Computational analysis of biological
systems: Past, present and future
Sven Bergmann
UNIL tenure track commission
5 January 2010
Research Overview
Large (genomic) systems
Small systems
• many uncharacterized
• elements well-known
elements
• relationships unknown
• computational analysis should:
• many relationships established
• aim at quantitative modeling of
systems properties like:
 improve annotation
 Dynamics
 reveal relations
 Robustness
 reduce complexity
 Logics
PAST
Large-scale data analyses
How to extract information from very
large-scale expression data?
Search for transcription modules:
Set of genes co-regulated under
a certain set of conditions
• context specific
• allow for overlaps
J Ihmels, G Friedlander, SB, O Sarig, Y Ziv & N Barkai Nature Genetics (2002)
Identification of transcription modules
using many random “seeds”
random
“seeds”
Transcription
modules
Independent
identification:
Modules may
overlap!
SB, J Ihmels & N Barkai Physical Review E (2003)
New Tools: Module Visualization
http://maya.unil.ch:7575/ExpressionView
Data Integration: Example NCI60
60 cancer cell lines
(9 tissue types)
Drug
Response
Data
~5,000 drugs
Gene
Expression
Data
~23,000 gene probes
How to identify Co-modules?
Iteratively refine genes, cell-lines
and drugs to get co-modules
Z Kutalik, J Beckmann & SB, Nature Biotechnology (2008)
6’189
individuals
CoLaus = Cohort Lausanne
Genotypes
Phenotypes
500.000 SNPs
159 measurement
144 questions
Collaboration with:
Vincent Mooser (GSK), Peter Vollenweider & Gerard Waeber (CHUV)
PCA of POPRES cohort
Impact: Web of Science 2005-2009
Impact: Who cites our work?
PRESENT
Large-scale data analysis
Current insights from GWAS:
• Well-powered (meta-)studies
with (ten-)thousands of samples
have identified a few (dozen)
candidate loci with highly
significant associations
• Many of these associations
have been replicated in
independent studies
Current insights from GWAS:
• Each locus explains but a tiny (<1%)
fraction of the phenotypic variance
• All significant loci together explain
only a small (<10%) of the variance
David Goldstein:
“~93,000 SNPs would be required to explain
80% of the population variation in height.”
Common Genetic Variation and Human Traits,
NEJM 360;17
So what do we miss?
1. Other variants like Copy Number
Variations or epigenetics may play an
important role
2. Interactions between genetic variants
(GxG) or with the environment (GxE)
3. Many causal variants may be rare
and/or poorly tagged by the measured
SNPs
4. Many causal variants may have very
small effect sizes
Status:
- Dec: submitted to PLoS Computational Biology (IF=6.2)
(after positive reply to pre-submission inquiry)
Status:
accepted for
publication in
Nature (IF=31.4 )
Status:
- Dec: submitted
to PLoS Genetics
(IF=8.7),
currently under
review
Status:
- submitted to Biostatistics (IF=3.4, 2nd best of 92 journals for
Statistics & Probability)
- Revision accounting for reviewers’ comments to be submitted soon
Status: accepted for publication GASTROENTEROLOGY (IF=12.6).
Status: submitted as application note to Bioinformatics (IF=4.32,
2nd best of 28 journals for Mathematical & Computational Biology)
Status: manuscript ready for submission to PLoS Comp Biology
Research Overview
Large (genomic) systems
Small systems
• many uncharacterized
• elements well-known
elements
• relationships unknown
• computational analysis should:
• many relationships established
• aim at quantitative modeling of
systems properties like:
 improve annotation
 Dynamics
 reveal relations
 Robustness
 reduce complexity
 Logics
PAST
Modeling
Drosophila as model for Development
Quantitative Experimental Study
using Automated Image Processing
a: mark anterior and posterior pole, first and last eve-stripe
b: extract region around dorsal midline
c: semi-automatic determination of stripes/boundaries
Experimental Results: Positions
• Bergmann S, Sandler S, Sberro H, Shnider S, Shilo B-Z, Schejter E and Barkai N
Pre-Steady-State Decoding of the Bicoid Morphogen Gradient, PLoS Biology 5(2) (2007) e46.
• Bergmann S, Tamari Z, Shilo B-Z, Schejter E and Barkai N
Stability of the Bicoid Gradient? Cell 132 (2008) 15.
The
Canonical
Model
A bit
of Theory…
The morphogen density M(x,t) can be modeled by a
differential equation (reaction diffusion equation):
Change in
concentration of
the morphogen
at position x, time t
Diffusion
D: diffusion const.
