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• W2945759715 abstract "Metabolomic data is the youngest of the high-throughput data types; however, it is potentially one of the most informative, as it provides a direct, quantitative biochemical phenotype. There are a number of ways in which metabolomic data can be analyzed in systems biology; however, the thermodynamic and kinetic relevance of these data cannot be overstated. Genome-scale metabolic network reconstructions provide a natural context to incorporate metabolomic data in order to provide insight into the condition-specific kinetic characteristics of metabolic networks. Herein we discuss how metabolomic data can be incorporated into constraint-based models in a flexible framework that enables scaling from small pathways to cell-scale models, while being able to accommodate coarse-grained to more detailed, allosteric interactions, all using the well-known principle of mass action." @default.
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• W2945759715 date "2019-01-01" @default.
• W2945759715 modified "2023-10-14" @default.
• W2945759715 title "Insights into Dynamic Network States Using Metabolomic Data" @default.
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• W2945759715 doi "https://doi.org/10.1007/978-1-4939-9236-2_15" @default.
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