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 chronolog

SUBMITTER: Lebre S 

PROVIDER: S-EPMC2955603 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature