistic insights, we are devising a mathematical model of the anti-tumor T cell
response in the B16-OVA melanoma mouse model. Proliferation monitoring and accurate cell counting in all relevant
compartments (tumor, blood, draining lymph nodes, spleen) in conjunction with the mathematical model allows us to
quantify the time-dependent proliferative activity of the tumor-infiltrating lymphocytes and their migration behaviour. In
parallel, we are investigating next generation sequencing data of the repertoire of tumor-infiltrating T cells in melanoma and
ductal adenocarcinoma (PDA) patients. We have optimized the sequencing protocol for our purposes, such that it provides
an accurate, quantitative representation of the clone sizes. For melanoma, immune therapy is often successful and we are
using these data to identify potential biomarkers predicting clinical outcome. Preliminary results of our analysis of the TIL
repertoire in PDA patients indicate similarities to that in melanoma, suggesting a hitherto unappreciated tumor-reactivity
of the infiltrating lymphocytes; immunotherapeutic interventions like adoptive T-cell therapy might hence be a treatment
option also in this type of cancer.
PP 6-04
Markov-Chain Monte-Carlo methods to analyze mechanistic disease simulators:
Applicability and shortcommings
Ballnus, B.1, Müller, C. 2, Hatz, K. 3, Hug, S.1, Weyßer, F. 3, Theis, F.1, Schuppert, A. 2, Hasenauer, J.1, Görlitz, L. 3
Institute of Computational Biology, Helmholtz-Zentrum München, Neuherberg, Germany
RWTH Aachen, AICES Graduate School, Aachen, Germany
3
Bayer Technology Services, Leverkusen, Germany
1
2
Main Objectives: Disentangling the complex interplay between development of pathophysiological and drug-induced
changes on clinical phenotypes by mechanistic models is a powerful approach towards personalized therapies of complex
diseases.
For pharmacokinetics of drugs this paradigm was already successfully developed (e.g. [1]). Pharmacodynamic approaches
still fail as mechanistic disease models typically contain complex dynamic features hindering applicability of developed
approaches.
In this case-study, we examine the applicability of Bayesian inference and Markov-Chain Monte-Carlo (MCMC) methods
for two mechanistic models. The first model is a cardio-vascular whole body model (CV), which models human in vivo
mechanisms of blood flow & pressure on different time scales (based on [2,3]). The second application is a blood coagulation
model (BC), which aims to predict the coagulation behavior (based on [4]).
Materials & Methods: Both models are described by large ordinary differential equation systems. Model parameters,
their variability and uncertainty have to be inferred from experimental data. The experimental data contain measurements
of physiological values in humans for CV and total Thrombin in human blood plasma mixtures for BC. Inference of the
parameters and their distribution from the models is done using MCMC algorithms (e.g. [5]).
Results: Model analysis indicates that both simulators possess strong non-linear behavior, bifurcations and periodic or
a-periodic dynamics. These properties are shown to lead to instabilities of common MCMC approaches. We show that a
preceding optimization leads to more robust estimation results. Best results were obtained for BC using classic MetropolisHastings (MH) in combination with convex priors and a locally estimated covariance. However, our posterior approximations
revealed multiple, weakly separated modes.
In addition, our CV related analysis revealed that periodic solutions can induce multi-modalities. Furthermore, our results
show that bifurcations have less impact onto MCMC algorithms then initially expected. In analogy, the impact of multistabilities seems negligible. However, systems with separated, approximately equally weighted modes are only targetable by
more sophisticated multi-chain approaches as Parallel Tempering or Parallel Hierarchical Sampling.
Conclusions: We have confirmed the existence of practical limits using classic MCMC approaches in real-life applications.
This prevents us from inferring parameter densities within finite time. More sophisticated MCMC methods or augmentations
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based on previous optimization are necessary to reliably approach these problems. The latter improvement has been
successfully applied in previous pharmacokinetic studies and our BC results without major additional computational costs.
PP 6-05
Modeling biliary fluid dynamics reveals possible mechanism for dose-response and
personalization of UDCA treatment in PSC
Ostrenko, O.1, Segovia-Miranda, F. 2, Brosch, M. 3, Erhart, W.4, Meyer, K. 2, Kretzschmar, G. 5, Jüngst, C.6, Lammert, F.6, Zerial, M. 2, Schafmayer, C.4,
Brusch, L.1, Hampe, J. 3
Technische Universität Dresden, Center for High Performance Computing (ZIH), Dresden, Germany
Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
3
Technische Universität Dresden, University Hospital, Dresden, Germany
4
University Hospital Schleswig Holstein, Kiel, Germany
5
Technische Universität Dresden, Molecular Cell Physiology and Endocrinology, Institute of Zoology, Dresden, Germany
6
Saarland University Medical Center, Homburg, Germany
1
2
Background and Question: Primary sclerosing cholangitis (PSC) is a progressive liver disease characterized by
fibroobliterative destruction of the intra- and/or extra-hepatic bile ducts. Combined with immune-mediated insults to the
bile duct, biliary flow obstruction might lead to pressure damage to the biliary epithelium and may drive further disease
progression. Currently, the only and controversial medical treatment is the choleretic drug ursodeoxycholic acid (UDCA).
However, while moderate doses (10-15mg/kg/day) of UDCA might improve liver function tests and histology, high-dose
UDCA has been demonstrated to increase mortality in a RCT, thereby calling UDCA treatment in question.
Objective: Develop a hydrodynamic model of biliary pressure and flow to assess the effect of UDCA on biliary pressure in
normal liver and PSC.
Methods: A recently developed theory of bile secretion and transport was applied to UDCA treatment. UDCA reduces bile
viscosity (1) and increases solute concentration and consequently osmotic water influx into bile (2). Model parametrization
was completed using measured viscosity from gallbladder bile of patients treated with UDCA and quantitative
3D-reconstruction of stacked high resolution microscopy of healthy human and PSC livers. Bile viscosity and osmotic water
inflow are modelled as functions of UDCA dose according to effects (1) and (2) above.
Results: This quantitative model allows the assessment of the mechanical consequences of UDCA treatment in a dosedependent manner and predicts intrahepatic fluid pressure as a function of the physical properties of bile and the
geometrical properties of the tissue. For healthy liver, the model reproduces the insensitivity of biliary pressure to UDCA
dose. In PSC, a pronounced dose-response curve is predicted by our model: biliary pressure is decreasing at UDCA dose
increments, reaches a minimum and then rapidly increases beyond baseline upon further dose increments. The optimal
UDCA dose corresponding to the maximum pressure reduction can be quantified as a function of the physical and
geometrical parameters that may vary from patient to patient.
Conclusions: This study provides a hydrodynamic explanation of the clinical dose-response of UDCA in PSC. Further,
it confirms tolerance to high doses of the compound in normal liver. Mechanistically, the UDCA-induced increase in
water inflow has pressure-lowering (bile dilution) and pressure-increasing (increased fluid volume in need of drainage)
consequences. Thus, the model advocates individualized dose-optimization based on the patient-specific biliary microgeometry, thereby potentially rendering UDCA treatment safer and more widely applicable.
PP 6-06
Improving non-invasive liver function diagnostics
Bulik, S.1
Charité - Universtätsmedizin Berlin, Institut f. Biochemie / Computational Biochemistry, Berlin, Germany
1
The liver is the central organ of detoxification and metabolic homeostasis while at the same time exposed to pathogenic
and toxic challenges due to its position in the body. A peculiarity of liver diseases is their often silent progression without
serious clinical symptoms until they reach a threshold stage characterized by a sudden and often life-threatening onset of
severe liver failure. Improving liver diagnostics will help to detect the developing decline of liver function and enable the
application of required therapies.
We propose an adaptation of existing CO2 based breath-tests to individual bicarbonate metabolism to improve the
sensitivity of the tests. Substances containing stable isotope labeled 13C components like methacetin or galactose are applied
in clinical routine to assess the liver function by the measurement of 13CO2 expiration. The impact of the individual CO2
distribution in the body is hitherto neglected. We developed the 2DOB test where this effect is separately measured and by
computational modelling the functional liver test is purged from this influence.
Furthermore, the combination of functional tests with structural data from magnetic resonance elastography (MRE) will
102
provide a tool to develop a spatially resolved 3D-liver function model generating a means to support clinical assessment of
liver disease severity and control of disease progression.
PP 6-07
Integrative analysis of chemical high-throughput screens uncovers novel biological
information.
Liu, X.1, Adornetto, G.1, Campillos, M.1
1
Helmholtz Zentrum Munich, Institute of Bioinformatics and System Biology, 1, Germany
Objectives of the study: Chemical high-throughput screens (HTS) have been extensively used by pharmaceutical
companies, as well as by academic research groups to identify compounds with activity on protein targets or with specific
phenotypic activity. Recently, an extensive number of these screens have been deposited on public databases such as
PubChem and ChemBank. Concurrently, large databases of drug targets have appeared in the public domain. This explosion
of chemical information in the public sector is enabling the integration of information from HTS and more important, its
systematic exploration. We have developed computational methods for the integration of chemical HTS, applied them to the
ChemBank repository and interrogate the resulting resource. In this work, we illustrate the capabilities of this integrative
resource to uncover novel biological information with an example showing that chemical screens sharing selective hits
uncover novel biological relationships.
Methods: To enable the integration of disparate chemical HTS, we have improved existing computational methods for the
systematic identification of hits in chemical screens, developed the target prediction tool HitPick, and created filters to
remove promiscuous compounds. In order to compare the biological activities measured in the screen, we have assigned Gene
Ontology categories to the screens and constructed chemical hit profiles with sets of compounds differing on their selectivity
level for 1640 screens of ChemBank repository.
Results: We hypothesized that if the same set of active compounds modulates two apparently unrelated biological processes,
these processes are likely to be connected through the common targets of the compounds and thus, may be unexpectedly
related. We have tested and proved the hypothesis using the set of 1640 chemical genetic assays stored in ChemBank.
We showed that assays that share hits also share biological associations through common protein targets. We suggested
novel biological activities of known drug targets that represent potential targets for the treatment of cancers such as the
Cannabinoid receptor 2.
Conclusion: With this study we demonstrated the potential of integrative computational approaches applied to chemical
HTS to uncover novel biological information.
PP 6-08
A NAT2 Pharmacogenomic based PBPK model of Isoniazid in Men and Its Application in
Adjusting Tuberculosis Chemotherapy
Cordes, H.1, Thiel, C.1, Baier, V.1, Blank, L.1, Kuepfer, L.1
1
RWTH, iAMB, Aachen, Germany
Due to its greatest early bacterial activity among fist-line anti-tuberculosis agents, isoniazid plays an essential role
in tuberculosis treatment. Polymorphisms in the NAT2 gene encoding for the N-acetyl transferase 2 cause a trimodal
distribution of isoniazid pharmacokinetics (PK) among human populations, categorized into slow, intermediate and fast
acetylators. Success of isoniazid chemotherapy is hence associated with acetylator status and general health state of a patient.
Still, the WHO recommended standard dose is administered regardless of acetylator type or immune status of patients.
Adverse effects during isoniazid therapy occur in about 5 % - 33 % of all patients. Clinical studies show that slow acetylators
have a higher risk to develop drug-induced toxicities, while lower treatment efficacies are found for fast acetylators and
immune deficient patients. To mechanistically assess this trade-off, we developed a physiologically based pharmacokinetic
(PBPK) model to describe the NAT2-dependent PK of isoniazid and five of its metabolites. The comprehensive PBPK model
was combined with a pharmacodynamics (PD) model, describing the anti-bacterial drug effect on mycobacterial growth in
the lung. The resulting PBPK/PD model allowed the simultaneous consideration of treatment efficacy on the one hand and
exposure to toxic metabolites in off-target organs causing drug induced side effects on the other. Aim of the study hence was
to optimize the trade-off between treatment efficacy and drug induced toxicity during isoniazid chemotherapy. To this end,
the developed PBPK/PD model was used to evaluate various isoniazid dose regimes in NAT2 specific immune competent and
immune deficient virtual populations. Our simulation results suggest substantial dose adjustments for all acetylator types.
We found bisecting the standard treatment for slow acetylators would substantially reduce the probability of adverse effects
during isoniazid therapy, while maintaining high treatment efficacies. Especially, intermediate and fast acetylators would
benefit from a combination of increased isoniazid doses and switching to a bi-daily administration regimen resulting in
reduced treatment failures without serious gain of drug induced side effects.
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PP 6-09
Validation of a pregnancy population physiologically-based pharmacokinetic model for
renally cleared drugs
Dallmann, A.1, Ince, I. 2, Meyer, M. 2, Eissing, T. 2, Hempel, G.1
University of Münster, Münster, Germany
Bayer Technology Services, Systems Pharmacology CV, Leverkusen, Germany
1
2
Main objective: To validate a physiologically-based pharmacokinetic (PBPK) model for the prediction of pharmacokinetics
(PK) of renally cleared drugs in populations of pregnant women at different stages of pregnancy.
Methods: Based on a recent literature review on anatomical and physiological changes during pregnancy [1], a pregnancy
population PBPK model has been developed using PK-Sim®/MoBi® [2]. In this model, the standard model structure of an
adult woman was extended by 9 physiological compartments. These compartments are either specific to pregnancy (e.g.
placenta and fetus) or become of specific relevance during pregnancy (uterus and breasts). The impact of the model structure
extension was evaluated by comparing the simulated PK of virtual compounds using the standard model and the pregnancy
model at pregnancy day zero. To ensure smooth transition from the non-pregnant to pregnant state, organs and blood flows
at the onset of pregnancy were scaled to the non-pregnant levels. Populations of pregnant women were created using the
organ scaling approach implemented in PK-Sim® [3]. The pregnancy population PBPK model was applied to predict the PK of
predominantly renally cleared drugs at different stages of pregnancy. Prediction results were evaluated by comparison with
experimentally observed literature data.
Results: The extension of the model structure had only a negligible effect on the simulated PK of pregnant women at
pregnancy day zero. The pregnancy population PBPK model successfully predicted the PK of all drugs at different stages of
pregnancy. Compared to the non-pregnant state, maximum clearance changes of these drugs were observed in the early
2nd trimester with an increase of approximately 50%. The differences declined towards delivery, approximating values
comparable to non-pregnant clearance levels. No changes in the activity of renal transporters involved in the clearance were
necessary to correctly predict the experimentally observed PK. This indicates that the activity of these transporters remains
essentially constant throughout pregnancy.
Conclusion: We successfully developed and validated our pregnancy population PBPK model at different stages of gestation
for predominantly renally cleared drugs. PK changes in pregnant women could be fully attributed to pregnancy-related
changes in relevant parameters such as kidney volume and perfusion. Ultimately, this model can be applied to investigate in
silico the PK of renally cleared drugs and help design clinical trials in this vulnerable special population.
