title: Statistical inference of the time-varying structure of gene-regulation networks.
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.
authors.affiliation: Center for Bioinformatics, Imperial College London, London, UK.
authors.forename: Sophie
authors.initials: S
authors.lastname: Lèbre
authors.forename: Jennifer
authors.initials: J
authors.lastname: Becq
authors.forename: Frédéric
authors.initials: F
authors.lastname: Devaux
authors.forename: Michael P H
authors.initials: MP
authors.lastname: Stumpf
authors.forename: Gaëlle
authors.initials: G
authors.lastname: Lelandais
mesh_terms: D000465:Algorithms; D000818:Animals; D001542:Benomyl / Q000506:poisoning; D004331:Drosophila melanogaster / Q000235:genetics / Q000254:growth & development; D053263:Gene Regulatory Networks; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D012044:Regression Analysis; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000187:drug effects / Q000235:genetics; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D014157:Transcription Factors; D001542:Benomyl