title: Integrated module and gene-specific regulatory inference implicates upstream signaling networks.
abstract: Regulatory networks that control gene expression are important in diverse biological contexts including stress response and development. Each gene's regulatory program is determined by module-level regulation (e.g. co-regulation via the same signaling system), as well as gene-specific determinants that can fine-tune expression. We present a novel approach, Modular regulatory network learning with per gene information (MERLIN), that infers regulatory programs for individual genes while probabilistically constraining these programs to reveal module-level organization of regulatory networks. Using edge-, regulator- and module-based comparisons of simulated networks of known ground truth, we find MERLIN reconstructs regulatory programs of individual genes as well or better than existing approaches of network reconstruction, while additionally identifying modular organization of the regulatory networks. We use MERLIN to dissect global transcriptional behavior in two biological contexts: yeast stress response and human embryonic stem cell differentiation. Regulatory modules inferred by MERLIN capture co-regulatory relationships between signaling proteins and downstream transcription factors thereby revealing the upstream signaling systems controlling transcriptional responses. The inferred networks are enriched for regulators with genetic or physical interactions, supporting the inference, and identify modules of functionally related genes bound by the same transcriptional regulators. Our method combines the strengths of per-gene and per-module methods to reveal new insights into transcriptional regulation in stress and development.
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America ; Wisconsin Institute for Discovery, Madison, Wisconsin, United States of America.
authors.forename: Sushmita
authors.initials: S
authors.lastname: Roy
authors.forename: Stephen
authors.initials: S
authors.lastname: Lagree
authors.forename: Zhonggang
authors.initials: Z
authors.lastname: Hou
authors.forename: James A
authors.initials: JA
authors.lastname: Thomson
authors.forename: Ron
authors.initials: R
authors.lastname: Stewart
authors.forename: Audrey P
authors.initials: AP
authors.lastname: Gasch
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D053263:Gene Regulatory Networks / Q000235:genetics* / Q000502:physiology; D006801:Humans; D008957:Models, Genetic; D012441:Saccharomyces cerevisiae; D015398:Signal Transduction / Q000235:genetics* / Q000502:physiology; D012984:Software; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
chemical_list: D014157:Transcription Factors