References:
[1] Dallmann, A., 2015, PAGE 24, Abstr 3456.
[2] Eissing, T. et al. (2011). A computational systems biology software platform for multiscale modeling and simulation:
integrating whole-body physiology, disease biology, and molecular reaction networks. Frontiers in physiology 2: 1-10.
[3] Willmann, S. et al. (2007). Development of a physiology-based whole-body population model for assessing the influence of
individual variability on the pharmacokinetics of drugs. Journal of pharmacokinetics and pharmacodynamics 34(3): 401-431.
PP 6-10
A hypothesis-free, systemic approach to identify pharmacological targets for anxiety
disorders
Nussbaumer, M.1, Maccarrone, G.1, Teplytska, L.1, Murphy, M. 2, Asara, J. 3, Landgraf, R.1, Turck, C.1, Filiou, M.1
Max Planck Institute of Psychiatry, Munich, Germany
MRC Mitochondrial Biology Unit, Cambridge, Great Britain
3
Harvard Medical School, Boston, United States
1
2
Main Objectives: Anxiety disorders are the most prevalent psychiatric diseases worldwide. Since a third of the patients do
not respond to existing anxiolytic treatments, hypothesis-free approaches to identify novel drug targets are necessary for
improved response rates and personalized therapeutics.
Materials and Methods: We developed a hypothesis-free, systems biology platform based on quantitative proteomics (15N
metabolic labeling and mass spectrometry), metabolomics and bioinformatics to identify differences in mice selectively bred
for high vs. low anxiety. We then pharmacologically manipulated the identified differences in high anxiety mice in vivo with
appropriate compounds to assess whether selective pharmacological targeting exerts an alleviating effect in high anxiety.
Results: Our hypothesis-free approach revealed altered brain mitochondrial pathways in high vs. low anxiety mice,
including oxidative phosphorylation, oxidative stress and mitochondrial import/transport. These mitochondrial pathways
were selectively manipulated in vivo by MitoQ, a mitochondria-targeted antioxidant. We observed a decreased anxietyrelated behavior in MitoQ-treated compared to untreated high anxiety mice. We then analyzed the molecular correlates of
this anxiolytic effect with targeted metabolomics, immunoassays and biochemical assays.
Conclusion: This is the first time that a hypothesis-free, mechanism- rather than symptom-based approach is used to
104
manipulate a behavioral phenotype. Our findings emphasize the potential of systems biology-driven approaches to discover
new pharmacological treatments and reveal the therapeutic potential of mitochondrial targeting for brain disorders.
PP 6-11
The NormSys registry for modeling standards in systems and synthetic biology
Golebiewski, M.1, Nikolaew, A.1, Woetzel, N.1, Zander, J.1, Hollmann, S. 2, Mueller-Roeber, B. 2, Regierer, B. 3
HITS gGmbH, Heidelberg, Germany
University of Potsdam, Focus Area of Plant Genomics and Systems Biology, Potsdam, Germany
3
LifeGlimmer GmbH, Berlin, Germany
1
2
The rapid development of modern life science technologies allows data generation with increasing speed and complexity. In
systems biology these data have to be stored, shared, processed, integrated, analyzed and compared. Hence, standards for
formatting and describing experimental data, applied workflows and resulting computer models have become a critical issue.
Different stakeholders need to be engaged in the standardization process to incorporate their specific requirements:
Researchers form academia and industries with their grass-roots standardization communities like the Computational
Modeling in Biology Network (COMBINE: http://www.co.mbine.org), as well as representatives of standardization bodies
(e.g. German DIN, European CEN/CENELEC or the International Organization for Standardization ISO), scientific journals
and research funding agencies. The project NormSys, funded by the German Federal Ministry for Economic Affairs and Energy
(BMWi) aims at enhancing and promoting the formal standardization of existing modeling community standards by building
a bridge between stakeholder groups and developing the means for transferring information about community standards
between them.
To survey standard formats for computational modeling in systems biology and related fields we have developed the webbased NormSys registry for modeling standards (http://normsys.h-its.org). It provides a single access point for
consistent information about model exchange formats such as the Systems Biology Markup Language (SBML), CellML, the
Systems Biology Graphical Notation (SBGN), the Simulation Experiment Description Markup Language (SED-ML), the Synthetic
Biology Open Language (SBOL), NeuroML for neuroscience models, the Pharmacometrics Markup Language (PharmML)
and others. The publicly available platform not only lists the standards, but also compares their major features, their
possible fields of biological application and potential use cases (including model examples), as well as their relationships,
commonalities and differences. This NormSys registry provides a common entry point for anyone interested in modeling
standards, especially for experimentalists, modelers and software developers who plan to apply the standard formats
for their respective case of application, and serves them with detailed information, as well as with links to the webpages,
specifications and web services of the formats.
PP 6-12
Systems biology of MHC class I antigen presentation studied in human cancer cell lines
Hahlbrock, J.1, Navarro, P.1, Wagner, M. 2, Boegel, S. 2, Schild, H.1, Sahin, U. 2, Tenzer, S.1
1
2
Institute for Immunology, University Medical Center Mainz, Mainz, Germany
TRON gGmbH-Translational Oncology, University Medical Center Mainz, Mainz, Germany
Question: Cancer is a primary cause of mortality in industrialized countries. Due to molecular heterogeneity in cancer, often
less than 25% of treated individuals profit from the approved therapies. Individualized medicine is regarded as a potential
solution to low efficacies and high costs for innovation in drug development and health systems, but requires comprehensive
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data from individual samples and a well understanding of the underlying cancer biology.
In the present study, we analyzed the proteome, transcriptome and ligandome of ten MHC class I matched human cancer
cell lines derived from six different tissues with the aim to establish a prediction tool for MHC-presented peptide ligands,
required for an antigen-specific immunotherapy.
Methods: Tryptic peptides, generated from tumor cell lysates using a modified FASP protocol, were analysed by LC-MS
using a nanoAcquity UPLC system coupled to a Waters Synapt G2-S HDMS instrument. Rawdata analysis was performed
in PLGS3.02, searching UniProtKB/Swissprot (human reference proteome). Final identification and clustering steps were
performed with ISOQuant. RNA analyses were performed as a paired-end sequencing of cell line cDNA (2x50 nt) with an
Illumina HiSeq 2500 system. Reads were mapped against the human genome (hg19) using the STAR mapping Algorithm.
MHC class I molecule associated peptides (ligandome) were isolated using a modified immunoaffinity purification protocol
binding the molecules to W6/32 coupled BrCN-Sepharose beads.
Results: Using a novel data-independent acquisition (DIA) label-free LC-MS approach for proteome analysis, we established
a reference proteome data set for those cancer cell lines covering in total over 6.700 protein groups and their relative
abundances. On mRNA level we identified between 9.791 and 12.467 expressed (>= 1 RPKM) coding genes. Analyzing
correlations between proteomics and transcriptomics data, we found surprisingly high correlation values, ranging from r=
0.5 to 0.58 (spearman correlation coefficient). Gene Ontology analysis (protein folding, gene expression, RNA binding, etc.),
revealed correlation values up to r= 0.89. For ligandome analyses we identified between 762 and 3.338 ligands, whereby the
number of identified ligands correlated with the respective MHC expression level.
Conclusion: We generated comprehensive proteomic, transcriptomic and ligandomic data sets, which will be integrated
into a model to quantitatively predict the MHC class I ligandome and to establish novel strategies for antigen-specific
immunotherapy.
PP 6-13
NormSys & CHARME Two initiatives that aim at Harmonizing the Standardization
Processes for Data Exchange in Systems Biology
Hollmann, S.1, Golebiewski, M. 2, Regierer, B. 3
University of Potsdam, Plantgenomics and Systems Biology, Potsdam, Germany
HITS gGmbH, , Heidelberg, Germany
3
LifeGlimmer GmbH, Berlin, Germany
1
2
An essential prerequisite of modern life-science R&D is a high quality of the research data. By enabling the reuse of research
assets, research becomes considerably more efficient and economical. In this context a common understanding of formats and
standards for data and computer models particularly in interdisciplinary research fields is clearly evident: this encompasses
detection and description of the data, their efficient and secure exchange between research labs, institutes and industry,
their integration into workflows, as well as their processing and analysis. Thus, standards represent important drivers in the
life-sciences and technology transfer because they guarantee that data become accessible, shareable and comparable along
the value chain. Several initiatives launched the development and implementation of standards. Unfortunately these efforts
remain fragmented and largely disconnected.
The two initiatives NORMSYS & CHARME will merge the different approaches in the field with a particular reference to
systems biology, and thus avoid too many different solutions being generated in parallel universes that “in the worst case” are
neither compatible nor suitable for large-scale approaches.
NormSys is a research project funded by the German Ministry of Economic Affairs and Energy (BMWi) aiming at enhancing
and promoting the formal standardization of existing community standards in systems biology on a national level. It
develops a concept for the transformation of these standards into officially certified specifications. NormSys establishes
a platform for relevant stakeholders to participate in this standardization process for incorporation of their specific
requirements: researchers from academia and industries with their grass-roots standardization communities like the
Computational Modelling in Biology Network (COMBINE), as well as representatives of official standardization bodies (eg
DIN, CEN-CENELEC or ISO), scientific journals and funding agencies.
CHARME is a international project (COST Action) starting in Mai 2016 which aims to increase the awareness for the
need of standards, enabling the reuse of research data and its interoperability within the scientific community. CHARME
provide a common ground for researchers from academia, research institutes, SMEs and multinational organizations on an
international level.
Both of the above projects complement each other in an optimal way:
they will
106
• firstly combine and review existing community standards and standardization options including the development of a
common understanding/definition of the needs,
• secondly push the implementation of minimal standards in biotechnology especially in systems biology, and
• thirdly create a joint platform for stakeholders for sustainable and efficient exchange and cooperation
PP 6-14
A dynamic model of bile acid transport in the HepaRG cell-line
Kaschek, D.1, Sharanek, A. 2, Guillouzo, A. 2, Timmer, J.1,3,4, Weaver, R. J. 5
University of Freiburg, Institute of Physics, Freiburg, Germany
Université de Rennes 1, Faculté des Sciences Pharmaceutiques et Biologiques, Rennes, France
3
University of Freiburg, BIOSS Centre for Biological Signalling Studies, Freiburg, Germany
4
University of Freiburg, Freiburg Center for Systems Biology (ZBSA), Freiburg, Germany
5
Institut de Recherches Internationales Servier (I.R.I.S.), Paris, France
1
2
Hepatobiliary transporters might play a major role in the development of drug-induced liver injury (DILI). In the past,
various in vivo and in vitro studies have been carried out analyzing uptake and efflux of bile acids and the impact of cholestatic
versus non-cholestatic drugs. Although human hepatocytes are considered the most realistic in vitro cell model, the huge
variability observed in this system limits its suitability for the development of a quantitative mathematical model of bile acid
transport.
Here we present a dynamic model based on ordinary differential equations that describes uptake, basolateral export and
canalicular export of taurocholic acid, a bile acid, in the HepaRG cell line. The highly reproducible experimental data
obtained in HepaRG cells allows to estimate transport rates associated to different transporters like NTCP, MRP3 and BSEP.
Experiments with the cholestatic drug cyclosporin identify the dose-dependent inhibition of single transporters. Finally, we
compare our results in HepaRG to efflux-dynamics in human hepatocytes and determine the pivotal differences between both
cell types, thus, allowing to predict bile transport in human hepatocytes based on a calibrated model in HepaRG.
Funding: This work was supported by funding from the MIP-DILI project, a European Community grant under the Innovative
Medicines Initiative (IMI) Programme (Grant Agreement number 115336).
PP 6-15
Modeling Individual Time Courses of Thrombopoiesis During Multi-Cyclic Chemotherapy
Kheifetz, Y.1, Scholz, M.1, Löffler, M.1
1
IMISE (Institut Für Medizinische Informatik, Statistik und Epidemiologie), Leipzig, Germany
Question: Decreased platelet counts, called thrombocytopenia, is a major dose-limiting side effect of dose-intense cancer
chemotherapies. However, standard coursers of many chemotherapies result in considerable variability in drug induced
platelets dynamics. A major challenge of individualized medicine is to take all relevant factors into account for optimal
risk management using individualized modeling. Actually there is a gap between simple phenomenological predictive
population models and complex biomathematical modeling of averaged data. In order to tackle this problem we revised
a biomathematical model of average human thrombopoiesis under chemotherapy (Scholz et al. 2010) towards modelling
individual time courses.
Methods: We fitted model parameters hierarchically. More than 20 parameters were estimated using dense time series of
three patients treated with BEACOPP chemotherapy. We used these estimators as 3 alternative population models in order
to fit individually 6 parameters for sparser data of selected 135 patients from the German non-Hodgkin‘s lymphoma trial
group applying CHOP-like chemotherapies. The individual parameter estimations simultaneously used information from
other studies published in literature by incorporating it into the respective weighted likelihood functions, assuming a virtual
participation of the patients in the corresponding experiments. These additional information included average dynamics of
TPO, platelets and megakaryocytes of recombinant-TPO treated healthy patients as well as a platelets dynamics after labeled
platelets transfusions to patients with different degrees of thrombocytopenia.
Result: Several new biological insights were discovered and modeled. We hypothesize a bi-phasic TPO-stimulation of
thrombopoiesis. The slowly long-term decrease in average platelets level during multi-cyclic chemotherapy was attributed
to interactions between quiescent and active stem cells compartments. We have found that multi-cyclic chemotherapy
significantly reduces transit times for megakaryocytes and platelets. These long-term systems changes during multi-cyclic polychemotherapies are responsible for strong intra-individual variability of platelets’ nadirs and consequently of chemotoxicity
through treatment cycles. We have found an optimal tradeoff between goodness of fit and overfitting for most of the patients.
Conclusions: We established a model of individual thrombopoiesis response to chemotherapy. Heterogeneity between
patients can be traced back to heterogeneity of a few model parameters. We will exploit the predictive potential of the model
in the near future.
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Abstracts
PP 6-16
DESIGNING AND SIMULATION STUDIES OF Mycobacterium Tuberculosis DNA GYRASE.
Niranjan, V.1, Chaudhuri, T. 2, Prasanna, A.1, N. K. N2, Deshpande, S. 1
1
2
RVCE, BIOTECHNOLOGY, Bengaluru, India
GenEclat Technologies, Bioinformatics, Bengaluru, India
Tuberculosis is one of the airborne and contiguous diseases which ranks second leading causes of death that accounts for the
increase in the mortality rate after HIV. The Mycobacterium tuberculosis is a causative organism for tuberculosis. Unlike any
other bacteria, the M. tuberculosis genome analysis has identified a gyrB-gyrA contig in which gyrA and gyrB encode the A
and B subunits of DNA Gyrase, respectively. In our present study, we have focused on different methods involved in designing
the DNA Gyrase inhibitor for M. tuberculosis using the techniques like Molecular Docking and Molecular Dynamics using
SCHRODINGER SUITE.
