Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints

Sánchez, Benjamín J.; Zhang, Cheng; Nilsson, Avlant; Lahtvee, Petri‐Jaan; Kerkhoven, Eduard J.; Nielsen, Jens

doi:10.15252/msb.20167411

Title: Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints

Journal Article · Fri Aug 04 00:00:00 EDT 2017 · Molecular Systems Biology

DOI:https://doi.org/10.15252/msb.20167411· OSTI ID:1546851

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Department of Biology and Biological Engineering Chalmers University of Technology Gothenburg Sweden, Novo Nordisk Foundation Center for Biosustainability Chalmers University of Technology Gothenburg Sweden
Science for Life Laboratory KTH – Royal Institute of Technology Stockholm Sweden, State Key Laboratory of Bioreactor Engineering East China University of Science and Technology Shanghai China
Department of Biology and Biological Engineering Chalmers University of Technology Gothenburg Sweden
Department of Biology and Biological Engineering Chalmers University of Technology Gothenburg Sweden, Novo Nordisk Foundation Center for Biosustainability Chalmers University of Technology Gothenburg Sweden, Novo Nordisk Foundation Center for Biosustainability Technical University of Denmark Hørsholm Denmark

Abstract Genome‐scale metabolic models ( GEM s) 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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Research Organization:: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: SC0008744

OSTI ID:: 1546851

Alternate ID(s):: OSTI ID: 1423877; OSTI ID: 1546856

Journal Information:: Molecular Systems Biology, Journal Name: Molecular Systems Biology Vol. 13 Journal Issue: 8; ISSN 1744-4292

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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Cited by: 228 works

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59 BASIC BIOLOGICAL SCIENCES
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Title: Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints

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