Condition-Specific Modeling of Biophysical Parameters Advances Inference of Regulatory Networks

Abstract

Large-scale inference of eukaryotic transcription regulatory networks remains challenging. One underlying reason is that existing algorithms typically ignore crucial biological mechanisms, such as RNA degradation and post-transcriptional processing. Here we describe InfereCLaDR, which incorporates such elements and advances prediction in Saccharomyces cerevisiae. First, InfereCLaDR employs a new, high quality gold standard that we use separately as prior information and model validation. Second, InfereCLaDR explicitly models transcription factor activity and RNA half-lives. Third, it introduces expression subspaces to derive condition-responsive regulatory networks for every gene. InfereCLaDR's final network is validated by known data and trends, and produces multiple new insights. For example, it predicts long half-lives for transcripts of the nucleic acid metabolism genes, and members of cytosolic chaperonin complex as targets of the proteasome regulator RPN4. InfereCLaDR demonstrates that more biophysically realistic modeling of regulatory networks advances prediction accuracy, both in eukaryotes and prokaryotes.