Insights into Dynamic Network States Using Metabolomic Data

  • Reihaneh Mostolizadeh
  • Andreas Dräger
  • Neema JamshidiEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1978)


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.


Systems biology Dynamic network states Metabolomics 


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Reihaneh Mostolizadeh
    • 1
    • 3
    • 4
  • Andreas Dräger
    • 1
    • 3
    • 4
  • Neema Jamshidi
    • 2
    Email author
  1. 1.Center for Bioinformatics Tübingen (ZBIT)University of TübingenTübingenGermany
  2. 2.University of California, Los AngelesLos AngelesUSA
  3. 3.Department for Computer ScienceUniversity of TübingenTübingenGermany
  4. 4.German Center for Infection Research (DZIF)TübingenGermany

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