TY - JOUR
T1 - Time-Based Systems Biology Approaches to Capture and Model Dynamic Gene Regulatory Networks
AU - Alvarez, Jose M.
AU - Brooks, Matthew D.
AU - Swift, Joseph
AU - Coruzzi, Gloria M.
N1 - Funding Information:
Research on dynamic gene regulatory networks in G.M.C.’s laboratory is supported by National Institutes of Health (NIH) grant RO1 GM121753 and National Science Foundation Plant Genome Research Program grant IOS-1840761 to G.M.C., National Institute of General Medical Sciences Fellowship F32GM116347 to M.D.B., and Plant Genomics Grant A160051 from the Zegar Family Foundation. Research in J.M.A.’s laboratory is funded by ANID–Millennium Science Initative Program–Millennium Institute for Integrative Biology (iBio) ICN17_022 and ANID Fondo Nacional de Desarrollo Científico y Tecnológico grant 1210389. J.S. is an Open Philanthropy awardee of the Life Sciences Research Foundation.
Publisher Copyright:
© 2021 Annual Reviews Inc.. All rights reserved.
PY - 2021/6/17
Y1 - 2021/6/17
N2 - 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.
AB - 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.
KW - dynamic network modeling
KW - gene regulatory networks
KW - systems biology
KW - time-based genome-wide studies
KW - transcription factor
KW - transient regulatory events
KW - Plants/genetics
KW - Computational Biology
KW - Transcription Factors
KW - Gene Regulatory Networks
KW - Systems Biology
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U2 - 10.1146/annurev-arplant-081320-090914
DO - 10.1146/annurev-arplant-081320-090914
M3 - Review article
C2 - 33667112
AN - SCOPUS:85108345705
VL - 72
SP - 105
EP - 131
JO - Annual Review of Plant Physiology and Plant Molecular Biology
JF - Annual Review of Plant Physiology and Plant Molecular Biology
SN - 1543-5008
ER -