Monthly Seminar Program
Guest Speakers:
Dr. Krasimira Tsaneva-Atanasova
Research Fellow Speaker:
Dr. Jay Moore
Phd Speakers:
Philip Law
Alejandro Esparza-Franco
Violeta Kovacheva
2 November 2012
Seminar program
Time
13:00-14:00
Session
Lunch
Location
Common room
14:00-15:00
Invited guest speaker - Krasimira TsanevaAtanasova
MOAC Seminar room
15:00-15:15
Tea and coffee break
Common room
15:15-16:15
3 Phd Presentations
Phd presentations consist of 15 minute talks
(including questions) audience rotates between
three rooms
Philip Law
Alejandro Esparza-Franco
Violeta Kovacheva
MOAC Seminar Room
WSB 325
WSB 336
16:15-16:20
Break
Common room
16:20-16:45
Research Fellow - Jay Moore
MOAC Seminar room
16:45 onwards
Wine and Cheese
Common room
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Presentation Description
Guest Speaker Session
Krasimira Tsaneva-Atanasova
Decoding pulsatile GnRH signals
Gonadotrophin-releasing hormone (GnRH) is a hormone released from the
brain to control the secretion of reproductive hormones. Pulsatile GnRH
can increase fertility (e.g. in IVF programmes) whereas sustained GnRH
reduces fertility (and is used to treat hormone-dependent cancer) but the
ways in which the GnRH receptor and its intracellular signalling cascade
decode these kinetic aspects of stimulation are essentially unknown. In addition, our knowledge is scarce of the intracellular mechanisms that govern
frequency modulation of gonadotropins secretion, much less how such finetuning is regulated by different signal inputs. There is an emerging concept
that differential expression of gonadotropin subunits gene is associated with
modification of activation and/or stability of important regulatory proteins
and transcription factors.
We present a signalling pathway model of GnRH-dependent transcriptional activation developed to dissect the dynamic mechanisms of differential regulation of gonadotropin subunits gene. The model incorporates key
signalling molecules, including extracellular-signal regulated kinase (ERK)
and calcium-dependent activation of Nuclear Factor of Activated T-Cells
(NFAT), as well as translocation of activated/inactivated ERK and NFAT
across the nuclear envelope. We show that simulations with varying in
dose and frequency GnRH pulsatile inputs agree very well with experimental measurements of GnRH-dependent ERK and NFAT responses. In silico
experiments designed to probe trancriptional effects downstream of ERK
and NFAT reveal that interaction between transcription factors is sufficient
to account for frequency discrimination. Using parameter sensitivity and
bifurcation analysis we identify key parameter relationships that govern differential expression of gonadotropin subunits gene.
Finally, in order to elucidate the relationship between topology and function, we investigate the response of elementary adaptive circuits to periodic
stimulation. We compute input-output functions (period and amount of
maximal output versus pulse width) that are easily accessible experimentally. Characteristic signatures for different circuit types manifest themselves and are independent of kinetic or cooperativity parameters. These
indicators also reveal the type of circuit topology at the core of our detailed
signalling pathway model.
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Research Fellow Session
Jay Moore
Visualising Interactions, Domains, Experiments and Annotations:
the PRESTA IDEAs synthesis
Systems Biology projects typically integrate diverse data on dynamic changes
in and interactions between entities of biological interest from published
sources and novel experiments. Analytical techniques are key to extracting useful information from high-throughput omics data, but being able to
visualise data in context is also vital for biologists to make meaningful interpretations. Accordingly we developed a database framework to capture key
data sets including text mining results from literature abstracts, published
gene-gene interaction data and protein and promoter structure information,
and to use these data as context for browsing timeseries omics data, results
from mathematical modeling, and hybridisation and mutant phenotype experiments. We call this synthesis the IDEAs database. The IDEAs database
models published, observed and predicted gene interactions as a graph, using
node and edge attributes to capture information relating to individual genes
or experiments. The database is accessible via a website allowing search by
gene or groups of genes, navigation by browsing the network structure, and
access to experimental and modeling results by clicking on nodes and edges
in the graph. The PRESTA project investigates plant responses to environmental stress in Arabidopsis, integrating high-resolution transcriptomic
timeseries experiments with network modeling to propose key components
of the regulatory networks orchestrating plant responses to stress. Components (nodes and edges in the network) are validated experimentally by
mutant phenotype, hybridisation and expression experiments. We used the
IDEAs model to visualise these integrated data. In addition to supporting
biological interpretation of PRESTA experimental results, we have begun
to use the IDEAs model as a crowdsourcing tool to employ human judgement to rate believability of gene-gene interactions on the basis of review
and curation of experimental evidence.
Phd Session 1:
Philip Law
Parametric clustering of time series gene expression data
After performing genomic experiments, clustering is often used to determine
the biological function of a set of genes. To do this, genes are grouped together with the hypothesis that genes that have similar expression profiles
are involved in similar functions. Multiple variations of clustering exist, and
range from simple hierarchical clustering to clustering using Bayesian models. However, in these clustering methods, similarity is primarily based on
how closely experimental profiles fit to each other, and there is no defini3
tion of what a cluster should look like. In addition, different approaches are
used to determine the underlying biology of a set of genes, such as GO or
motif analyses, and these may give varying results. To this end, regression
analyses are used to fit functional curves to gene expression profiles, and
clustering is based on the fitted parameters to provide a better means of
identifying co-regulated genes.
Phd Session 2:
Alejandro Esparza-Franco
Towards model-based control of cell signalling
In the past ten years, synthetic biology has refined a toolkit of strategies for
manipulating the sensors, sensor-transducer interactions, and downstream
targets of cell signalling. This technology enables the rewiring of signalling
pathways to produce novel cellular behaviours. Reengineering cell signalling
circuits has the potential to deliver a wide array of technological benefits that
range from designing microorganisms that perform industrial tasks, to the
reprogramming of human cells for therapeutic purposes, however, until now
most synthetic biology projects have been driven by intuition, thus, making
it hard to anticipate potential side effects of performing a modification. To
enable the implementation of more sophisticated manipulation strategies
will require a shift towards model-based design, as traditional engineering
disciplines have done.
Phd Session 3:
Violeta Kovacheva
Towards Protein Network Analysis for Colon Cancer Using the
Toponome Imaging System
In order to understand cellular biology on a systems level, relationships
between molecular components must be understood not only at a functional level but also localised in the spatial domain. As a consequence,
new bioimaging techniques, such as the Toponome Imaging System, have
been recently proposed to visualise the co-location or interaction of several
proteins in the same tissue specimen. A novel method for analysing multivariate images has been presented. It is different from previously proposed
methods in that it considers the samples at cell rather than pixel level, it
is intensity independent, and it allows phenotyping of cells based on their
protein co-expression profile.
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