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Systems biology for crop improvement

期刊

PLANT GENOME
卷 14, 期 2, 页码 -

出版社

WILEY
DOI: 10.1002/tpg2.20098

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资金

  1. Science AMP
  2. Engineering Research Board (SERB) of Department of ScienceAMP
  3. Technology (DST), Government of India [SB/S9/Z-13/2019, SB/WEA-01/2017]
  4. Bill andMelinda Gates Foundation [OPP1130244]
  5. Bill and Melinda Gates Foundation [OPP1130244] Funding Source: Bill and Melinda Gates Foundation

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The generation of large-scale data from various omics levels in plant species has become routine, but the independent study of datasets in different crop species has hindered a comprehensive understanding of complex traits. A systems biology approach with integration of multiple omics data and modeling is crucial for understanding the flow of biological information underlying complex traits and biological networks. This approach allows a holistic understanding of dynamic systems and interactions with the external environment in phenotypic expression.
In recent years, generation of large-scale data fromgenome, transcriptome, proteome, metabolome, epigenome, and others, has become routine in several plant species. Most of these datasets in different crop species, however, were studied independently and as a result, full insight could not be gained on the molecular basis of complex traits and biological networks. A systems biology approach involving integration of multiple omics data, modeling, and prediction of the cellular functions is required to understand the flow of biological information that underlies complex traits. In this context, systems biology with multiomics data integration is crucial and allows a holistic understanding of the dynamic system with the different levels of biological organization interactingwith external environment for a phenotypic expression. Here, we present recent progress made in the area of various omics studies-integrative and systems biology approaches with a special focus on application to crop improvement. We have also discussed the challenges and opportunities in multiomics data integration, modeling, and understanding of the biology of complex traits underpinning yield and stress tolerance in major cereals and legumes.

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