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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.

Author information

1
Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, Tübingen, Germany.
2
Department for Computer Science, University of Tübingen, Tübingen, Germany.
3
German Center for Infection Research (DZIF), Tübingen, Germany.
4
University of California, Los Angeles, Los Angeles, CA, USA. njamshidi@mednet.ucla.edu.

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.

KEYWORDS:

Dynamic network states; Metabolomics; Systems biology

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