期刊
MOLECULAR SYSTEMS BIOLOGY
卷 13, 期 8, 页码 -出版社
WILEY
DOI: 10.15252/msb.20167411
关键词
enzyme kinetics; flux balance analysis; molecular crowding; proteomics; Saccharomyces cerevisiae
资金
- European Union's Horizon 2020 research and innovation program [686070, 720824]
- Novo Nordisk Foundation
- Knut and Alice Wallenberg Foundation
- US Department of Energy, Office of Science, Office of Biological and Environmental Research, Genomic Science program [DE-SC0008744]
- CONICYT [6222/2014]
- NNF Center for Biosustainability [Yeast Cell Factories] Funding Source: researchfish
- Novo Nordisk Fonden [NNF10CC1016517] Funding Source: researchfish
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.
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