Journal Article

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

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

2017

Overview

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. Synopsis The GECKO method takes into account enzyme abundances and kinetics to enhance genome‐scale models of metabolism (GEMs). An implementation for Saccharomyces cerevisiae gives insight into metabolism and enzyme usage. GECKO is a method that enhances a GEM with enzyme constraints, using both kinetic and omics data. The enzyme‐constrained ecYeast7 model of S. cerevisiae outperforms previous models in simulation capabilities and allows exploring enzyme usage. Directly integrating quantitative proteomic data in ecYeast7 significantly reduces the inherent flux variability of model simulations. Physiological behavior such as maximum specific growth rate, overflow metabolism and gene deletion response can be explained by a limited enzyme pool in cell. Graphical Abstract The GECKO method takes into account enzyme abundances and kinetics to enhance genome‐scale models of metabolism (GEMs). An implementation for Saccharomyces cerevisiae gives insight into metabolism and enzyme usage.

• Publisher: Nature Publishing Group UK,EMBO Press,Wiley,John Wiley and Sons Inc,Springer Nature
• Subject: BASIC BIOLOGICAL SCIENCES / Carbon / Carbon sources / Clonal deletion / Constraints / EMBO17 / EMBO21 / EMBO22 / Enzyme Kinetics / Enzymes / escherichia-coli / evolutionary / expression / fermentative capacity / Flux Balance Analysis / Geckos / Gene deletion / Genome, Fungal / Genomes / Growth rate / integration / Kinetics / Metabolic Engineering / Metabolic flux / Metabolic Networks and Pathways / Metabolic pathways / Metabolism / Metabolites / Models, Biological / Molecular Crowding / Phenotype / protein / Proteomics / quantification / quantitative prediction / Saccharomyces Cerevisiae / Saccharomyces cerevisiae - enzymology / Saccharomyces cerevisiae - growth & development / Systems Biology - methods / Yeast