Enhancing Metabolic Models with Genome-Scale Experimental Data

Kristian Jensen, Steinn Gudmundsson, Markus Herrgård

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

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.
Original languageEnglish
Title of host publicationSystems Biology
EditorsNikolaus Rajewsky, Stefan Jurga, Jan Barciszewski
PublisherSpringer
Publication date2018
Edition2018
Pages337-350
ISBN (Print)978-3-319-92966-8
ISBN (Electronic)978-3-319-92967-5
DOIs
Publication statusPublished - 2018
SeriesRNA Technologies
ISSN2197-9731

Keywords

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

Cite this

Jensen, K., Gudmundsson, S., & Herrgård, M. (2018). Enhancing Metabolic Models with Genome-Scale Experimental Data. In N. Rajewsky, S. Jurga, & J. Barciszewski (Eds.), Systems Biology (2018 ed., pp. 337-350). Springer. RNA Technologies https://doi.org/10.1007/978-3-319-92967-5_17
Jensen, Kristian ; Gudmundsson, Steinn ; Herrgård, Markus. / Enhancing Metabolic Models with Genome-Scale Experimental Data. Systems Biology. editor / Nikolaus Rajewsky ; Stefan Jurga ; Jan Barciszewski. 2018. ed. Springer, 2018. pp. 337-350 (RNA Technologies).
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Jensen, K, Gudmundsson, S & Herrgård, M 2018, Enhancing Metabolic Models with Genome-Scale Experimental Data. in N Rajewsky, S Jurga & J Barciszewski (eds), Systems Biology. 2018 edn, Springer, RNA Technologies, pp. 337-350. https://doi.org/10.1007/978-3-319-92967-5_17

Enhancing Metabolic Models with Genome-Scale Experimental Data. / Jensen, Kristian; Gudmundsson, Steinn; Herrgård, Markus.

Systems Biology. ed. / Nikolaus Rajewsky; Stefan Jurga; Jan Barciszewski. 2018. ed. Springer, 2018. p. 337-350 (RNA Technologies).

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

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N2 - 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.

AB - 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.

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Jensen K, Gudmundsson S, Herrgård M. Enhancing Metabolic Models with Genome-Scale Experimental Data. In Rajewsky N, Jurga S, Barciszewski J, editors, Systems Biology. 2018 ed. Springer. 2018. p. 337-350. (RNA Technologies). https://doi.org/10.1007/978-3-319-92967-5_17