Book chapter
Enhancing Metabolic Models with Genome-Scale Experimental Data
iLoop, Translational Management, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark1
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark2
Department of Biotechnology and Biomedicine, Technical University of Denmark3
University of Iceland4
Research Groups, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark5
Global Econometric Modeling, Research Groups, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark6
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.
Language: | English |
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Publisher: | Springer |
Year: | 2018 |
Edition: | 2018 |
Pages: | 337-350 |
Series: | Rna Technologies |
ISBN: | 3319929666 , 3319929674 , 9783319929668 and 9783319929675 |
ISSN: | 21979758 |
Types: | Book chapter |
DOI: | 10.1007/978-3-319-92967-5_17 |
ORCIDs: | Jensen, Kristian and Herrgård, Markus |