A systems biology approach toward understanding seed composition in soybean. (December 2015) - Li, Ling; Hur, Manhoi; Lee, Joon-Yong; Zhou, Wenxu; Song, Zhihong; Ransom, Nick; Demirkale, Cumhur; Nettleton, Dan; Westgate, Mark; Arendsee, Zebulun; Iyer, Vidya; Shanks, Jackie; et al.
Abstract Background The molecular, biochemical, and genetic mechanisms that regulate the complex metabolic network of soybean seed development determine the ultimate balance of protein, lipid, and carbohydrate stored in the mature seed. Many of the genes and metabolites that participate in seed metabolism are unknown or poorly defined; even more remains to be understood about the regulation of their metabolic networks. A global omics analysis can provide insights into the regulation of seed metabolism, even without a priori assumptions about the structure of these networks. Results With the future goal of predictive biology in mind, we have combined metabolomics, transcriptomics, and metabolic flux technologies to reveal the global developmental and metabolic networks that determine the structure and composition of the mature soybean seed. We have coupled this global approach with interactive bioinformatics and statistical analyses to gain insights into the biochemical programs that determine soybean seed composition. For this purpose, we usedP lant/Eukaryotic and MicrobialM etabolomics SystemsR esource (PMR, http://www.metnetdb.org/pmr, a platform that incorporates metabolomics data to develop hypotheses concerning the organization and regulation of metabolic networks, and MetNet systems biology toolshttp://www.metnetdb.org for plant omics data, a framework to enable interactive visualization of metabolic and regulatory networks. Conclusions This combination of high-throughput experimental data and bioinformatics analyses has revealed sets of specific genes, genetic perturbations and mechanisms, and metabolic changes that are associated with the developmental variation in soybean seed composition. Researchers can explore these metabolomics and transcriptomics data interactively at PMR.