Systems Approaches to Identifying Gene Regulatory Networks in Plants

1Department of Biology, Duke University, Durham, North Carolina 27708, USA.
Annual Review of Cell and Developmental Biology (Impact Factor: 16.66). 08/2008; 24(1):81-103. DOI: 10.1146/annurev.cellbio.24.110707.175408
Source: PubMed


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.

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    • "This functional diversity makes it difficult to elucidate the individual influences of each family member of bZIP TFs within their various regulatory networks [56]. Bioinformatics in a high-throughput mode and molecular modelling approaches can be employed to identify the components of bZIP TFs regulatory networks [59]. Continued research to identify the biological roles of bZIP TFs and molecular mechanisms of their function is essential before effective GE of crop plants can be undertaken. "
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    • "ns , representing a binary phase transition study , in nongerminating ( or dormant ) and germinating Arabidopsis thaliana seeds ( Bassel et al . , 2011 ) . Integration of the ex - pression 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 ) ."
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