Groups - Lausanne - Computational Systems Biology Lab

Summary

The aim of systems biology is to achieve a quantitative and dynamic understanding of cellular networks by combining experimental data with theoretical and computational methodologies. The Computational Systems Biology (CSB) group’s interest lies in the regulatory and cellular networks involved in oncogenic signalling, cell-cycle regulation, and molecular oscillators. Data obtained from technologies such as microarrays, chromatin-immunoprecipitation (ChIP) and genome sequencing are brought together to discover regulatory dependencies between genes and regulatory proteins involved in cell proliferation. One thematic focus is the study of biomolecular oscillators, in particular the circadian clock.

Introduction

The CSB group develops bioinformatics approaches to investigate gene networks relevant to cancer and chronobiology. It studies how oncogenic signals alter transcriptional programmes in cancers using combined genome-wide technologies such as expression ChIP. Data are used to map relationships between the promoter state and transcriptional responses. The group is also interested in computational approaches to study circadian biology, both through the modelling of circadian pathways to decipher oscillator stability, and the use of comparative genomics to infer cis-regulatory elements at the heart of the clock.

Projects and Services

Analysis methods for chromatin immunoprecipitation experiments

Accurate mapping of binding sites from ChIP experiments is crucial for the analysis of regulatory networks. To this end, methodologies for signal processing developed for microarrays were ported to GeneChip tiling arrays. The Sliding Linear Modelling package (SLM) allows accurate identification of significant sites, and relies on locally linear regression for smoothing.

Integration of location and expression data

Combinatorial gene regulation contributes significantly to phenotypic versatility in higher eukaryotes. Genome-wide ChIP is combined with expression profiling to dissect regulatory circuits. The SLM algorithm is used to study regulators such as sp1, c-Myc and ESR1. In particular, the CSB group is interested in the correlation between promoter occupancy, as measured by binding, and expression of the target gene in different conditions. Using these methodologies, a functionally distinct signature for dual c-Myc/sp1 sites was identified.

Modelling of phase oscillators and bioluminescence recordings

Rhythmic behaviour and physiology in mammals is orchestrated through a complex hierarchy of molecular clocks in which the suprachiasmatic nucleus (SCN) clock acts as master pacemaker and sends synchronization cues to slave or peripheral oscillators. A central question in the field is whether peripheral oscillators are quantitatively similar to those in the SCN, and whether peripheral cells can sustain cell-autonomous, undamped oscillations. Using data from a luciferase reporter, the CSB group has shown that the dominant cause of amplitude reduction is the desynchronization of self-sustained oscillators.

A new model now combines three essential aspects of circadian clocks: the stability of the limit cycle, susceptibility to fluctuations, and intercellular coupling. The precision of individual frequencies, the period dispersion and the coupling strength could be simultaneously estimated. As part of this project biochemical models for circadian clocks were simulated to better understand the stability properties underlying circadian limit-cycle oscillators.

Circadian time series experiments

Molecular circadian rhythms can be probed using time series experiments. Early studies have uncovered broad programmes of rhythmic gene expression both in plants and animals. The CSB group developed statistics to assess cyclers and is now extending these methodologies to GeneChip tiling arrays. Software has been developed that will enable the reliable identification of cycling transcriptional units with high spatial resolution.

Dynamic simulations of small size gene networks

Gene networks can perform their function through time-dependent changes in protein activities. To tackle the problem of how network structure affects its dynamic properties, the BNetDyn algorithm was developed to compute discrete time stochastic dynamic trajectories using logical dynamic update rules. Applied to a model for the yeast cell cycle, analysis pointed to the crosstalk in the cascades downstream of the SBF/MBF transcription activators as the main determinant for checkpoint optimization.