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

Abstract

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 ofmore » model-based design in metabolic engineering.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [3]
  1. Chalmers Univ. of Technology, Gothenburg (Sweden)
  2. KTH Royal Inst. of Technology, Stockholm (Sweden); East China Univ. of Science and Technology, Shanghai (China)
  3. Chalmers Univ. of Technology, Gothenburg (Sweden); Technical Univ. of Denmark, Lyngby (Denmark)
Publication Date:
Research Org.:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1423877
Grant/Contract Number:  
sc0008744
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Molecular Systems Biology
Additional Journal Information:
Journal Volume: 13; Journal Issue: 8; Journal ID: ISSN 1744-4292
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Enzyme Kinetics; Flux Balance Analysis; Molecular Crowding; Proteomics; Saccharomyces Cerevisiae

Citation Formats

Sánchez, Benjamín J., Zhang, Cheng, Nilsson, Avlant, Lahtvee, Petri‐Jaan, Kerkhoven, Eduard J., and Nielsen, Jens. Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. United States: N. p., 2017. Web. doi:10.15252/msb.20167411.
Sánchez, Benjamín J., Zhang, Cheng, Nilsson, Avlant, Lahtvee, Petri‐Jaan, Kerkhoven, Eduard J., & Nielsen, Jens. Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. United States. doi:10.15252/msb.20167411.
Sánchez, Benjamín J., Zhang, Cheng, Nilsson, Avlant, Lahtvee, Petri‐Jaan, Kerkhoven, Eduard J., and Nielsen, Jens. Wed . "Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints". United States. doi:10.15252/msb.20167411. https://www.osti.gov/servlets/purl/1423877.
@article{osti_1423877,
title = {Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints},
author = {Sánchez, Benjamín J. and Zhang, Cheng and Nilsson, Avlant and Lahtvee, Petri‐Jaan and Kerkhoven, Eduard J. and Nielsen, Jens},
abstractNote = {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.},
doi = {10.15252/msb.20167411},
journal = {Molecular Systems Biology},
number = 8,
volume = 13,
place = {United States},
year = {Wed Mar 08 00:00:00 EST 2017},
month = {Wed Mar 08 00:00:00 EST 2017}
}

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