title: Understanding transcriptional regulatory networks using computational models.
abstract: Transcriptional regulatory networks (TRNs) encode instructions for animal development and physiological responses. Recent advances in genomic technologies and computational modeling have revolutionized our ability to construct models of TRNs. Here, we survey current computational methods for inferring TRN models using genome-scale data. We discuss their advantages and limitations. We summarize representative TRNs constructed using genome-scale data in both normal and disease development. We discuss lessons learned about the structure/function relationship of TRNs, based on examining various large-scale TRN models. Finally, we outline some open questions regarding TRNs, including how to improve model accuracy by integrating complementary data types, how to infer condition-specific TRNs, and how to compare TRNs across conditions and species in order to understand their structure/function relationship.
authors.affiliation: Interdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, IA 52242, USA.
authors.forename: Bing
authors.initials: B
authors.lastname: He
authors.affiliation: Interdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, IA 52242, USA; Department of Internal Medicine, University of Iowa, Iowa City, IA 52242, USA. Electronic address: tank1@chop.edu.
authors.forename: Kai
authors.initials: K
authors.lastname: Tan
mesh_terms: D019295:Computational Biology; D053263:Gene Regulatory Networks / Q000235:genetics*; D015894:Genome, Human; D006801:Humans; D013329:Structure-Activity Relationship; D014157:Transcription Factors / Q000737:chemistry / Q000235:genetics*; D014158:Transcription, Genetic
chemical_list: D014157:Transcription Factors