Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling

Overview

Journal Front Genet

Date 2020 Jun 18

PMID 32547596

Citations 17

Authors

Lisa Van den Broeck

Max Gordon

Dirk Inze

Cranos Williams

Rosangela Sozzani

Affiliations

Soon will be listed here.

Abstract

Plant responses to environmental and intrinsic signals are tightly controlled by multiple transcription factors (TFs). These TFs and their regulatory connections form gene regulatory networks (GRNs), which provide a blueprint of the transcriptional regulations underlying plant development and environmental responses. This review provides examples of experimental methodologies commonly used to identify regulatory interactions and generate GRNs. Additionally, this review describes network inference techniques that leverage gene expression data to predict regulatory interactions. These computational and experimental methodologies yield complex networks that can identify new regulatory interactions, driving novel hypotheses. Biological properties that contribute to the complexity of GRNs are also described in this review. These include network topology, network size, transient binding of TFs to DNA, and competition between multiple upstream regulators. Finally, this review highlights the potential of machine learning approaches to leverage gene expression data to predict phenotypic outputs.

Citing Articles

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An optimal Bayesian intervention policy in response to unknown dynamic cell stimuli.

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Hong U, Tamiru-Oli M, Hurgobin B, Lewsey M J Exp Bot. 2024; 76(1):35-51.

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