Enhancing Metabolic Models with Genome-Scale Experimental Data

Research output: Chapter in Book/Report/Conference proceedingBook chapter – Annual report year: 2018Researchpeer-review

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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
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

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

ID: 152740541