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A systems biology approach toward understanding seed composition in soybean
BMC Genomics
  • Ling Li, Iowa State University
  • Manhoi Hur, Iowa State University
  • Joon-Yong Lee, Iowa State University
  • Wenxu Zhou, Iowa State University
  • Zhihong Song, Iowa State University
  • Nick Ransom, Iowa State University
  • Cumhur Yusuf Demirkale, Iowa State University
  • Daniel S. Nettleton, Iowa State University
  • Mark E. Westgate, Iowa State University
  • Zebulun Wayne Arendsee, Iowa State University
  • Vidya Vaancheeswaran Iyer, Iowa State University
  • Jacqueline V. Shanks, Iowa State University
  • Basil Nikolau, Iowa State University
  • Eve Wurtele, Iowa State University
Document Type
Article
Publication Version
Published Version
Publication Date
1-1-2015
DOI
10.1186/1471-2164-16-S3-S9
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 used Plant/Eukaryotic and Microbial Metabolomics Systems Resource (PMR, http://www.metnetdb.org/pmr webcite, a platform that incorporates metabolomics data to develop hypotheses concerning the organization and regulation of metabolic networks, and MetNet systems biology tools http://www.metnetdb.org webcite 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.
Comments

This is an article from BMC Genomics 16 (2015): 1, doi:10.1186/1471-2164-16-S3-S9. Posted with permission.

Rights
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright Owner
Li et al
Language
en
File Format
application/pdf
Citation Information
Ling Li, Manhoi Hur, Joon-Yong Lee, Wenxu Zhou, et al.. "A systems biology approach toward understanding seed composition in soybean" BMC Genomics Vol. 16 Iss. Suppl 3 (2015) p. 1 - 18
Available at: http://works.bepress.com/basil-nikolau/7/