METHODOLOGY ARTICLE Open Access

Statistical inference of the time-varying structure of gene-regulation networks Sophie Lèbre1,2, Jennifer Becq3,4,5, Frédéric Devaux6, Michael PH Stumpf1,7*†, Gaëlle Lelandais3,4,5*† 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 o