Systems Approaches to Identifying Gene Regulatory Networks in Plants

Abstract

Complex gene regulatory networks are composed of genes, noncoding RNAs, proteins, metabolites, and signaling components. The availability of genome-wide mutagenesis libraries; large-scale transcriptome, proteome, and metabalome data sets; and new high-throughput methods that uncover protein interactions underscores the need for mathematical modeling techniques that better enable scientists to synthesize these large amounts of information and to understand the properties of these biological systems. Systems biology approaches can allow researchers to move beyond a reductionist approach and to both integrate and comprehend the interactions of multiple components within these systems. Descriptive and mathematical models for gene regulatory networks can reveal emergent properties of these plant systems. This review highlights methods that researchers are using to obtain large-scale data sets, and examples of gene regulatory networks modeled with these data. Emergent properties revealed by the use of these network models and perspectives on the future of systems biology are discussed.

Inference of Longevity-Related Genes from a Robust Coexpression Network of Seed Maturation Identifies Regulators Linking Seed Storability to Biotic Defense-Related Pathways

“…Systems biology approaches are being increasingly used to monitor the global dynamics of entire biological circuits rather than analyzing the role of a few genes (Long et al, 2008). Genes encoding proteins that participate in the same pathway or that are part of the same protein complex are often coregulated.…”

“…One example is the SeedNet, a genome-wide network model describing transcriptional interactions, representing a binary phase transition study, in nongerminating (or dormant) and germinating Arabidopsis thaliana seeds (Bassel et al, 2011). Integration of the expression data at the resolution of developmental stages and in different environmental contexts is expected to generate a high confidence dynamic gene regulatory model (Long et al, 2008). Developing seeds represent an excellent model to test this hypothesis.…”

Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis

“…Cellular activities and biological functions are executed through complex physical and regulatory interactions of genes that resemble a network (Barabási and Oltvai, 2004;Long et al, 2008;Urano et al, 2010). Transcriptome profiling technologies, including microarray and high-throughput sequencing platforms, have made it possible to infer functional associations based on the concordant expression patterns of genes to direct subsequent biological experiments (Bansal et al, 2007;MorenoRisueno et al, 2010;Less et al, 2011;Friedel et al, 2012).…”

Systems Biology of Organ Initiation at the Shoot Apex

“…This problem does not only concern plant research, and it would be far beyond the scope of this article to review the entire literature concerning regulatory networks in biology. We will, therefore, only give a brief outline of the methods currently used in meristem research and provide a number of relevant examples (for further reading, see de Jong, 2002;Albert, 2007;Long et al, 2008;and refs. therein).…”