MATERIALS and METHODS: PDB ID for target DNA Gyrase (Subunit A and B) was identified through http://www.rcsb.
org/ and screening of PDB were done based on X-Ray resolution and the Scaffold structure of Ligand. The screened PDB’s was
docked. The Post-Reproducibility was carried out by using MAESTRO. Homology Analysis was carried out by using PRIME.
The protein sequence was retrieved from the UNIPROT database.
For Docking study Chembl 2.0 ligand database was selected for studies based on the Number of molecules and the
total number of experiments carried on Homo sapiens. It employs combinatorial technology for lead identification and
optimization. Chemoinformatics analysis was carried out by using CANVAS 2.1. To perform molecular docking, the
software GLIDE 6.0 was used. The High throughput Virtual Screening method was applied first to screen the ligand database
consisting of 5,17,261 molecules. Hierarchical clustering was carried out for generation of Dendrogram. In silico screening of
drug-likeness was done by Qikprop. Molecular Dynamics was carried out using DESMOND. The system was relaxed either by
minimization or by selecting the panel option to relax the model system before simulation. The simulation parameters were
set in one of the general Desmond panels.
RESULT and DISCUSSION: In our present study, Homology Analysis was carried using PRIME SCHRODINGER SUITE,
where two targets receptor i.e. 3ZM7.pdb (DNA Gyrase A) and 3IFZ.pdb (DNA Gyrase B) showed a better result. These two
structures showed a high resolution(R value of 3.30 A° and 2.70 A° respectively) when downloaded from PDB. Hence, they
were subjected to docking study against Chembl 2.0, a tool used that generates a combinatorial library. The Cluster 2 which
had three poses had a best Average score of -8.667. Protein-ligand interactions were explored through the ‹Simulation
Interactions Diagram› panel.
CONCLUSION: The M. tuberculosis genome analysis has identified a gyrB-gyrA contig in which gyrA and gyrB encode the
A and B subunits. Newer fluoroquinolones, including Moxifloxacin and Gatifloxacin, exhibit potent activity against M.
tuberculosis and show potential to shorten the duration of TB treatment. M. tuberculosis DNA Gyrase is thus a validated
target for anti-tubercular drug discovery. A novel inhibitor of M. tuberculosis DNA Gyrase would be effective against multidrug resistant (MDR)-TB, and it could also be effective against Fluoroquinolones-resistant M. tuberculosis.
PP 6-17
Early biomarkers and magnetic resonance imaging for diagnosing Bronchopulmonary
Dysplasia
Sass, S.1, Förster, K. 2,3, Theis, F.4,1, Hilgendorff, A. 2,3
Helmholtz Zentrum München, Institute of Computational Biology, Neuherberg, Germany
Dr. von Hauner Children’s Hospital, Dept. of Neonatology, Munich, Germany
3
Helmholtz Zentrum München, Comprehensive Pneumology Center, Munich, Germany
4
Technical University of Munich, Department of Mathematics, Grosshadern, Germany
1
2
Rationale: Neonatal chronic lung disease, i.e. BPD, as a consequence of pre- and postnatal injury to an immature lung
determines long-term pulmonary and neurologic development. As to date, the diagnosis is solely stated with respect to
its clinical course referring to oxygen dependency on day 28 post partum or at 36 weeks postmenstrual age (PMA). Early
markers are urgently needed for timely diagnosis and personalized treatment.
Objectives: In a unique approach this prospective study determined structural and functional changes in the preterm lung
at the time of diagnosis and identified early disease markers by proteome screening in plasma in the first week of life.
Method: Forty preterm infants (27.7±2.09wks, 984±332g) were included for advanced MR imaging (3-Tesla) and
complemented by Infant lung function testing (ILFT) in spontaneously breathing infants. Plasma samples were processed for
proteomic screening by SOMAscan™. Key findings were confirmed in an independent study cohort (n=21 infants). Statistical
analysis used penalized and Poisson regression analysis; for protein analysis confounder effects were subtracted by lasso
regression.
Results: Statistical analysis confirmed a high correlation of MRI and lung function variables and identified a pattern
characterizing changes in the lungs of preterm infants by T2- and T1-weighed image analysis and lung volume measurements
as well as ILFT. Statistical modelling using the outcome variables ‘days of oxygen’ or ‘days of MV’ instead of BPD diagnosis
108
confirmed the analysis indicated above. Functional enrichment analysis showed overrepresentation of the GO categories
‘immune function’, ‘extracellular matrix’, ‘cellular proliferation/migration’, ‘organ development’ and ‘angiogenesis’ in infants
with BPD. Second, 12 proteins were significantly regulated when comparing the group of infants later developing BPD with
infants without the disease, reflecting the categories identified by functional enrichment analysis. One protein was identified
as a potential biomarker.
Conclusions: We identified a structural pattern characterizing BPD by advanced MRI confirmed by ILFT. The findings
reflect the characteristic picture outlined by previous studies of human tissue samples, i.e. the presence of interstitial and
emphysematous changes. The identified protein indicated BPD development in the first week of life enabling personalized
treatment strategies.
PP 6-18
Using physiologically-based pharmacokinetic modelling to analyze the effect of hepatic
impairment on drug detoxification capacity at the whole-body level
Schenk, A.1,2, Ghallab, A. 3,4, Hassan, R. 3,4, Hofmann, U. 5,6, Schuppert, A.1,7,2, Teutonico, D.7, Hengstler, J. 3, Kuepfer, L.7
RWTH Aachen, Joint Research Center for Computational Biomedicine, Aachen, Germany
RWTH Aachen, Aachen Institute for Advanced Study in Computational Engineering Science (AICES), Aachen, Germany
3
Leibniz Research Centre for Working Environment and Human Factors at the Technical University Dortmund, Dortmund, Germany
4
South Valley University, Department of Forensic Medicine and Toxicology, Faculty of Veterinary Medicine, Qena, Germany
5
Dr. Margarete Fischer-Bosch Institute for Clinical Pharmacology, Stuttgart, Germany
6
University of Tuebingen, Tuebingen, Germany
7
Bayer Technology Services GmbH, Computational Systems Biology, Leverkusen, Germany
1
2
Main objective: To quantify and analyze the effect of hepatic impairment on pharmacokinetics of a drug cocktail at the
whole-body level by means of physiologically-based pharmacokinetic (PBPK) modelling.
Methods: For healthy mice, we measured the plasma concentration profiles of a drug cocktail of six parent drugs (caffeine,
codeine, midazolam, pravastatin, talinolol, torsemide) including some of the corresponding metabolites. Notably, we
considered the plasma concentration profiles at three sampling sites within the body, i.e. the heart, the portal vein and the
hepatic vein. Additionally, we quantified the area of expression within the liver of selected cytochrome p450 enzymes by
immunostaining. We used this data set to firstly establish PBPK models for the six compounds for healthy mice including
in particular liver zonation with a periportal and a pericentral region based on the results of the stainings. Notably, the
mechanistic nature of the PBPK models allowed us to consider all three sampling sites at the same time. Next we quantified
the impact of CCl4 -induced liver damage on the hepatic metabolic capacity. To this end, the same plasma concentration
profiles data was also gathered for mice after CCl4 -administration.
Results: The six PBPK models for the healthy mice were in excellent agreement with the experimental concentration profiles
at all three sampling sites as such allowing a quantification of hepatic metabolic capacity at the whole-body level. Notably,
the PBPK models include experimental information from different scales of biological organization ranging from enzyme
expression in the hepatic lobule to drug concentration profiles in the blood plasma. Furthermore, it was possible to describe
the plasma concentration profiles of mice after CCl4 -induced liver damage.
Conclusion: We successfully established PBPK models for a cocktail of six drugs for healthy mice. These models feature
liver zonation and concentration profiles from three different sampling sites within the body. The approach allows the
quantification and analysis of hepatic impairment at the whole-body level.
PP 6-19
Dynamical modelling of the murine immune response to pneumococcal lung infection with
and without antibiotic treatment
Kuepfer, S.1, Ahnert, P.1, Loeffler, M.1, Wienhold, S. 2, Nouailles-Kursar, G. 2, Witzenrath, M. 2, Scholz, M.1
University of Leipzig, IMISE, Leipzig, Germany
Charite Universitaetsmedizin, Berlin, Germany
1
2
Pneumonia is considered to be one of the leading causes of death worldwide. The outcome depends on both, proper antibiotic
treatment and the effectivity of the immune response of the host. However, due to the complexity of the immunologic
cascade initiated during infection, the latter cannot be predicted easily.
We construct a biomathematical model of the murine immune response during infection with pneumococcus aiming at
predicting the outcome of antibiotic treatment. The model consists on a number of non-linear ordinary differential equations
describing dynamics of pneumococcal population, the inflammatory cytokine IL-6, neutrophils and macrophages fighting the
infection and destruction of alveolar tissue due to pneumococcus. Equations were derived by translating known biological
mechanisms and assuming certain response kinetics. Antibiotic therapy is modelled by a transient depletion of bacteria.
Unknown model parameters were determined by fitting the predictions of the model to data sets derived from mice
109
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Abstracts
experiments of pneumococcal lung infection with and without antibiotic treatment. Time series of pneumococcal population,
debris, neutrophiles, activated epithelial cells, macrophages, monocytes and IL-6 serum concentrations were available for
this purpose. The antibiotics Ampicillin and Moxifloxacin were considered.
Parameter fittings resulted in a good agreement of model and data for all experimental scenarios. Sensitivities of parameter
estimates could be estimated. The model can be used to predict the performance of alternative schedules of antibiotic
treatment.
We conclude that we established a biomathematical model of pneumococcal lung infection in mice allowing predictions
regarding the outcome of different schedules of antibiotic treatment. We aim at translating the model to the human situation
in the near future.
PP 6-20
Modelling immuno-chemotherapy of lymphomas
Scholz, M.1, Roesch, K.1, Hasenclever, D.1
1
IMISE / University of Leipzig, Leipzig, Germany
Background: Moderate intensifications improved the outcome of lymphoma chemotherapy, but highly intense therapies are
inferior. We hypothesise that the immune system has a key role in controlling residual tumour cells after treatment. More
intense therapies result in a stronger depletion of immune cells allowing an early re-growth of the tumour.
Methods: To understand this process in more detail, we propose a differential equations based model of the dynamics
and interactions of tumour and immune cells under chemotherapy. Major model features are an exponential tumour
growth, a modulation of the production rate of effector cells by the presence of the tumour (immunogenicity) and mutual
destruction of tumour and immune cells. Chemotherapy causes damage to both, immune and tumour cells. Immunotherapy by the monoclonal antibody Rituximab is modelled by a direct cell kill and an intensified immune response. Growth
rate, chemosensitivity, immunogenicity and initial size of the tumour are assumed to be patient-specific, resulting in
heterogeneity regarding therapy outcome. Maximum-entropy distributions of these parameters were estimated on the basis
of clinical survival data.
Results: The resulting model can explain the outcome of eight different chemotherapeutic regimens with and without
Rituximab and corresponding hazard-ratios. Estimated parameters are biologically plausible. We demonstrate how the model
can be used to make predictions regarding yet untested therapy options.
Conclusions: We conclude that our model explains observed paradox effects in lymphoma therapy by the simple
assumption of a relevant anti-tumour effect of the immune system. Heterogeneity of therapy outcomes can be traced back to
heterogeneity of a few model parameters whose distribution can be estimated on the basis of clinical survival data. The model
can be used to predict the performance of new therapy options.
PP 6-21
Nuclear-encoded mitochondrial genes in hypercholesterolema and atherosclerosis
progression
Vilne, B.1, Björkegren, J. L. 2, Skogsberg, J. 3, Foroughi, Asl H. 3, Kessler, T.1, Schunkert, H.1
Deutsches Herzzentrum München, Klinik für Herz- und Kreislauferkrankungen, München, Germany
Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences Icahn Institute for Genomics and Multiscale Biology, New York, United States
3
Karolinska Institutet, Cardiovascular Genomics Group, Division of Vascular Biology, Department of Medical Biochemistry and Biophysics, Stockholm, Sweden
1
2
Background and Aims: Dysregulation of nuclear-encoded mitochondrial gene (NEMG) expression might constitute an
intermediate step by which hypercholesterolemia promotes atherosclerotic lesion development.
Methods and Results: The transcriptome of the aorta was examined in Ldlr−/−Apob100/100 Mttpflox/flox Mx1-Cre mice at 10-week
intervals. During the phase of rapid lesion expansion and
foam cell formation, i.e. between 30 and 40 weeks of age, NEMGs were 4-fold down-regulated compared to non-NEMGs (P =
6.11e-72) and involved >28% of total NEMGs (n=1,240). Down-regulation included PGC1-α and -β, i.e. two members of the
peroxisome proliferator-activated receptor γ co-activator 1 family of transcription regulators. In addition, we identified two
mitochondria-related gene co-expression modules as being the most negatively associated with lesion development (r= -0.42
and -0.43; P=0.04). Within these modules, we observed significant over-representation of estrogen related receptor ERR-α
binding sites, reactive oxygen species (ROS) target genes and several key mitochondrial pathways. In fact, the expression of
ERR-α, an effector of PGC1 in the control of mitochondria biogenesis, was found tightly correlated with PGC1, mitofusin 2
(MFN2), uncoupling protein 3 (UCP3), superoxide dismutase 2 (SOD2), lipin 1 (LPIN1) and citrate synthase (CS) expressions.
Plasma cholesterol lowering (PCL) at 30 weeks, previously shown to prevent advanced plaque formation, resulted in 4-fold
up-regulation of NEMGs (P = 3.89e-16).
110
Conclusion: Our results suggest that arterial wall responds to prolonged hypercholesterolemia by a co-ordinated downregulation of NEMGs, including mitochondrial biogenesis and antioxidant genes, possibly regulated by ERR-α/PGC1. Downregulation demonstrates an inverse relationship to lesion expansion and foam cell accumulation, and is partly reversed by
PCL.
