Understanding transcriptional regulatory networks using computational models

• Published in: Current opinion in genetics & development Vol. 37; pp. 101 - 108
• Main Authors: He, Bing, Tan, Kai
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01-04-2016
• Subjects: Computational Biology; Gene Regulatory Networks - genetics; Genome, Human; Humans; Medical Education; Structure-Activity Relationship; Transcription Factors - chemistry; Transcription Factors - genetics; Transcription, Genetic
• ISSN: 0959-437X; 1879-0380
• DOI: 10.1016/j.gde.2016.02.002

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.