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Methods Mol Biol. 2019;1978:243-258. doi: 10.1007/978-1-4939-9236-2_15.

Insights into Dynamic Network States Using Metabolomic Data.

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Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, Tübingen, Germany.
Department for Computer Science, University of Tübingen, Tübingen, Germany.
German Center for Infection Research (DZIF), Tübingen, Germany.
University of California, Los Angeles, Los Angeles, CA, USA.


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.


Dynamic network states; Metabolomics; Systems biology

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