Learning global models of transcriptional regulatory networks from data.

Organisms must continually adapt to changing cellular and environmental factors (e.g., oxygen levels) by altering their gene expression patterns. At the same time, all organisms must have stable gene expression patterns that are robust to small fluctuations in environmental factors and genetic variation. Learning and characterizing the structure and dynamics of Regulatory Networks (RNs), on a whole-genome scale, is a key problem in systems biology. Here, we review the challenges associated with inferring RNs in a solely data-driven manner, concisely discuss the implications and contingencies of possible procedures that can be used, specifically focusing on one such procedure, the Inferelator. Importantly, the Inferelator explicitly models the temporal component of regulation, can learn the interactions between transcription factors and environmental factors, and attaches a statistically meaningful weight to every edge. The result of the Inferelator is a dynamical model of the RN that can be used to model the time-evolution of cell state.