Time-Based Systems Biology Approaches to Capture and Model Dynamic Gene Regulatory Networks

Jose M. Alvarez, Matthew D. Brooks, Joseph Swift, Gloria M. Coruzzi

Research output: Contribution to journalReview articlepeer-review

Abstract

All aspects of transcription and its regulation involve dynamic events. However, capturing these dynamic events in gene regulatory networks (GRNs) offers both a promise and a challenge. The promise is that capturing and modeling the dynamic changes in GRNs will allow us to understand how organisms adapt to a changing environment. The ability to mount a rapid transcriptional response to environmental changes is especially important in nonmotile organisms such as plants. The challenge is to capture these dynamic, genome-wide events and model them in GRNs. In this review, we cover recent progress in capturing dynamic interactions of transcription factors with their targets mdash at both the local and genome-wide levels mdash and how they are used to learn how GRNs operate as a function of time. We also discuss recent advances that employ time-based machine learning approaches to forecast gene expression at future time points, a key goal of systems biology.

Original languageEnglish (US)
Pages (from-to)105-131
Number of pages27
JournalAnnual Review of Plant Biology
Volume72
DOIs
StatePublished - Jun 17 2021

Keywords

  • dynamic network modeling
  • gene regulatory networks
  • systems biology
  • time-based genome-wide studies
  • transcription factor
  • transient regulatory events

ASJC Scopus subject areas

  • Physiology
  • Molecular Biology
  • Plant Science
  • Cell Biology

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