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Integrative Approaches to Enhance Understanding of Plant Metabolic Pathway Structure and Regulation
Author(s) -
Takayuki Tohge,
Federico Scossa,
Alisdair R. Fernie
Publication year - 2015
Publication title -
plant physiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.554
H-Index - 312
eISSN - 1532-2548
pISSN - 0032-0889
DOI - 10.1104/pp.15.01006
Subject(s) - computational biology , pairwise comparison , biology , metabolic network , function (biology) , context (archaeology) , data integration , data science , metabolic pathway , computer science , data mining , gene , genetics , artificial intelligence , paleontology
Huge insight into molecular mechanisms and biological network coordination have been achieved following the application of various profiling technologies. Our knowledge of how the different molecular entities of the cell interact with one another suggests that, nevertheless, integration of data from different techniques could drive a more comprehensive understanding of the data emanating from different techniques. Here, we provide an overview of how such data integration is being used to aid the understanding of metabolic pathway structure and regulation. We choose to focus on the pairwise integration of large-scale metabolite data with that of the transcriptomic, proteomics, whole-genome sequence, growth- and yield-associated phenotypes, and archival functional genomic data sets. In doing so, we attempt to provide an update on approaches that integrate data obtained at different levels to reach a better understanding of either single gene function or metabolic pathway structure and regulation within the context of a broader biological process.

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