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Open Access Highly Accessed Methodology article

Statistical inference of the time-varying structure of gene-regulation networks

Sophie Lèbre12, Jennifer Becq345, Frédéric Devaux6, Michael PH Stumpf17* and Gaëlle Lelandais345*

Author Affiliations

1 Center for Bioinformatics, Imperial College London, London, UK

2 Laboratoire des Sciences de l'Image de l'Informatique et de la télédétection (LSIIT), UMR UdS-CNRS 7005, Université de Strasbourg, Strasbourg, France

3 Dynamique des Structures et Interactions des Macromolécules Biologiques (DSIMB), INSERM U 665, Paris, F-75015, France

4 Université Paris Diderot - Paris 7, UMR-S665, Paris, F-75015, France

5 INTS, Paris, F-75015, France

6 Laboratoire de Génomique des Microorganismes, CNRS FRE 3214, Université Pierre et Marie Curie, Institut des Cordeliers, Paris, France

7 Institute of Mathematical Sciences, Imperial College London, London, UK

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BMC Systems Biology 2010, 4:130  doi:10.1186/1752-0509-4-130

Published: 22 September 2010

Abstract

Background

Biological networks are highly dynamic in response to environmental and physiological cues. This variability is in contrast to conventional analyses of biological networks, which have overwhelmingly employed static graph models which stay constant over time to describe biological systems and their underlying molecular interactions.

Methods

To overcome these limitations, we propose here a new statistical modelling framework, the ARTIVA formalism (Auto Regressive TIme VArying models), and an associated inferential procedure that allows us to learn temporally varying gene-regulation networks from biological time-course expression data. ARTIVA simultaneously infers the topology of a regulatory network and how it changes over time. It allows us to recover the chronology of regulatory associations for individual genes involved in a specific biological process (development, stress response, etc.).

Results

We demonstrate that the ARTIVA approach generates detailed insights into the function and dynamics of complex biological systems and exploits efficiently time-course data in systems biology. In particular, two biological scenarios are analyzed: the developmental stages of Drosophila melanogaster and the response of Saccharomyces cerevisiae to benomyl poisoning.

Conclusions

ARTIVA does recover essential temporal dependencies in biological systems from transcriptional data, and provide a natural starting point to learn and investigate their dynamics in greater detail.