Degradation
α: decay rate
Source
Model including nuclear trapping
M
2M
N  k  n M n  s0 ( x )
 D 2  kn MB
t
x
M n
 kn MB
N  k n M n
t
nuclear morphogen
Mn(x,t)
nuclear absorbtion
nuclear emission
free morphogen
kn
production
s0
k-n
M(x,t)
diffusion
D
Nuclei density
NB(x,t)
PRESENT
Modeling
Precision is highest at mid-embryo
1xbcd
2xbcd
4xbcd
Δ: Gt
Δ: Kr
□: Hb
Similar
trend in
o: Eve
direct measurements
of Bcd noise by
Gregor et al.
(Cell 2007)
Scaling is position-dependent!
“hyper-scaling” at anterior pole
Status:
- May: submitted to Molecular Systems Biology (IF=12.2)
- Aug: first resubmission after mostly positive reviews
- Dec: second submission (informally) accepted subject
to proper response with respect to minor issues
Modeling the Drosophila wing disk
• Partner in SystemsX.ch project WingX
- PhD student: Aitana Morton Delachapelle
- PostDoc: Sascha Dalessi
• Image processing to obtain spatiotemporal measures of proteins
• Modeling Dpp gradient
formation with focus on scaling
Modeling the plant growth
• Partner in SystemsX.ch project PlantX
- PostDoc: Micha Hersch
- PostDoc: Tim Hohm
• Image processing to obtain spatiotemporal measures of seedlings
• Modeling shade avoidance behavior
Future directions
The challenge of many datasets:
How to integrate all the information?
Organisms
?
–
–
–
–
–
Biological
Insight
Genotypic
(SNPs/CNVs)
Epigenetic data
Gene/protein expression
Protein interactions
Organismal data
Data types
Modular Approach for Integrative Analysis
of Genotypes and Phenotypes
Phenotypes
Measurements
Modular
links
Individuals
SNPs/Haplotypes
Genotypes
Association of (average) module expression is
often stronger than for any of its constituent
genes
Towards interactions: Network Approaches
for Integrative Association Analysis
Using knowledge on physical gene-interactions or pathways to
prioritize the search for functional interactions
Modeling:
Cross-talk between Drosophila
and Arabidopsis modeling
Both systems are growing multi-cellular tissues:
Modelers (in my group and within the two RTDs)
may learn from each other and exchange tools
Acknowledgements to my group
People:
Zoltán Kutalik
Micha Hersch
Aitana Morton
Diana Marek
Barbara Piasecka
Bastian Peter
Karen Kapur
Alain Sewer*
Toby Johnson*
Armand Valsessia
Gabor Csardi
Sascha Dalessi
Tim Hohm
*alumni
Funding: SystemsX.ch, SNSF, SIB, Cavaglieri, Leenaards, European FP
http://serverdgm.unil.ch/bergmann
Acknowledgements to my collaborators
DGM:
Jacqui Beckmann
Roman Chrast
Carlo Rivolta
Uni Geneva:
Stylianos Antonarakis
Manolis Dermitzakis
Jacques Schrenzel
Weizmann:
Naama Barkai
Benny Shilo
Orly Reiner
CIG:
Christian Fankhauser
Sophie Martin
Alexandre Reymond
Mehdi Tafti
Bernard Thorens
Uni Bern:
Cris Kuhlemeier
Andri Rauch
Richard Smith
MRC Cambridge:
Ruth Loos
Nick Wareham
UNIL/CHUV:
Murielle Bochud
Pierre-Yves Bochud
Fabienne Maurer
Marc Robinson-Rechavi
Amalio Telenti
Peter Vollenweider
Gerard Weber
EPFL:
Dario Floreano
Felix Naef
Uni Basel:
Markus Affolter
Mihaela Zavolan
ETH & Uni Zurich:
Konrad Basler
Ernst Hafen
Matthias Heinemann
Christian v. Mehring
Markus Noll
Eckart Zitzler
Uni Minnesota:
Judith Berman
GSK:
Vincent Moser
Dawn Waterworth
UCSD:
Trey Ideker
UCLA:
John Novembre
Teaching: Past and Present
http://www2.unil.ch/cbg/index.php?title=Teaching
Teaching: Future
1. How can we equip Biology students at UNIL with
basic knowledge in Computational Biology?
• more “hands on” training!
• group projects
• new Master
2. How can we educate proficient Computational
Biologists?
• New Master program jointly with SIB, UniGE?
• Develop ties with EPFL?
Integration: Past & Present
Integration: Future
How can UNIL/FBM strengthen its position in Computational Biology?
1. Networking!
2. Create new senior positions!
Integration: Future
How can UNIL/FBM strengthen its ties with the industry?
Vincent Moser
David Heard
Ulrich Genick
CBG
Pierre Farmer
Pietro Scalfaro
Andreas Schupert
Manuel Peitsch