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Enhancing Metabolic Models with Genome-Scale Experimental Data

  • Kristian Jensen
  • Steinn Gudmundsson
  • Markus J. Herrgård
Chapter
Part of the RNA Technologies book series (RNATECHN)

Abstract

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.

Keywords

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

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Kristian Jensen
    • 1
  • Steinn Gudmundsson
    • 2
  • Markus J. Herrgård
    • 1
  1. 1.The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of DenmarkKongens LyngbyDenmark
  2. 2.Center for Systems BiologyUniversity of IcelandReykjavikIceland

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