PP 7: Systems Medicine & Genetic/ Epigenetic Mechanisms
PP 7-01
Acute and Chronic inflammation and mast cell activation, IN Silico
Abdukerim, A.1, Barberis, M.1, Sahin, N. 2, van Ham, M.1,3, Westerhoff, H. V.1,2,4
University of Amsterdam, Synthetic Systems Biology , Amsterdam, Great Britain
VU University Amsterdam, Department of Molecular Cell Physiology, Amsterdam, Great Britain
3
Sanquin Blood Supply, Department of Immunopathology, Amsterdam, Netherlands
4
Manchester Interdisciplinary Biocentre, Manchester Centre for Integrative Systems Biology, Manchester, Great Britain
1
2
Main objectives of the study: Systems Immunology addresses how regulators of the immune system that integrates
at various system levels, modulate the response to inflammation. We aimed to design a computer model able to provide
a rational explanation of the network’s response to antigen in terms of acute or chronic inflammation. The model should
calculate in silico the level of tumor necrosis factor alpha (TNF-α). It should likewise predict the effect of an in silico drug
targeting the immunoglobulin free light chain (FLC) that activates mast cells, on the outcome of an inflammation.
Materials and methods: A network of the native immune system was decomposed into various dynamic processes,
the activation of which depending on the concentration of their components in time. For each species in the network, a
balance equation was then formulated, specifying its time dependence as the difference between the rates of the reactions
synthesizing the commodity and the rates of the processes degrading it. The corresponding rate equations were integrated in
time or for steady state by using the COPASI simulation software. Immunostaining on cancer biopsies, protein detection, and
in vitro and in vivo studies were used for validation.
Results: The model enabled us to 1) examine scenarios such as the capability of the system to kill invading microorganisms,
2) explain the dichotomy between acute and chronic inflammation, and 3) test the effect of a in silico peptidic drug that
inhibits FLC. In our experimental studies FLCs secreted from B-cells bound to their specific receptor on mast cells.FLC and
mast cells were co-localized in various cancer biopsies such as those of lung cancer tissues. Therapeutically, administration of
an anti-FLC peptide had a beneficial effect on tumorigenesis, suggesting that FLC may play a role by targeting inflammation
that promotes cancer. The model reproduced these findings and was subjected to tests for dynamic stability and to sensitivity
analysis.
Conclusions: An iterative process of model building is able to provide new understanding about the mechanisms of
inflammation. A systems mechanism for the effects of therapeutic peptides against inflammation and tumorigenesis was
identified and made computable in terms of personalized molecular properties. This should facilitate further testing as well
as provide a platform for personalized systems immunology.
PP 7-02
Prioritization of candidate causal genes in rare genetic diseases through integration of
exome sequencing data and biological databases
Avsec, Z.1, Mertes, C.1, Prokisch, H. 2, Gagneur, J.1
TU Munich, Bioinformatics, Garching bei München, Germany
Helmholtz Zentrum München, Institut für Humangenetik, Neuherberg, Germany
1
2
Identification of novel causal genes in rare Mendelian diseases helps to understand the disease’s pathomechanism, solve
more cases and reveal novel biological pathways. For proving the causality of a gene, experimental follow-up studies are
needed. These are usually expensive and time-consuming, hence the genes sent for validation should be chosen carefully.
Here, we propose different statistical models for prioritizing candidate genes that integrate exome sequencing data, known
causal genes, molecular pathways and protein-protein interaction networks. The models were applied to a whole-exome
sequencing dataset of about 550 mostly unrelated cases diagnosed with a mitochondrial disease and 300 controls. Sensitivity
for recovering 75 known causal genes in a cross-validated fashion was used to benchmark the models. Bayesian approaches
showed increased sensitivity over classical statistical testing. Highest sensitivity -40% in the 100 most highly ranked
predictions- was reached using the machine learning approach. Our method is general and could be applied to other rare
diseases with a few known causal genes.
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Abstracts
PP 7-03
A glance at smoking effect on preeclamptic placenta with transcriptional regulation
approach
Aydin, B.1, Arga, K.1
1
Marmara University, Bioengineering, Istanbul, Turkey
Preeclampsia (PE) is a pregnancy specific disease that indicated by glitches of the placenta like abruptions or infarctions.
PE can cause adverse pregnancy outcomes like prematurity and growth restriction of baby meanwhile hypertension and
proteinuria for mother. Smoking affects baby by plecanta previa, low birth weight but paradoxically risk of PE is lower in
smoker mothers than non-smoker ones. Exact mechanism and relation between cigarette smoking and preeclampsia has not
been enlightened yet.
Objective: To search whether a preventive side of smoking on preeclampsia. If so, to investigate a relation between cigarette
smoking and preeclampsia by using transcription factors (TFs).
Methods: To elucidate gene expression differences, three high throughput screening datasets downloaded (GSE44711preeclamptic plancenta, GSE7434- cigarette smoking placentas, GSE48424 smoking-preeclamptic placenta). Differentally
expressed genes (DEGs) are sorted out and transcription factors that regulates these DEGs are determined. A program
TRANSREGNET coded which includes 57157 genes and 348 TFs that allows the user finding of Tfs which is responsible
for regulation of query genes. By using this program Tfs were found for DEGs from each three cases. Then these TFs were
classified by their job.
Results: Preeclamptic placenta, cigarette smoking placenta and smoking-preeclamptic placenta samples share common TFs
of AR, E2F4, ESR1, FOXA1, FOXP3, GATA1, GATA2, GATA3, MYC and YBX1. Among these TFs ESR1 and GATA3 regulates
uterus development, MYC is responsible from Wnt signaling which is essential for female reproduction, uterine function and
organ development. ESR1 and E2F4 have roles in epithelial cell development which is important for PE. Because epithelial
dysfunction plays an essential role in pathogenesis of PE, also.
Conclusion: The system behind cigarette smoking in pregnancy and reduced PE risk is not fully clear in literature yet. While
the hierarchy between genes and cross-talk of TFs in our model, there could be a logical relation between smoking during
pregnancy, lowers PE risk by regulation of oxidative stress and prevention of vascularization.
PP 7-04
Post-transcriptional gene regulation in mitochondrial disorder patients
Bader, D. M.1, Kremer, L. S. 2, Pichler, G. 3, Schwarzmayr, T. 2, Holzerova, E. 2, Kopajtich, R. 2, Wieland, T. 2, Strom, T. M. 2,4, Mann, M. 3, Prokisch, H. 2,4,
Gagneur, J.1
Technische Universitaet Muenchen, I12 Computational Genomics, Munich, France
Helmholtz Zentrum, Institute for Human Genetics, Neuherberg, Germany
3
Max-Planck Institute of Biochemistry, Department of Proteomics and Signal Transduction, Martinsried, Germany
4
Technische Universitaet Muenchen, Institute of Human Genetics, Munich, Germany
1
2
Joint analysis of transcriptome and proteome allows a more detailed explanation of the observed phenotype and the
regulatory steps that lead to its intermediates, than separate studies of RNA and protein levels. Recently, researchers started
integrative genome-wide studies on RNA and protein levels. Yet, most effort was spent on model organisms leaving the
complex regulation of protein per RNA (PPR) in humans largely unexplored.
Here, we investigate PPR regulation in 35 human fibroblast cell lines, originating from mitochondrial disease patients. Our
PPR analysis is based on the comparison of more than 4,500 gene products measured with RNAseq and mass-spectrometry.
We demonstrate that controlling for hidden technical biases in the data rescues the similarity of biological replicates.
Interestingly, pathways showed various levels of RNA protein correlation with the ribosome complex showing significant
low correlations and sarcolemma high correlations. This indicates that pathways adopt different regulatory strategies, which
are rather transcriptional or post-transcriptional. Additionally usage of frequent codons correlates with the PPR expression
ratio, consistent with the positive contribution of frequent codons to translation rates.
The joint analysis of RNA and protein in human cell lines widens our view on cellular architecture and regulation.
112
PP 7-05
Quantitative analysis of gene expression based on large distance histone acetylation
modifications as enhancers
Haghighi, E.1, Heinig, M.1
1
Helmholtz Zentrum Muenchen, Institute of Computational Biology, Neuherberg, Germany
Objectives: Enhancers are highly tissue specific and important for the regulation of transcription of their target genes.
Previously it has been shown that levels of histone modifications in transcription start sites are highly predictive of
gene expression levels. Here we aim to study the role of long distance enhancers for tissue specific gene expression using
quantitative analysis of gene expression based on the levels of the histone modification H3K27ac.
Methods and materials: We used gene expression and histone modification data for 19709 genes and 48 tissues from the
epigenomics roadmap consortium. In order to predict the gene expression level, a linear model which is penalized using Lasso
is fitted on the different sites of H3K27ac, for which the number of reads per kilo base per million (RPKM) is computed. The
evaluation process is accomplished using one-leave-out method. For each gene the following steps are applied: once a tissue
is left out, the K nearest tissues are considered to learn a linear model to estimate their gene expression with respect to the
relevant histone acetylation sites. These relevant sites are selected based on the most significant correlations between the
measured RPKM for gene expression and H3K27ac sites. Using the estimated model, the gene expression can be predicted for
the excluded tissue. It is also worth reminding that the nearest tissues are determined once using the distance between gene
expression vectors for each pair of tissues over the whole genome as a preprocessing procedure.
Results: The proposed method is evaluated for each gene based on the correlation between the actual level of the gene expression and
the estimated one. The proper value of K is a trade off point between selecting more similar tissues and most relevant acetylation sites
(lower values of K) and being sure to have enough information for estimation (higher values of K). With respect to our experiments,
the best value to set K is 8, for which the correlation of the results for about half of the genes is more than 0.5. The proposed method is
more effective for the genes, which are active in a larger number of tissues. For the genes with more than 20 active tissues, the mean of
the correlation is 0.49, whilst for the overall genes is 0.44.
Conclusion: In this abstract, a linear model to predict gene expression based on long distance enhancers is presented. Applying a
proper value for the number of K nearest tissues, is important to use the relevant information to estimate the model. For about half
of the genes, the estimated models correlates reasonably with the actual values of gene expression. The ability of predicting gene
expression level increases for the genes, which are active in most of the tissues. Our results also allow to associate long distance
enhancers to their target genes and can be used for the interpretation of the genetic variations related to cancer or common diseases.
PP 7-06
Time series analysis of macrophage activation and signalling from RNAseq data
Bergey, F.1, England, H. 2, Papoutsopoulou, S. 2, Werner, M. 2, Paszek, P. 2, Kittner, M.1
1
2
LifeGlimmer GmbH, Berlin, Germany
University of Manchester, Faculty of Life Science, Manchester, Great Britain
Time series high-throughput data require advanced procedures to compare between different parameters. Here we developed
new statistical methods based on linear regression models to determine temporally regulated gene clusters in macrophage
cells undergoing differential immune activation. The RNA-seq data used in this work have been generated within the
SysmedIBD consortium (FP7, grant agreement n° 305564).
Macrophages are our first line of defence against foreign threats, but their activation must be coordinated to avoid harmful
effects. Here we investigated their temporal response of mouse bone marrow derived macrophages to the pro-inflammatory
cytokine tumour necrosis factor alpha (TNF-α), and lipid A, agonist of toll-like receptor signalling, a major pathogen sensing
system. Samples were collected for the untreated condition and 1 hour, 3 hours and 6 hours after treatment. A linear model
is estimated based on the gene expression values using time, treatment and the interaction between time and treatment as
explanatory variables. Statistical tests were performed to estimate the role of these variables between treatments.
Based on this analysis, genes were classified depending on their response pattern after treatment. We focused on some
particular, temporarily regulated gene clusters such as genes activated early after stimulation and then switched off, or
genes activated early or late with prolonged expression. In these specific clusters, we analysed whether the treatments induce
different response patterns or different expression levels, and established treatment specific effects. For instance, A20 and
IκBα genes, negative regulators of the overall response, belong to the first cluster and show similar activation patterns for
both treatments. However, key signalling molecules such as interleukin 1α and β, show differential activation: TNF-α induces
early transient activation whereas lipidA causes a sustained response. Genes within clusters were then further analysed for
molecular functions and interactions on the protein level.
The linear model designed in this study enables grouping of genes according to their significant response patterns and easily
determines genes that respond differently to one of the treatments. Such an approach can be applied similarly to determine
differences in expression when comparing different cell types or between species.
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PP 7-07
In situ transcriptomics reveals metabolic and immunological zonation in human liver
Brosch, M.1, Muders, M. 2, Matz-Soja, M. 3, Schafmayer, C.4, von Schönfels, W.4, Zeissig, S.1, Dahl, A. 5, Segiova, F.6, Zerial, M.6, Küpfer, L.7, Brusch, L. 8,
Kaderali, L. 9, Gebhardt, R. 3, Becker, T.4, Baretton, G. B. 2, Ehninger, G.1, Hampe, J.1
University Hospital Carl Gustav Carus, Department of Internal Medicine I, Dresden, Germany
University Hospital Carl Gustav Carus, Institute of Pathology, Dresden, Germany
3
University of Leipzig, Institute of Biochemistry, Leipzig, Germany
4
University Hospital Schleswig-Holstein, Department of General and Thoracic Surgery, Kiel, Germany
5
Technische Universität Dresden, BIOTEChnologisches Zentrum der TU Dresden, Dresden, Germany
6
Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
7
Bayer Technology Services GmbH, Computational Systems Biology, Leverkusen, Germany
8
Technische Universität Dresden, Center for Information Services and High Performance Computing, and Center for Advancing Electronics Dresden, Dresden, Germany
9
University Medicine Greifswald , Institute for Bioinformatics,, Greifswald, Germany
1
2
Liver parenchyma shows a considerable and functional plasticity, known as metabolic zonation. Thus, different metabolic
pathways are carried out in different, the periportal and pericentral, zones of the liver lobules. Along the axis of the lobule all
liver functions need to be performed resulting in a heterogeneous distribution of enzymes, transporters and other functional
components. At least for the mouse a pattern of gene expression could be drawn on the basis of microarray gene expression
analysis on preparations of isolated periportal and pericentral hepatocytes. To investigate the global gene expression pattern
in human liver zonation we used RNA-Seq on laser captured microdissected periportal and pericentral human hepatocytes.
In total 261 genes were identified, that showed significant changes (p<0.01) between the hepatocytes of the two different
zones of the liver. The results are in good concordance with the described gene expression in mice with a differential
expression of genes encoding enzymes of the intermediary metabolism, ammonia utilization, amino acid metabolism
and xenobiotic metabolism. In addition genes of proteins of the zonal driver pathways Wnt/b-Catenin and Hedgehog
were complementary expressed in periportal and pericentral hepatocytes. Furthermore an innate immunity zonation was
observed with a high expression of genes encoding for antimicrobial proteins in hepatocytes surrounding the periportal
field. Immunohistochemical staining validated the spatial distribution of c-reactive protein. In conclusion, the human data
confirms the metabolic zonation as it is observed in mouse expression analysis, indicating that whole tissue approaches have
to be assessed carefully under consideration of the heterogeneous and functional plasticity of hepatocytes along the liver
lobule axis. Furthermore the periportal expression of genes encoding for proteins of the innate immune system indicates
that the hepatocytes surrounding the periportal field might function as a barrier for gut derived pathogens.
