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

Abstract

Summary: Large-scale inference of eukaryotic transcription-regulatory networks remains challenging. One underlying reason is that existing algorithms typically ignore crucial regulatory 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 high-quality Gold Standard dataset that we use separately as prior information and for 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 results in multiple insights. For example, it predicts long half-lives for transcripts of the nucleic acid metabolism genes and members of the cytosolic chaperonin complex as targets of the proteasome regulator Rpn4p. InfereCLaDR demonstrates that more biophysically realistic modeling of regulatory networks advances prediction accuracy both in eukaryotes and prokaryotes. : This work demonstrates that extending the biophysical accuracy of the assumed model of transcriptional regulation improves large-scale regulatory network inference. As a proof of concept, Tchourine et al. show that incorporating RNA degradation into the model results in better network recovery while simultaneously predicting accurate RNA degradation rates. Keywords: gene regulatory networks, network inference, RNA degradation rates, RNA stability, transcriptional regulatory networks, biophysical modeling, systems biology, machine learning, saccharomyces cerevisiae, network remodeling