Understanding transcriptional regulatory networks using computational models

Bing He, Kai Tan

Research output: Contribution to journalReview articlepeer-review

24 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)101-108
Number of pages8
JournalCurrent Opinion in Genetics and Development
StatePublished - Apr 1 2016

All Science Journal Classification (ASJC) codes

  • Genetics
  • Developmental Biology


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