PP 7-08
Deep learning of regulatory factors of the transcriptional landscape
Eraslan, G.1, Preusse, M.1, Theis, F. J.1,2, Mueller, N. S.1
1
2
Helmholtz Zentrum München, Institute of Computational Biology, Neuherberg, Germany
Technische Universität München, Department of Mathematics, Garching, Germany
Myriad of RNA-binding proteins (RBP) and ribonucleoproteins (RNP) are involved in many key cellular mechanisms
including RNA splicing, RNA modifications and post-transcriptional regulation. Although the factors of RNA regulation
and the elements of RNA interactome are explored in the literature progressively, effective computational techniques, which
can shed light on the diverse RNA landscape are still lacking. Therefore, we aim to elucidate the interactions between RNA
transcripts and various factors of RNA interactome using the sequence information by using the exceptional predictive
power of deep convolutional neural networks and vast amount of high-throughput sequencing data. Our novel method
uses wide-range context information from RNA sequences, not limited to short RNA motifs. At present, we utilize CLIPSeq, HITS-CLIP, iCLIP and PAR-CLIP datasets from the doRiNA database in order to learn sequence features of RBP and
RNP binding sites, beyond the limited length of RBP motifs. We are planning to further incorporate more RNA interactome
resources such as RNA m6a-seq methylation sites and microRNA binding positions. This predictive method of the RNA
landscape can be used to analyse mutation effects and their putative transcriptome-wide effects.
114
PP 7-09
Integrant roles of microRNAs and transcription factors in ovarian cancer transcriptional
regulatory network
Gov, E.1, Arga, K. Y.1
1
Marmara University, Bioengineering, Istanbul, Turkey
Question: Transcriptional regulation of gene expression is often the primary process due to response of system alterations,
wherein the information contained in a genome is converted and then eventually used to produce the proteins required for a
given response.
Methods: Here, we set out to reconstruct a transcriptional regulatory network of Homo sapiens consisting of experimentally
verified interactions on miRNAs, TFs and their target genes. We have performed topological analyses to determine the
transcriptional regulatory roles of miRNAs and TFs. Differentially expressed genes (DEGs) were identified by using two
independent ovarian cancer associated transcriptome datasets (GSE7463 and GSE14407) from the Gene Expression Omnibus
(GEO) database. Integration of differential expressed gene and miRNA data which was extracted from miR2disease database,
on ovarian cancer was achieved to observe dynamic patterns of the disease specific gene expression.
Results: As the regulators, the TFs HIF1A, TP53, EGR1, ETS1 and GATA3 as well as the miRNAs miR-30a-5p, miR-429,
miR-206, miR-142-3p, and miR-30e-5p dominated the disease-specific sub-network regulating excessive number of DEGs.
Overall results provided that positive and negative correlations occur in the active subnetwork and therefore mutual TFmiRNA regulation (interactive cooperation) and transcriptional regulations of target genes may occur through hierarchical
mechanisms where miRNAs were the upstream regulators of TFs were the prominence strategies in gene regulatory network
of ovarian cancer. In addition, multiple signals from miRNAs were integrated by TFs.
Conclusions: Our results demonstrate new insights on TF and miRNA crosstalk, and here we present reciprocal interplay
between ovarian cancer related miRNAs, TFs and their target genes.
PP 7-10
Cigarette smoking increased expression of the G protein-coupled receptor 15 by change
in CpG methylation
Haase, T.1,2, Krause, J.1,2, Müller, C.1,2, Stenzig, J. 3,2, Röthemeier, C.1, Wild, P. S.4,5,6, Blankenberg, S.1,2, Zeller, T.1,2
University Heart Center Hamburg, Clinic for General and Interventional Cardiology, Hamburg, Germany
German Center for Cardiovascular Research (DZHK e.V.), partner site Hamburg/Kiel/Lübeck, Hamburg, Germany
3
University Medical Center Hamburg-Eppendorf, Department of Experimental Pharmacology and Toxicology, Hamburg, Germany
4
University Medical Center of the Johannes Gutenberg-University Mainz, Center for Cardiology, Preventive Cardiology and Preventive Medicine, Mainz, Germany
5
University Medical Center of the Johannes Gutenberg-University Mainz, Center for Thrombosis and Hemostasis, Mainz, Germany
6
German Center for Cardiovascular Research (DZHK e.V.), partner site RhineMain, Mainz, Germany
1
2
OBJECTIVE: Cigarette smoking increases the risk for cardiovascular disease. Recent studies have focused on the effect
of smoking habit on DNA methylation of target genes. Within the GPR15 gene, a novel cardiovascular risk candidate gene
encoding the G-protein coupled receptor 15, the CpG locus cg19859270 has been identified to be hypomethylated in smokers,
which was associated with increased GPR15 expression in white blood cells.
The aim of this study was to analyze the longitudinal effect of smoking behavior on GPR15 expression in monocytes and
peripheral mononuclear blood cells (PBMCs).
METHODS: To investigate the effect of changes in smoking behavior and changes in expression over time, monocytic GPR15
mRNA expression was measured by qPCR in smokers (n = 179), ex-smokers (n = 369) and never-smokers (n = 480) from the
population-based Gutenberg Health Study (GHS) at baseline and at the five-year follow-up visit. Linear mixed regression models
were applied to test differential gene expression between baseline and follow-up visit.
The methylation status of the GPR15 cg19859270 locus was determined in PBMCs from 262 subjects by bisulfite conversion and
high resolution melting (HRM) and expression of mRNA of PBMCs was analyzed with the Affymetrix Exon ST1.0 GeneChip
Array. Regression analysis was performed to estimate the association between smoking and GPR15 CpG methylation as well as
gene expression.
RESULTS: Monocytic GPR15 expression was significantly increased in smokers compared to ex- (p < 0.001) and neversmokers (p < 0.001). Ex-smokers had significantly elevated GPR15 expression levels compared to never-smokers up to ten
years after quitting (p < 0.001). Smoking initiation between baseline and follow-up visit (17 subjects) led to an increase in
GPR15 expression, whereas smoking cessation resulted in decreased GPR15 expression (42 subjects, p < 0.001).
In PBMCs, the correlation between smoking and increased GPR15 gene expression (p < 0.0001) was associated with
cg19859270 hypomethylation (p < 0.05). Likewise, smoking cessation was linked to cg19859270 methylation as well as
reduced GPR15 expression (p < 0.05). Furthermore, higher cumulative smoking exposure correlated with lower GPR15 CpG
site methylation and as well as higher GPR15 mRNA expression (p < 0.0001).
CONCLUSION: This was the first longitudinal study showing that changing smoking behavior changed GPR15 expression.
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Abstracts
This time- and dose-dependent effect was mediated by changes in DNA methylation. Hence, GPR15 poses an interesting new
candidate gene for the link between smoking and cardiovascular disease.
PP 7-11
Genetic analysis of spontaneous (non-toxic) liver fibrosis in a congenic mouse model
Hall, R.1, Hochrath, K.1, Lammert, F.1, Grünhage, F.1
1
Saarland University Medical Center, Homburg, Germany
Background: Mutations in the ABCB4 (ATP-binding casette, subfamily B, member 4) gene cause cholestatic liver
diseases including progressive intrahepatic familial cholestasis (PFIC). Modifying genes of these diseases have yet to be
identified systematically. In this study we used the Abcb4 (Mdr2) knockout (-/-) mouse model, in which the deficiency of the
hepatobiliary phophatidylcholine floppase leads to chronic cholestasis, liver injury and fibrosis. As different mouse strains
show varying fibrosis susceptibility, we applied a systematic approach to elucidate the genetic control of liver fibrosis in an
experimental cross of ABCB4 deficient mice.
Methods: The Abcb4 -/- knockout was crossed from the fibrosis-resistant FVB-Abcb4 -/- mice to the susceptible BALB/cJ
strain by repeated backcrossing. To identify genetic modifiers that contribute to the fibrosis susceptibility linked to ABCB4
deficiency, we crossed these two congenic strains to generate an F2 intercross population. By quantitative trait locus (QTL)
analysis differences in disease progression were mapped to polymorphic genetic regions across the whole genome. Single and
two-dimensional QTL scans were applied to identify modifiers and pairwise gene interactions.
Results: Compared to FVB-Abcb4 -/- mice, the BALB-Abcb4 -/- mice progress to higher fibrosis stages. The heterogenic F2
population shows marked phenotypic variation. Whereas single modifiers demonstrate minor effects, gene-gene interaction
scans identified a significant interaction of two QTLs on chromosomes 4 and 17. Underlying these loci we identified the genes
Abcg5, Abcg8 and sterol carrier protein 2 (Scp2) that are functionally related with hepatobiliary cholesterol homeostasis and
resemble creedal modifer genes.
Conclusions: The congenic Balb-Abcb4 knockout mouse allows the genomic exploration of a spontaneous, non-toxic disease
model of a human gene defect. The experimental cross of the two genetic backgrounds with distinct fibrosis susceptibility
enables the identification of Abcb4-dependent modifiers of cholestatic liver diseases.
PP 7-12
ChIP-Seq pipeline for the identification of differential binding of the glucocorticoid receptor
in high fat versus low fat diet in mouse livers
Hawe, J.1, Quagliarini, F. 2, Uhlenhaut, H. 2, Heinig, M.1
1
2
Deutsches Forschungszentrum für Gesundheit und Umwelt, Helmholtz Zentrum München, Institute of Computational Biology, 85764 Neuherberg, Germany
Deutsches Forschungszentrum für Gesundheit und Umwelt, Helmholtz Zentrum München, Institute for Diabetes and Obesity, 85748 Garching, Germany
Background: Glucocorticoid receptor (GR) is a nuclear receptor and important transcription factor (TF) involved in
regulating immune response, metabolism and other significant biological processes, often mediated through the binding
of glucocorticoids (GC) to GR. Due to its role in metabolism, we studied the effects of high fat diet (HFD) as compared to
normal/low fat diet (LFD) on the GR genomic binding in mice.
Materials and methods: Liver samples were taken in a regular interval of 4 hours over 24 hours from mice exposed
to the individual diets, first 5 days and then 12 weeks after start of the diet. A total of 48 samples, including replicates,
were characterized by chromatin immunoprecipitation combined with sequencing (ChIP-Seq). We developed a specialized
computational pipeline for the analysis of ChIP-Seq data, utilizing well established methods for quality control and the
identification of TF bound chromatin regions, as well as a custom data normalization procedure and further downstream
analysis methods. The normalization step included random down-sampling of the reads to a certain threshold, in order to
account for variance between samples introduced during the sequencing process. To establish a set of well-defined, GR bound
regions based on our data, we first identified potential peaks in the individual samples using standard peak calling methods
and then built a universe of peaks reflecting possible GR binding sites by 1) retaining only peaks overlapping between
replicates and 2) merging overlapping regions from different samples. This peak universe (17736 regions) then served as a
basis for all downstream analysis. In addition, we identified and removed hidden confounding variables from the data via
a factor analysis method, thereby getting rid of unwanted noise present in the data. The data adjusted for the identified
confounding variables was then used for differential binding analysis.
Results: We identified a total of 225 differential GR regions at a 10% false discovery rate. To further characterize the
functional importance of those regions, we applied standard functional enrichment tools. Here we detected diverse mouse
phenotype annotations, related to unhealthy liver and disturbed fat metabolism. The respective genes are currently subject
to further experimental validation. In the future, this data set will also allow us to study the GR/GC circadian dynamics with
respect to distinct diets in rodents.
116
PP 7-13
EXPERIMENTALLY-BASED MECHANISTIC MODELING TO STUDY T HELPER 17 CELL
DIFFERENTIATION
Intosalmi, J.1, Chan, Y. H.1, Rautio, S.1, Lähdesmäki, H.1
1
Aalto University, Department of Computer Science, Espoo, Finland
Objectives: T helper 17 (Th17) cell differentiation is steered by extracellular cytokine signals that activate and control the
lineage specific transcriptional program. Experimental studies provide us with an extensive amount of information about
the Th17 lineage specific regulatory network but precise mechanistic understanding of the transcription factor dynamics is
yet to be attained. Our main objective is to develop experimentally-based mechanistic modeling approaches that can be used
to unravel the key molecular mechanisms steering the Th17 lineage specification and cell differentiation processes in general.
Methods: To infer Th17 lineage specific regulatory mechanisms using time-course data, we combine mathematical modeling
with advanced statistical techniques. We construct mathematical models in the form ordinary differential equations (ODEs)
and link the models with experimental data by using well-defined statistical models. The ODE model design can be based
on both known and hypothetical molecular interactions. In our computational implementation, we use state-of-the-art
numerical and statistical methods, including population-based Markov chain Monte Carlo sampling and thermodynamic
integration.
Results: We apply our modeling approach to study the core network steering Th17 cell differentiation. We describe the core
molecular dynamics by means of ODEs and calibrate the model parameters as well as the model structure in a data-driven
manner using time-course RNA sequencing (RNA-seq) measurements. Within our modeling framework, the statistical
properties of discrete read count RNA-seq data are taken into account by linking the ODE modeling with the observations
through the negative binomial distribution. Our results show that we are capable of approving many known molecular
interactions in the core Th17 network as well as predicting balancing effects that are caused by components of competing
subsets of Th cells.
Conclusions: By means of our modeling approach, it is possible to recover and predict core regulatory interactions that
steer the Th17 lineage specification. Our results also illustrate how experimentally-based mathematical modeling enables
knowledge and data driven model construction which takes the analysis of time-course data well beyond standard statistical
analyses.
PP 7-14
Uncovering potential therapeutic targets and prognostic biomarkers of ovarian tissue
related diseases via systems biomedicine approach
Kori, M.1, Gov, E.1, Arga, K. Y.1
1
Marmara University, Bioengineering, Istanbul, Turkey
Question: Dysfunctions and disorders in the ovary may lead to various clinical or subclinical diseases, including ovarian
cancer, ovarian endometriosis and polycystic ovarian syndrome (PCOS). Although the etiological relationship between
ovarian cancer and endometriosis has been studied, the molecular mechanisms behind their connections are not yet in
certain. Additionally in the light of the possibility of developing ovarian cancer is rising in women with PCOS, association
among the diseases is feasible. The availability of high-throughput functional genomics (i.e., transcriptomics, proteomics,
and metabolomics) data will expedite the understanding of the molecular mechanisms behind ovarian diseases.
Methods: In the present study, we performed statistical methods for the analysis of transcriptomics data for ovarian cancer,
ovarian endometriosis and PCOS, and integrated them with genome scale biological networks. Consequently, biomolecule
(i.e: hub protein, transcription factor, miRNA) signatures at proteome, metabolism and transcription regulation levels were
determined via integrative analyses, which might be feasible to uncover the novel biological mechanism insights behind the
ovarian diseases.
