%0 Journal Article %T Statistical inference of the time-varying structure of gene-regulation networks. %+ Center for Bioinformatics %+ Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection (LSIIT) %+ Dynamique des Structures et Interactions des Macromolécules Biologiques - Pôle de La Réunion (DSIMB Réunion) %+ Génomique des Microorganismes (LGM) %+ Institute for Mathematical Sciences [London] (IMS) %A Lèbre, Sophie %A Becq, Jennifer %A Devaux, Frédéric %A Stumpf, Michael %A Lelandais, Gaëlle %Z SL and MPHS gratefully acknowledge support from the BBSRC. JB and GL gratefully acknowledge support from the Institut National de la Transfusion Sanguine. MPHS is a Royal Society Wolfson Research Merit Award holder. %< avec comité de lecture %@ 1752-0509 %J BMC Systems Biology %I BioMed Central %V 4 %N 1 %P 130 %8 2010 %D 2010 %R 10.1186/1752-0509-4-130 %M 20860793 %Z Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN] %Z Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] %Z Computer Science [cs]/Bioinformatics [q-bio.QM]Journal articles %X 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. %G English %2 https://inserm.hal.science/inserm-00663719/document %2 https://inserm.hal.science/inserm-00663719/file/1752-0509-4-130.pdf %L inserm-00663719 %U https://inserm.hal.science/inserm-00663719 %~ INSERM %~ UNIV-PARIS7 %~ UPMC %~ UNIV-AG %~ AFRIQ %~ CNRS %~ DSIMB %~ SORBONNE-UNIVERSITE %~ FST-REUNION %~ UNIV-PARIS %~ UP-SCIENCES %~ SU-TI %~ ALLIANCE-SU