Enhancing Metabolic Models with Genome-Scale Experimental Data

Kristian Jensen, Steinn Gudmundsson, Markus J. Herrgård*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Citations (Scopus)


Genome-scale metabolic reconstructions have found widespread use in scientific research as structured representations of knowledge about an organism’s metabolism and as starting points for metabolic simulations. With few simplifying assumptions, genome-scale models of metabolism can be used to estimate intracellular reaction rates in any organism for which a well-curated metabolic reconstruction is available. However, with the rapid increase in the availability of genome-scale data, there is ample opportunity to refine the predictions made by metabolic models by integrating experimental data. In this chapter, we review different methods for combining genome-scale metabolic models with genome-scale experimental data, such as transcriptomics, proteomics, and metabolomics. Integrating experimental data into the models generally results in more precise and accurate simulations of cellular metabolism.

Original languageEnglish
Title of host publicationRNA Technologies
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages14
Publication statusPublished - 2018

Publication series

NameRNA Technologies
ISSN (Print)2197-9731
ISSN (Electronic)2197-9758

Bibliographical note

Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.

Other keywords

  • Constraint-based metabolic modeling
  • Flux balance analysis
  • Genome-scale data
  • Genome-scale modeling
  • Machine learning
  • Metabolomics
  • Proteomics
  • Shadow prices
  • Transcriptomics


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