Results: In the study, it was determined (i) potential tendency of PCOS and ovarian endometriosis to tumorigenesis, (ii)
molecules and pathways that related with cell cycle, apoptosis and MAPK signaling is the common indicator in formation and
progression of all three diseases and (iii) significant association between PCOS and neurodegenerative diseases.
Conclusions: To the best of our knowledge, this is the first report that illustrates the three ovarian diseases from systems
biomedicine perspective. This study proposed signatures that could be considered as potential therapeutic targets together
with the prognostic biomarkers in further experimental and clinical applications.
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Abstracts
PP 7-15
Transcriptional profile of Aldara induced skin-reaction in humans
Krause, L.1, Garzorz, N. 2, Lauffer, F. 2, Eyerich, S. 3, Theis, F. J.4,1, Eyerich, K. 2, Mueller, N. S.1
Helmholtz Center Munich, Institute of Computational Biology, Neuherberg, Germany
Technical University, Dermatology and Allergy, Munich, Germany
3
Helmholtz Center and Technical University, Center of Allergy and Environment (ZAUM), Munich, Germany
4
Technical University, Mathematics, Munich, Germany
1
2
Imiquimod (Aldara) is used in mouse studies to mimic human psoriasis to test new therapeutic strategies and understand the
underlying pathogenesis. Our aim is to understand what reaction Aldara induces in human skin. Over a time course of 30 days
Aldara cream was regularly applied on 14 different patients. For comparison we used patients with different inflammatory skin
diseases like psoriasis and allergic contact dermatitis (ACD). During the course of the experiment the patients were clinically
monitored and several skin biopsies were taken for histological and gene expression analysis. Gene expression was measured
using Agilent microarrays and modeled with linear mixed effect models to take into account inter-individual dependencies.
Interestingly, the induced skin reactions rather show characteristics of eczema than hallmarks of psoriasis on the level of
histopathology. Gene expression fold changes in Aldara treated skin show highest correlation with ACD skin (r = 0.78) and
lower, yet significant, correlation with psoriasis skin (r = 0.57). Moreover, the overlap of significantly differentially expressed
genes between Aldara treated skin and psoriasis is only 31% compared to 65% overlap between Aldara skin and ACD. In
summary, Aldara induced skin reactions do not show the same histological and gene expression properties as seen in psoriasis.
Whether some aspects of the disease might be reflected in the Aldara model is a question for further investigations.
PP 7-16
A next generations sequencing toolbox with application to schizophrenia
Hastreiter, M.1, Jeske, T.1, Hoser, J.1, Mewes, W.1, Küffner, R.1
1
Helmholtz Center Munich, IBIS, Neuherberg, Germany
Motivation/question: Success of large-scale data analysis depends on sophisticated bioinformatic support to process,
integrate, analyse, and interpret Big Data volumes. An example for such Big Data is Next Generation Sequencing (NGS) data.
Analysis of NGS data requires chaining of various tools with complex input and output formats. This makes reliable and
efficient data handling difficult for non-expert users.
Methods: In order to cope with the increased throughput of massive data generating experiments we are relying on KNIME,
the Konstanz information miner. This system allows to create structured and reusable processing pipelines for all kinds of
analyses that are easy to use and can be shared among the community.
Results: Therefore we have developed a comprehensive KNIME toolkit including nodes for typical steps like read preprocessing,
read mapping, variant calling, detection of differential expression and annotation. Complementary to existing nodes, our
toolbox now facilitates the assembly of basic building blocks into a wide range of customized NGS analysis workflows. We also
developed a High-Throughput Executor (HTE) for KNIME, in order to process complex datasets with minimal intervention.
In schizophrenia, so called “de novo” mutations found in affected children but not in their healthy parents are likely involved.
Thus, the genotype could starting point for both therapy and diagnosis. Here we examined 36 well characterized family
trios and several affected single individuals resulting in 278 cases and 72 parents. Our approach combined exome, genome
and metabolome techniques and the detection of associations between large datasets and clinical phenotypes We present
an automated, ready to use pipeline for the identification
and characterization of variants. The analysis workflow for
processing NGS data and the detection of de novo and loss-offunction (LOF) variants is depicted in the figure.
Conclusion: Compiling the necessary tools into standardized
graphical workflows improves transparency and adaptability
of analysis pipelines and can therefore stimulate collaboration
between computational and wet lab biologists. The publication
of scientific results together with the generating workflows
furthermore has the potential to improve representation,
reproducibility, and dissemination of findings substantially.
We demonstrated the advantages of our workflows based on a
systems medicine approach to discover schizophrenia disease
mutations.
118
PP 7-17
The properties of Ewing Sarcoma driver cells - widen the view beyond the master fusion
gene
Mallela, N. V.1, Hotfilder, M. 2, Seggewiss, J. 3, Jakalski, M. T.1, Dirksen, U. 2, Korsching, E.1
Institute of Bioinformatics, University of Muenster, Muenster, Germany
Pediatric Hematology and Oncology, University Children’s Hospital, Muenster, Germany
3
Institute of Human Genetics, University Children’s Hospital, Muenster, Germany
1
2
Main Objectives: Ewing’s Sarcoma (ES) belongs to the group of bone cancers defined up to now by the existence of one
fusion gene. This oncogenic fusion consists of EWSR1 and members of the ETS gene family. In this study we use the model
cell line CADO-ES1 (EWSR1-ERG) to characterize the tumor biology of ES stem cell like driver cells subpopulation versus the
main population of tumor forming cells. We aim to compare these two subtypes to identify specific characteristics beyond
the fusion gene concept which does not form a sufficient model to explain Ewing sarcoma biology. We also aim to compare
these results to the properties of mesenchymal stem cells and fibroblast cells to gain finally a better understanding of the
interplay of the ES constituting cell subpopulations and possible therapeutic interventions.
Materials and Methods: The CADO-ES1 cultured cells were FACS sorted into SP (side population) and nonSP (non
proliferative) and extensively characterized. Transcriptomic sequencing and whole genome sequencing of CADO-ES1 of
all cell (sub)types was done. Established bioinformatics tools (GATK, Tophat, edgeR, defuse, STAR) were used to conduct
various types of analysis. Inhouse tools were also developed to facilitate the analysis of colorspace reads with standard
bioinformatics tools.
Results: In addition to the EWSR1-ERG fusion, we have also found the NAIP-OCLN fusion gene in the SP cells. Although this
has not yet been discussed in the field of ES, this fusion gene has been reported in the small cell lung cancer (Iwakawa et al.,
2013).
Looking at the DNA repair mechanisms, the expression of the e.g. gene PARP1 has remained the same in both SP and nonSP
cells. We have also observed that PTEN has almost no expression in both the cell types and there is no differential expression
of TP53.
Apart from the above observations, we have also done a variant analysis of the ES transcriptome and genome and have so
far observed a SNP in CHN2 that is presumed to have a disruptive impact at the protein level. We also observed a significant
down regulation of its expression in SP cells. With a family of further candidates the expression results are coherent with the
observations by Hu-Lieskovan et al. 2005.
Conclusion: The whole amount of observations up to now underline that the fine structure of ES biology and the search for
cell populations with complementing sub tasks are necessary to get closer to an explanatory concept of the systemic stability
and the crucial power of ES.
PP 7-18
Genome-wide interplay of DNA methylation with genetic variants on the human
metabolism
Molnos S.1
1
Helmholtz Zentrum München , AME, Munich, Germany
In the past years, through genome-wide association studies (GWAS) hundreds of genetic variants were found to be associated
with metabolites that are involved in relevant biological processes. However, so far none study have systematically, on a large
scale investigated an interplay between genetic and epigenetic changes such as DNA methylation.
In this study we calculate a genome-wide analysis of the interaction between genetic variants and DNA methylation sites on
metabolites in order to enhance the understanding of important biological processes.
Metabolite data were measured by three different metabolomics platforms using a kit-based-targeted quantitative FIA-MS/
MS method (‘Biocrates’), non-targeted, semi-quantitative LC-MS/MS and GC-MS methods (‘Metabolon’), and 1H NMR
measurements in order to derive lipid-related parameters (‘LipoFIT’).
To reduce the number of potential models, CpGs and SNPs with a superthreshold association value to one of the metabolites
(p-value < 0.05) were filtered.
Finally, interaction analysis was conducted using the sample correlation |r| as test statistic. P-values were subjected to
multiple testing corrections using the Bonferroni correction.
In the lipofit platform most associations were found for the lipoprotein fractions small and medium HDL and large LDL. In
the biocrates platform most associations were found for amino acids, whereas in the metabolon platform most associations
were found for the metabolite butyrylcarnitine. Furthermore, interacting SNPs and CpGs can be on different chromosomes.
Interestingly, for the metabolon platform, we found strongest associations with the metabolite butyrylcarnitine and the
corresponding SNPs and CpGs were only close to each other on the same chromosome in contrast to associations with
metabolites measured by the other platforms.
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Abstracts
PP 7-19
Reconstruction of the FANCA interactome and discovery of new putative oncogenes in
head and neck squamous cell carcinoma.
Pitea, A.1, Sass, S. 2, Theis, F. J. 2,3, Zitzelsberger, H.4, Unger, K.4, Müller, N. S. 2
Helmholtz Zentrum München Research Center for Environmental Health (GmbH), Institute of Computational Biology/Research Unit Radiation Cytogenetics, Neuherberg, Germany
Helmholtz Zentrum München Research Center for Environmental Health (GmbH), Institute of Computational Biology, Neuherberg, Germany
3
TUM Technische Universität München, Mathematics, Garching, Germany
4
Helmholtz Zentrum München Research Center for Environmental Health (GmbH), Research Unit Radiation Cytogenetics, Neuherberg, Germany
1
2
Head and neck squamous cell carcinomas (HNSCC) are regularly treated with radiotherapy alone or concomitant with
chemo- or immunotherapy. Patients suffering from HNSCC present an overall 5-year survival rate of approximately only
50% which is likely to be attributed to radioresistance. A previous study on patients that were treated with radiotherapy
alone identified a genomic copy number gain of chromosome 16q24.3, containing the DNA repair gene FANCA, which was
associated with unfavourable outcome in radiation-treated patients. With this as a starting point, we set up to validate
FANCA at multiple molecular levels in an independent cohort and to build an integrated framework. This would allow us to
understand the FANCA associated mechanisms of radioresistance and to discover new putative HNSCC specific oncogenes
as potential therapeutic targets. To study the molecular profile of radiotherapy treated patients on a multi-omics level, we
used the HNSCC dataset available on The Cancer Genome Atlas (TCGA) portal. Copy number variation and clinical data
allowed us to validate the copy number gain and, also, to characterize our subset at the genome level. We want to link clinical
data with copy number changes, as well as with mRNA and miRNA expression levels of the investigated HNSCC cohort on
genome-wide level. To integrate the three omics data, we have first designed a data-driven method, called miRlastic, for
the identification of mRNAs targeted by microRNAs through a penalized elastic net model coupled to prior knowledge of
target predictions. Functional annotation of the multi-omics regulation network was achieved by a scoring approach of the
local neighbourhoods in the network. The next step lies in cross-linking the mRNA and miRNA level with genomic copy
number data. The overall aim comprises the challenge of validating and identifying novel putative oncogenes involved in the
regulatory network of HNSCC and genes that affect the patient outcome.
PP 7-20
Modelling the crosstalk between DNA methylation and histone modifications in cancer
Przybilla, J.1, Rohlf, T.1, Loeffler, M.1,2, Galle, J.1
1
2
Leipzig University, Interdisciplinary Center for Bioinformatics, Leipzig, Germany
University Leipzig, Institute for Medical Informatics, Statistics and Epidemiology , Leipzig, Germany
Aberrant DNA methylation is a characteristic feature of various types of cancer and in several cases has been used to classify
tumor subtypes with different clinical outcome. It was shown that promoters associated with nucleosomes that carry trimethylation of lysine 27 at histone 3 (H3K27me3) are prone to become methylated in cancer. Tri-methylation of lysine 4 at
histone H3 (H3K4me3) on the other hand is known to protect CpG-rich promoters from getting DNA methylated. Here we
ask how crosstalk between different epigenetic regulators generates experimentally observed DNA methylation pattern.
We introduce a computational model framework that allows simulating the transcriptome and the epigenome of stem cells
during homeostasis and cancer development. Each cell in the model contains an artificial genome. The artificial genome
encodes a transcription factor network that, together with histone modifications and DNA methylation, controls gene
expression, which itself feeds back on the epigenome. In simulations of our model we analyze the regulatory states of more
than hundred cells, thus bridging the molecular-, cellular-, and population scale of description.
We demonstrate that increased proliferation and aberrant activity of epigenetic modifiers can affect the stability of
histone modifications and consequently the DNA methylation pattern. We find that hyper-methylation of CpGs following
accelerated proliferation occurs frequently at regions that are associated with H3K27me3 modified chromatin, whereas
hypo-methylation occurs in regions associated with stable H3K4me3. Our model suggests that the specific local combination
of histone modifications determines the possible methylation changes of the associated DNA. We validate this prediction by
re-analyzing data of the TCGA consortium on DNA methylation in colorectal carcinoma samples.
In summary, our computational model allows for the first time to simulate how modified activity of DNA methylation and
histone modifiers in concert might determine the development of specific DNA methylation pattern in cancer.
120
PP 7-21
Importance of rare gene copy number alterations for personalized tumor characterization
Seifert, M.1, Friedrich, B. 2, Beyer, A. 3
Institute for Medical Informatics and Biometry (IMB), Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
Institute of Molecular Systems Biology, Zurich, Switzerland
3
Cellular Networks and Systems Biology, CECAD, University of Cologne, Cologne, Germany
1
2
Copy number alterations (CNAs) of large genomic regions are frequent in many tumor types, but only few of them are
assumed to be relevant for the cancerous phenotype. It has proven exceedingly difficult to ascertain rare mutations that
might have strong effects in individual patients. New systems biological approaches are urgently needed to overcome this.
Here, we show that a genome-wide transcriptional regulatory network inferred from gene expression and gene copy number
data of 768 human cancer cell lines can be used to quantify the impact of individual patient-specific gene CNAs on cancerspecific survival signatures using a specifically designed network impact propagation algorithm. The model was highly
predictive for gene expression in 4,548 clinical samples originating from 13 different tissues. Focused analysis of tumors
from six tissues revealed that in an individual patient a combination of up to 100 gene CNAs directly or indirectly affect the
expression of clinically relevant survival signature genes. Importantly, rare patient-specific gene CNAs (less than 1% in a
given cohort) often have stronger effects on signature genes than frequent gene CNAs. Subsequent integration with genomic
data suggests that frequency variation among high-impact genes is mainly driven by gene location rather than gene function.
