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- W2038111222 abstract "Incomplete knowledge of biochemical pathways makes the holistic description of plant metabolism a non-trivial undertaking. Sensitive analytical platforms, which are capable of accurately quantifying the levels of the various molecular entities of the cell, can assist in tackling this task. However, the ever-increasing amount of high-throughput data, often from multiple technologies, requires significant computational efforts for integrative analysis. Here we introduce the application of network analysis to study plant metabolism and describe the construction and analysis of correlation-based networks from (time-resolved) metabolomics data. By investigating the interactions between metabolites, network analysis can help to interpret complex datasets through the identification of key network components. The relationship between structural and biological roles of network components can be evaluated and employed to aid metabolic engineering." @default.
- W2038111222 created "2016-06-24" @default.
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- W2038111222 creator A5059658943 @default.
- W2038111222 creator A5082852942 @default.
- W2038111222 date "2013-01-01" @default.
- W2038111222 modified "2023-09-29" @default.
- W2038111222 title "Network analysis: tackling complex data to study plant metabolism" @default.
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- W2038111222 doi "https://doi.org/10.1016/j.tibtech.2012.10.011" @default.
- W2038111222 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/23245943" @default.
- W2038111222 hasPublicationYear "2013" @default.
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