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

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—at both the local and genome-wide levels—and using them 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. Expected final online publication date for the Annual Review of Plant Biology, Volume 72 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Dynamic changes in mRNA nucleocytoplasmic localization in the nitrate response of Arabidopsis roots

“…Using cell-fractionation and RNA-seq analysis, we obtained a high-resolution subcellular transcriptome in response to nitrate treatment in Arabidopsis thaliana roots. Thousands of genes have been previously reported as differentially expressed in response to nitrate treatments under various experimental conditions (Wang et al, 2004; Krouk et al, 2010; Canales et al, 2014; Varala et al, 2018; Alvarez et al, 2019; Swift et al, 2020), and several gene expression layers have been described (Vidal et al, 2020; Alvarez et al, 2021). Notwithstanding, we identified 1,183 regulated genes in the subcellular fractions that are not detected as regulated in the total fraction.…”

“…Nitrogen (N) is an essential macronutrient whose availability limits growth and development in plants (Andrews et al, 2013; Gutiérrez, 2013; Fredes et al, 2019; Araus et al, 2020; Vidal et al, 2020; Alvarez et al, 2021). Nitrate is the most abundant source of N in agricultural soils (Owen and Jones, 2001).…”

The biology of time: dynamic responses of cell types to developmental, circadian and environmental cues

“…At the molecular level, TF–target binding is inherently stochastic; TFs are diffuse throughout the nucleus and only by chance find their target binding site. To improve the odds, genomes rely on a range of mechanisms to promote TF–target binding events, such as increasing TF concentration, or changing the epigenetic landscape (Figure 3b; for review, see Alvarez et al., 2021; Swift and Coruzzi, 2017). Importantly, these mechanisms enable plants to align the timing of the transcriptional output to the timing of the environmental cue.…”

“…A central goal of systems biology is to map out the underlying gene regulatory networks that allow plants to respond dynamically to environmental cues (Alvarez et al., 2021). To detect these networks, one popular approach is to track how gene expression dynamics change over time using time‐series RNA‐seq.…”

An Optimal Boolean Approach for Computational Modeling of Gene Regulatory Networks from Temporal Gene Expression Profile

“…Hajiramezanali et al [17] presented optimal classification of cellular trajectories under regulatory model uncertainty based on partially-observed Boolean dynamical systems and noisy gene expression data. In recent years, special attention has been focused on computational modeling and analysis of GRNs based on time-course gene expression data, as reported inliterature [18][19][20], that is the main topic of this study.…”