Survival analyses on independent tumor cohorts revealed tumor-type specific trends indicating that rare gene CNAs can be
as important as frequent gene CNAs for the prediction of patient survival. Our framework contributes to the individualized
quantification of cancer risk, along with determining individual key risk factors and their downstream targets.
PP 7-22
Microarrays in large-scale phenotyping at the GMC
Söllner, J.1, Fuchs, C.1, Theis, F. J.1
1
Helmholtz Zentrum München, ICB, Neuherberg, Germany
At the German Mouse Clinic (GMC) mouse knockouts (KO) are not only phenotyped with respect to e.g. clinical, behavioural
or neurological parameters but also their gene expression is analysed. Thus, a dataset of 28 Illumina BeadChips is available
comprising 219 samples covering nine organs and ten genetic backgrounds.
In most studies, one is interested in determining differentially expressed genes between mutant and wildtype mice of one
specific KO line.
Here, in contrast, we aim at assessing whether there are strain- or tissue-specific patterns in the transcriptome. In a largescale approach we investigate all available datasets simultaneously.
The findings would facilitate future studies across mouse strains and tissues. For example, one could then compare
transcriptomics data of two KO lines with different genetic backgrounds. Knowing which genes are differentially expressed
merely due to strain-specific patterns helps to narrow down the overall gene list to those relevant for further analysis.
PP 7-24
ModuleDiscoverer: a novel approach to the discovery of disease modules refining
molecular characterization of diet induced rodent models of fatty liver.
Tokarski, C.1,2,3, Guthke, R.1, Schuster, S. 2, Dahmen, U. 3, Vlaic, S.1,2
Hans-Knöll-Institute (HKI) Jena, AG Systemsbiology and Bioinformatics, Jena, Germany
Friedrich-Schiller-University, Department of Bioinformatics, Jena, Germany
3
University Hospital Jena, Friedrich-Schiller-University, General, Visceral and Vascular Surgery, Jena, Germany
1
2
Diet induced rodent models of fatty liver disease are essential in numerous areas of research where the availability of
human tissue is limited due to ethical as well as practical reasons. Therefore, model specific characterization of molecules,
mechanisms and processes involved in the induction and maintenance of steatosis is important for the translation of
experimental results to human pathophysiology of the fatty liver. In the science of networks, recent publications highlight
the advantages of disease module discovery to increase our understanding of diseases on the mechanistic as well as the
systems level. Thus, identification of diet specific disease modules has the potential to enhance existing characterization of
rodent models of fatty liver disease (FLD).
Given published microarray data of four high-fat (HF) diet induced rodent models of the fatty liver we identify diet specific
differentially expressed genes resembling the molecular alterations that can be attributed to the source of fat in the diet,
namely lard (HF-L), olive oil (HF-O), coconut fat (HF-C) and fish oil (HF-F). We then introduce the ModuleDiscoverer
algorithm, a new approach for the ab initio identification of disease modules using protein-protein interaction networks in
conjunction with differentially expressed genes. Our results show that the identified disease modules for HF-O, HF-F, HF-C
121
Program /
Abstracts
are significantly enriched with genes associated to diseases relevant to fatty liver. Structural analysis of the derived dietspecific disease modules revealed key-regulatory genes with central roles in the modules. Among already well known FLDassociated genes, our findings enclose new key-genes potentially involved in the induction of FLD in the respective rodent
models.
We present ModuleDiscoverer, an algorithm suitable for the ab initio identification of disease modules in protein-protein
interaction networks. Its heuristic approach to the detection of communities allows processing of real-world biological
networks at low computational costs. This makes ModuleDiscoverer favorable over current computationally heavy approaches
that, for example, rely on the enumeration of all maximum cliques. Application to experimental data reveals new insides to
mechanisms involved in the induction of FLD. While all diets are known to induce FLD in rodents, on the molecular level the
HF-O diet shows the widest spectrum of human FLD associated pathophysiological characteristics, followed by HF-F and
HF-C.
PP 7-25
Differential gene expression in atrial fibrillation patients
von den Driesch, L.1, Krause, J. 2,3, Müller, C. 2,3, Rzayeva, N. 2,3, Schillert, A.4, Schnabel, R. 2,3, Zeller, T. 2,3, Heinig, M.1
Helmholtz Zentrum München, Institute of Computational Biology, Neuherberg, Germany
University Heart Center Hamburg, Clinic for General and Interventional Cardiology, Hamburg, Germany
3
German Center for Cardiovascular Research (DZHK e.V.), Hamburg, Germany
4
Universität zu Lübeck, Institut für Medizinische Biometrie und Statistik, Lübeck, Germany
1
2
Objective: Atrial fibrillation (AF) is the most common arrhythmia in the general population with high comorbidity,
mortality and significant public health impact. So far, the molecular mechanisms underlying AF are largely unknown. The
first step of investigating candidate genes, which associate with AF, was the analysis of the differential gene expression of
population-based cohort with 83 individuals including 26 AF cases.
Materials and methods: We performed transcriptome analysis in right atrial appendage tissue samples of 84 patients, who
had a bypass surgery. Among those 26 AF cases, some were diagnosed with prevalent AF, while others developed incident
AF after surgery. Gene expression values of mRNA and lincRNAs were measured on an Affymetrix Gene 2.0 ST chip. Visual
quality control was performed using principle components analysis, batch effects were removed using combat and potential
hidden confounders were detected using PEER. To focus on the AF related gene expression changes, the ongoing analysis
introduces three groups of cardiovascular risk factors and biomarkers into a linear model to adjust for the influence of
other confounding factors. These groups consist of a simple correction by age and sex, medical conditions (e.g. Myocardial
Infarction) and molecular phenotypes (e.g. Nt-proBNP). Furthermore, to identify the underlying regulatory mechanisms,
the differentially expressed genes will be integrated with predicted transcription factor binding affinities to highlight the
transcription factors related to AF.
Conclusion: We aim to verify already published and identify novel candidate genes, which correlate both with prevalent
and incident AF cases. After confirming those differentially expressed genes by quantitative PCR, the next step will
be a molecular characterization by using different in vitro approaches including siRNA-mediated silencing or targeted
overexpression of the identified transcription factors, to confirm their direct target genes.
PP 7-26
Integrative analysis of exome, transcriptome and cellular bioenergetics profiles in patients
with rare mitochondrial diseases
Yepez, V.1, Kremmer, L.1, Bader, D.1, Prokisch, H.1, Gagneur, J.1
TUM, Department for Bioinformatics, Garching, Germany
1
Multi-omics studies that include analyzing the exome, transcriptome, proteome and phenome in an integrated manner
are gaining popularity because they can provide deeper insights into biological systems. As such, we investigate certain
phenotypic traits of fibroblast cell lines, originated from patients with rare mitochondrial diseases. Because the predominant
physiological function of mitochondria is the generation of ATP by oxidative phosphorylation (OXPHOS) during cellular
respiration, we are interested in measuring traits related to that biological process. So far, we have oxygen consumption rate
(OCR) and extracellular acidity rate (ECAR), that reflect OXPHOS and glycolytic metabolism, respectively. Taken at different
experimental conditions, OCR and ECAR can give insight into specific bioenergetics like basal and maximal respiration
and ATP production. We then compare the results for each patient with their transcriptome and find genes related to
each possible dysfunction. The long term scope of the project is to join these findings with ones obtained from exome and
proteome analyses and do a full multi omics study.
122
INDEX
A
Abdukerim A. 111
Abou-Jaoudé W. 67
Adamson A. 38, 57, 60
Adlung L. 44, 67
Adlung L. 68
Admon Y. 46
Adornetto G. 103
Ahnert P. 109
Ahrends R. 100
Alessandro L. 59
Aliluev A. 57
Alkan O. 45
Allgöwer F. 62
Alon U. 49
Alpert A. 100
Andersen M. K. 52
Anderson A. 32
Andriasyan V. 54
Andrieux G. 68
Angerer P. 55
Ankers J. 60
Appel L. M. 101
Arga K. 112
Arga K. Y. 79, 115, 117
Arvaniti E. 56
Asara J. 104
Asbeh N. 46
Ashall L. 38
Avsec Z. 111
Awais R. 60
Aydin B. 112
B
Bacall F. 90
Bader D. 122
Bagnall J. 35
Baier V. 103
Bajikar S. S. 58
Bakermans O. 93
Bakker B. 46
Balbus J. 84
Ballnus B. 101
Balsa-Canto E. 62
Banga J. 62
Bao J. 67
Barberis M. 111
Baretton G. B. 114
Bartenschlager R. 39
Bathen T. F. 52
Baum K. 39, 68
Bazil J. 93
Beard D. 93
Bruggeman F. 82
Dalmasso G. 88
Beaudette P. 39
Brühlmann D. 41
Damm G. 48, 39
Beckers J. 57
Brümmendorf T. 96
Daniels D. 38
Becker T. 114
Brummer, T. 28
Davies D. 47
Belderbos M. E. 46
Brusch L. 35, 102, 89, 114
de Back W. 42, 89
Belicova L. 53
Buard V. 91
Deharde D. 48
Bell E. 36
Buchholz V. 58
de Jonge N. 70, 46
Benary U. 69
Bueb J.- L. 85
de Landtsheer S. 74
Bergey F. 113
Buettner F. 58
del Mistro G. 74
Bergheim I. 31
Buettner M. 58
Depner S. 44, 79
Berndt N. 83
Bulik S. 83, 102
Deshpande S. 108
Berndt N. 82
Bullinger E. 77
Deutsch A. 35, 89
Bertilsson H. 52
Burkhart J. M. 99
De Vos W. 36
Beyer A. 121
Busch D. 58
Diaz-Zuccarini V. 97
Beyer H. 69
Busch H. 80
Diego J. 29
Bielow C. 82
Busch H. 68, 67
Diken M. 101
Billing U. 56, 77
Buske P. 42
Dirksen U. 119
Binder M. 39, 78
Büttner F. 55
Dittmar G. 39
Bitzer M. 94
Büttner M.1 55, 57
Dittrich A. 56
Dittrich A. 77
Björkegren J. 34
Björkegren J. L. 110
C
Divine M. 89
Björkhem I. 87
Campillos M. 103
Dooley S. 91
Blankenberg S. 115
Cappell S. 47
Dörffler V.4 76
Blank L. 103
Carpenter A. E. 47
Downton P. 35, 38
Blasi T. 47
Catena R.1 54, 65
Drabløs F. 52
Blüthgen N. 28
Cedersund G. 44, 47
Dräger A.1 81
Boddington C. 35, 38, 57
Celliere G. 43, 88
Drasdo D. 43, 54, 88, 92
Bode J. G.4 39
Cerveira J. 47
Drayman N. 49
Bodenmiller B. 30, 54, 65,
Chakraborty S. 39, 68
Drummond D. 45
Chan P. 91
Dubovik T. 46
66, 61, 64, 81
Boegel S. 105
Chan Y. H. 117
Boekholdt M. 93
Chaouiya C. 67
E
Boerries M.4 80, 5 67
Chara O. 35
Ebrahim A. 81
Bohl S. 80
Chaudhuri T. 108
E. de Bont S. J. 46
Böhm M. E. 39, 44, 79
Chis O.- T. 62
Egners A. 82
Boissier N. 43, 88
Christen H. 77
Ehlting C. 39
Boley D. 85
Chung M. 47
Ehninger G. 114
Borgos S. E. 52
Claassen M. 56
Eils R. 60
Börner K. 59
Clarke K. 71
Eisfeld A. 40
Borries M. 68
Clausznitzer D. 78
Eissing T. 104
Borth N. 83
Colomé Tatché M. 46
Enard W. 38
Böttcher A. 57
Coman C. 100
Engesser R. 69
Botvinick E. 51
Cordes H. 103
England H. 113
Boyd J. 35, 38, 60
Corne T. 36
Engström M. 47
Bradley A. 46
Cramer T. 82
Eraslan G. 114
Braun, S. 28, 59, 72, 80
Cvitanović T. 40
Erdem M. 82
Erdmann T. 83
Brehme M. 96
Breuhahn K. 54
D
Erhart W. 102
Bridge L. 60
Dahl A. 114
Eyerich K. 118
Brieu R. 95
Dahmen U. 122
Eyerich S. 118
Brignall R. 35
d’Alché-Buc F. 91
Broly H. 41
D’Alessandro L. A. 92, 80
Brosch M. 114, 102
Dallmann A. 104
123
Program /
Abstracts
F
Guthke R. 80, 122
Faryna M. 101
Holzhütter H. 95
Kanev K. 66
Holzhütter H. - G. 80, 82, 83
Kania R. 90
Fehling-Kaschek M. 70
H
Hopf T. 92
Kankeu C. 71
Ferronika P. 46
Haanstra J. 82
Hoppe A. 80
Kaoma T. 85
Feuerhake F. 95, 98
Haase T. 115
Horan S. 51
Karlsson M. 47
Fiedler A. 70
Haghighi E. 113
Horger M. 94
Kar S. 67
Filby A. 47
Haghverdi L. 55, 58
Horn M. 59
Kaschek D. 70, 72, 76, 77, 107
Filiou M. 104
Hahlbrock J. 105
Hoser J. 118
Kastenmüller G. 61
Findeisen R. 77
Hall R. 116
Hoshino D. 78
Katsura H. 40
Florez A. 36
Halsema N. 46
Hotfilder M. 119
Kauczor H.- U. 54
Floßdorf M. 58, 101
Hamacher-Brady A. 52, 88
Howard M. 34
Kawakami E. 40
Foijer F. 46
Hammad S. 91
Huang X. 39, 59, 72, 80
Kawaoka Y. 40
Forestier G. 95
Hampe J. 53, 102, 114
Huber H. 70, 71
Kawata K. 78
Foroughi Asl H. 110
Hanscho M. 83
Hucho T. 63
Kazemier H. G. 46
Forsgren M. 47
Harder N. 59
Hug S. 71, 101
Kegel V. 48
Förster K. 108
Harper C. 38, 60
Huppelschoten S. 72, 76
Keller S. 71
Franke A. 44
Hasenauer J. 4
3, 60, 62, 63,
Hutter S. 41
Kempa S. 82
70, 71
Frankish J. 78
Kessler T. 43,110
Frensing T. 28
Hasenauer J. 101
I
Khatri P. 100
Freyer N. 48
Hasenclever D. 110
Iber D. 51
Kheifetz Y. 107
Friebel A. 88
Hassan R. 49, 109
Ince I. 104
Kierzek A. M. 33
Friedrich B. 121
Hassel J. 101
Ingraham J. 92
King Z. A. 81
Fritsche-Guenther, R. 28
Hass H. 79
Intosalmi J. 117
Kitano H. 40
Fröhlich F. 43
Hastreiter M. 118
Ipenberg I. 39
Kittner M. 113
Fröhlich F. 60, 62
Hatano A. 78
Irmler M. 57
Klarner H. 73
Fuchs C. 58, 83, 121
Hatzikirou H. 95
Israel A. 63
Klemm S. 28
Fujii M. 78
Hatz K. 101
Ito Y. 78
Klingmueller U.2 92
Fuji K. 40
Hausser A. 70
Itzkovitz S. 36
Klingmüller U. 39, 44, 67, 68,
Hawe J. 116
Hecht A. 69
J
Klipp E. 44
Gage M. 97
Heemskerk J. W. M. 99
Jackson D. 57, 60
Kneissl J. 71
Gagneur J. 111, 122
Heinäniemi M. 85
Jackson H. 54, 65
Koeppl H. 61
Galleguillos S. 83
Heinig M. 113, 116, 122
Jagiella N. 92
Kofahl B. 39, 68, 69
Galle J. 42, 120
Heinonen M. 91
Jaillet C. 91
Kok K. 46
Galliani S. 89, 97
Heldt F. S. 28
Jaimovich A. 47
Kondofersky I. 80, 83
Garzorz N. 118
Hellmann I. 38
Jakalski M. T. 119
König M. 95
Gebhardt R. 84, 86, 114
Hempel G. 104
Janes K. A. 58
Korf U. 70
Gebhardt R. 87
Hengstler J. 4 3, 49, 88, 91,
Janssens S. 70
Kori M. 117
Jarsch M. 44
Korsching E. 119
92, 109
Georgi F. 54
124
72, 76, 79, 80, 92
G
Georg J. 75
Hennig H. 47
Jaster R. 76
Koschmieder S. 96
Ghallab A. 43, 49, 109
Herberg M. 42
Jeneson J. 93
Koshkaryev A. 45
Glauche I. 42, 55
Hergenröder R. 91
Jeneson T. 94
Kramer B. A. 44
Goble C. 90
Herrmann F. 76
Jeruc J. 87
Krantz M. 73
Golebiewski M.1 90, 105, 106
Herr, R. 28
Jeske T. 118
Kranz L. M. 101
González-Vallinas M. 54
Hess W. 75
John E. 85
Krause J. 115, 122
Goria T. 40
H. Groen J. M. 46
Jones N. 60
Krause L. 118
Görlitz L. 101
Hilgendorff A. 108
Joshi T. 97
Krebs O. 90
Gov E. 115, 117
Hiller T. 48
Jünger A. 72
Kremmer L. 122
Grabe N. 54
Hiltermann T. J. N. 46
Jüngst C. 102
Kretschmer L. 58
Graef P. 58
Hinz J. 100
Juvan P. 87
Kretzschmar G. 102
Grandclaudon M. 67
Ho A. D. 44
Green V. 50
Hochrath K. 116
K
Kreutz C. 79
Gretz N. 80
Hoehme S. 88, 92
Kaderali L. 76,78, 114
Kreutz C. 39
Grimm D. 59
Hoekstra-Wakker K. 46
Kakuda H.
