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

• Published in: Annual review of plant biology 2021-06, Vol.72 (1), p.105-131
• Main Authors: Alvarez, Jose M, Brooks, Matthew D, Swift, Joseph, Coruzzi, Gloria M
• Format: Article
• Language: English
• Subjects: Biology; Changing environments; Computational Biology; dynamic network modeling; Environmental changes; Gene expression; Gene regulation; Gene Regulatory Networks; Genomes; Learning algorithms; Machine learning; Plants - genetics; Systems Biology; time-based genome-wide studies; transcription factor; Transcription Factors; transient regulatory events
• ISSN: 1543-5008; 1545-2123
• DOI: 10.1146/annurev-arplant-081320-090914

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-at both the local and genome-wide levels-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.