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Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints [electronic resource].

Published:
Washington, D.C. : United States. Dept. of Energy. Office of Science, 2017.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
Physical Description:
Article numbers 935 : digital, PDF file
Additional Creators:
Massachusetts Institute of Technology, United States. Department of Energy. Office of Science, and United States. Department of Energy. Office of Scientific and Technical Information
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Summary:
Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We applied GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model-based design in metabolic engineering.
Report Numbers:
E 1.99:1423877
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Note:
Published through SciTech Connect.
03/08/2017.
Molecular Systems Biology 13 8 ISSN 1744-4292 AM
Benjamín J. Sánchez; Cheng Zhang; Avlant Nilsson; Petri‐Jaan Lahtvee; Eduard J. Kerkhoven; Jens Nielsen.
Funding Information:
sc0008744
View MARC record | catkey: 23498873