Kreuz M. 59
Grünhage F. 116
Höfer T. 36, 58, 101
Kubota H. 78
Kubica K. 84
Guillouzo A. 107
Höfer T. 67
Kalaidzidis Y. 53
Kubot H. 41
Guipaud O. 91
Hoffmann A. 30
Kaleta C. 85
Kuchen E. 36
Gunawan R. 41
Hofmann U. 109
Kalkan T. 42
Kuepfer L. 103, 109
Guryev V. 46
Hollmann S. 105, 106
Kallenberger S. 60
Kuepfer S. 109
Kreutz C. 87
Küffner R. 118
Mansour A. 100
N
R
Küffner R. 65
Marchesini G. 95
Nahlik T. 53
Raeth S. 70
Kulms D. 74
Marin Zapata P. A. 52
Navarro P. 105
Raghuraman S. 65
Kumar S. 61
Marioni J. 30
Nederveen A. 93
Rand D. 31
Kunida K. 78
Markowetz F. 37
Nguyen Q. 90
Rapsomaniki M. A. 64
Küpfer L. 114
Marks D. 92
Nicot N. 85
Rateitschak K. 76
Kupke S. Y. 28
Marks D.S. 28
Nikolaew A. 105
Raue A. 44, 45, 79
Kuritz K. 62
Marr C. 55
Nikos V. 85
Rautio S. 117
Kuroda S. 41, 78
Martin V. 70
Niranjan V. 108
Rees P. 47
Kurup A. 51
Mastrobuoni G. 82
N. K. N 108
Regierer B. 105, 106
Matsuoka Y. 40
N. Mattheij J. A. 99
Reichl U. 28
L
Matz-Soja M. 84, 86
N. Müller S. 80
Reif R. 49
Laeremans T. 70
Matz-Soja M. 87, 114
Nordgård A. 52
Reinz E. 70
Lähdesmäki H. 117
Mazurov V.2 35
Normand R. 46
Rennert C. 84, 86
Lahrmann B. 54
McDonagh C. 45
Nouailles-Kursar G. 109
Reuss M. 89, 94, 97
Lam C.1 38
Meichsner J. 59
Nussbaumer M. 104
Rey M. 90
Lammert F. 102, 116
Menck K. 100
Nyman E. 44
Rise K. 52
Landgraf R. 104
Menni C. 61
Lang C. 51
Merino Tejero E. 82
O
Robijns J. 36
Lange B. 43
Merkle R. 44, 79
Offringa R. 101
Rodrigo Albors A. 35
Lansdorp P. M. 46
Merkt B. 76
Ohno S. 78
Rodriguez-Gonzalez A. 44
Lapin A. 94
Mertes C. 111
Olesch J. 54
Rodriguez Martinez M. 61,64
Lattermann S. 44, 67
Mewes W. 118
Oppelt A. 72, 76
Roeder I. 42, 55
Lauffer F. 118
Meyer-Hermann M. 98
Oprea T. 32
Roelli P. 66
Lehmann, N. 28
Meyer K. 102
Ostrenko O. 102
Roesch K. 110
Lehmann W. D.1 44
Meyer M. 104
Othman A. 91
Rohlf T. 120
Lehmann W. D.2 79
Meyer R. 80
Owen S. 90
Rohr K. 59
Lehrach H. 43
Meyer T. 47
Leonhardt H. 38
Michos O. 51
P
Ross E. 37
Lerman J. A. 81
Miller P. C. 81
Palsson B. O. 81
Rost F. 35
Lewis N. E. 81
Milliat F. 91
Papoutsopoulou S. 113
Röthemeier C. 115
Lickert H. 57
Mintser O. 75
Parekh S. 38
Rother K. 42
Ligon T. 62
Moeller K. 63
Passante E. 70, 71
Rowe W. 35, 38
Li J. 74
Molenberghs F. 36
Paszek P.1 3 5, 38, 57,60, 64,
Rozman D. 40, 87
Liu X. 103
Möller P. 85
Loeffler M. 4
2, 59, 107, 109,
Molnos S. 119
Patterson J. O. 47
Rudolph N. 77
Montazeri M. 96
Peckys D. 70
Rummel H. 56, 77
Loos C. 63
Monteiro P. T. 67
Pelkmans L. 50
Ryan S. 38
Lopez Alfonso J. C. 95
Morales-Navarrete H. 53
Perse M. 87
Ryan S. 60
Lorbek G. 87
Morbidelli M. 41
Pfau T. 85
Rychtarikova R. 53
Lotz J. 54
Morkel, M. 28
Pichardo-Almarza C. 97
Rye M. 52
Lübberstedt M. 48
Moškon M. 40
Pineda-Torra I. 97
Ryl T. 36
Luber B. 71
Mraz M. 40
Pires Pacheco M. 85
Rzayeva N. 122
Lucarelli P. 74
Muckenthaler M. U. 80
Pitea A. 120
S
Lu J. S. 81
Muders M. 114
Poelwijk F. 92
Saber A. 46
Lundberg P. 47
Mueller N. S. 114, 118
Politi A. 68
Saez-Rodriguez J. 66
Lundengård K. 47
Mueller-Roeber B. 105
Pollak N. 62
Sahay A. 28
Lun X. 61, 64
Mueller W. 90
Porubsky D. 46
Sahin N. 105, 111
Lutter D. 63
Müller B. 54
Poschke I. 101
Sakabe S. 40
Müller C.2 101, 115, 122
Prasanna A. 108
Salopiata F. 44, 79
M
Müller F. 62
Preusse M. 114
Sander C. 29, 92
Maccarrone G. 104
Müller N. S. 120
Prokisch H. 111,122
Sano T. 78
Mader W. 75
Müller S. 80
Przybilla J. 42, 120
Sass S. 57, 108, 120
Maemura T. 40
Muramoto Y. 40
Maiwald T. 39
Murphy M. 104
Q
Satoshi F. 40
Malenka M. 75
Murphy R. 29
Quagliarini F. 116
Sauter T. 74, 85
120
Robichon K. 39
Rosenblatt M. 77
113
Ruckerbauer D. E. 83
Sato M. 78
Malkusch S. 76
Schaadt N. 95, 98
Mallela N. V. 119
Schafmayer C. 102, 114
Mansmann U. 74
Schaper F. 56, 77
125
Program /
Abstracts
Schapiro D. 54, 65, 66
Spierings D. C. J. 46
V
Wolf J. 39, 68, 69
Scheidereit C. 39
Spiller D. 35, 38, 60
Vallar L. 85
Wolkenhauer O. 76
Schelker M. 44, 79
Springer M. 92
van den Berg A. 46
Wolstencroft K. 90
Schenk A. 109
Stanford N. 90
van den Bos H. 46
Wrona A. 84
Schild H. 105
Starosvetsky E. 46, 100
Van der Hoeven F. 59
Wuchter P. 44
Schillert A. 122
Starruß J. 89
van der Wekken A. J. 46
Schilling M. 59
Steiert B. 44, 79
van de Water B. 72, 76
Y
Schilling M. 39, 44, 67, 68,
Sten S.1 47
van Ham M. 111
Yakimovich A. 54
Stenzig J. 115
Vanlier J. 59, 72
Yepez V. 122
Schliemann-Bullinger M. 77
Stepath M. 44
Van Niekerk D. 90
Yilmaz Z. B. 39
Schmidt G. 92
Sterr M. 57
van Oudenaarden A. 28
Yin Y. 54
Schmidt L. 35
Stettler M. 41
Vatlitsov D. 75
Yugi K. 41, 78
Schmiedel J. M. 28
Steuer R. 68
Vaudry D. 91
Schnabel R. 122
Strålfors P. 44
v.d. Stel W. 72
Z
Schneider A. 39
Strijkers G. 93
Veltman D. 70
Zahedi R. 99
Schneider C. 48
Strobel O. 101
Verschuuren M. 36
Zander J. 105
Schoeberl B. 45
Stys D. 53
Vieth B. 38
Zanghellini J. 83
Scholz M.1 107, 109, 110
Summers H. D. 47
Vignon-Clementel I. 43,
Zanotelli V. 54, 65
Schönmeyer 95
Suzuki Y. 78
54, 88
Zehn D. 66
Schunkert H. 110
Swieringa F. 99
Vignon-Clementel I. E. 54
Zeilinger K. 48
Villiger T. K. 41
Zeissig S. 114
79, 80
Schuppert A. 96, 109, 101
Schuster S. 85, 122
T
Vilne B. 110
Zeller T. 115
Schweinoch D. 78
Tanaka E. M. 35
Vinh J. 91
Zeller T. 122
Seddek A. 49
Tarlet G. 91
Vlaic S. 80, 122
Zerial M. 53
Sedlaczek O. 54
Taudt A. 46
Vlasov A. 80
Zerial M. 102, 114
Seehofer D. 48
Teif V. 98
Voit E. 81
Zerjatke T. 42, 55
Seehofer D. 39
Telfah A. 91
von den Driesch L. 122
Zhang F. 76
See V. 60
Tenzer S. 105
von Schönfels W. 114
Zhao D. 40
Seggewiss J. 119
Teplytska L. 104
Vvedenskaya O. 82
Zheng Y. 28
Segiova F. 114
Tessem M.- B. 52
Segovia-Miranda F. 53, 102
Teusink B. 82
W
Zierer J. 61
Seifert M. 121
Teutonico D. 109
Wade J. 81
Zitzelsberger H. 120
Seifert S. 53
Thalheim T.1 42
Wagner M. 105
Ziv-Kenet A. 46
Sers, C. 28
Theis F. 43, 47, 55, 57, 58, 60,
Wagner M.- C. 44, 67
Żulpo M.1 84
Sevimoglu T. 79
65, 70, 71, 80, 83, 101, 108,
Waldherr S. 56
Shadrin A. 43
114, 118, 201
Warth A. 54
Shao C. 36
Thieffry D. 67
Wäsch M. 44, 79
Sharanek A. 107
Thiel C. 103
Watanabe S. 40
Shchekinova E. 89, 97
Thiele I. 31
Watanabe T. 40
She B. 67
Timens W. 46
Weaver R. J. 107
Shen Orr S. 46, 100
Timmer J. 6 9, 70, 72, 75, 77,
Weber W. 69
Shoemaker J. 40
79, 87, 107
Weidemann A. 90
Sickmann A. 99
Timmer J. 39, 44, 67, 72
Wemmert C. 95
Sieber, A. 28
Tinneveld T. 82
Werner M. 113
Siebert H. 73
Tokarski C. 122
Westerhoff H. V. 111
Sieprath T. 36
Tönsing C. 87
Westermann F. 36
Sinkkonen L. 85
Trairatphisan P. 74
Weyßer F. 101
Sinnaeve P. 70
Tritschler S. 66
White M. 38, 60
Sison-Young R. 76
T. Sweeney E. 100
White M. 35, 57
Skogsberg J. 110
Turck C. 104
Wienhold S. 109
Smets M. 38
Wierling C. 43
Smith A. 42
U
Wild P. S. 115
Snoep J. 90
Uda S. 78
Willemsen J. 39
Solari F. 99
Uhlenhaut H. 116
Wittig U. 90
Söllner J.1 121
Unger K. 120
Witzel, F. 28
Sonnenschein N. 81
Urlep Z. 40, 87
Witzenrath M. 109
Soumelis V.2 67
Ussar S. 63
Woetzel N. 105
Spector T. 61
Uvarovskii A. 98
Wolf A. 58
Spencer S. 47
126
Ziegenhain C. 38
Wolf F. A. 65
PP01
PP02
PP03
WC
3
1
4
1 Registration Desk
2
2Wardrobe
5 Bio Sys Net
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