Open AccessImproving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints
Author(s) -
Sánchez Benjamín J,
Zhang Cheng,
Nilsson Avlant,
Lahtvee PetriJaan,
Kerkhoven Eduard J,
Nielsen Jens
Publication year - 2017
Publication title -
molecular systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.523
H-Index - 148
ISSN - 1744-4292
DOI - 10.15252/msb.20167411
Subject(s) - biology , flux (metallurgy) , saccharomyces cerevisiae , metabolic network , metabolic engineering , phenotype , metabolic flux analysis , metabolic pathway , yeast , systems biology , computational biology , flux balance analysis , enzyme , model organism , biological system , biochemistry , gene , metabolism , chemistry , organic chemistry
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.