文献类型：期刊文献

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

作者：Sanchez, Benjamin J.[1,2];Zhang, Cheng[3,4];Nilsson, Avlant[1];Lahtvee, Petri-Jaan[1,2];Kerkhoven, Eduard J.[1,2];Nielsen, Jens[1,2,5]

机构：[1]Chalmers Univ Technol, Dept Biol & Biol Engn, Gothenburg, Sweden;[2]Chalmers Univ Technol, Novo Nordisk Fdn, Ctr Biosustainabil, Gothenburg, Sweden;[3]KTH Royal Inst Technol, Sci Life Lab, Stockholm, Sweden;[4]East China Univ Sci & Technol, State Key Lab Bioreactor Engn, Shanghai, Peoples R China;[5]Tech Univ Denmark, Novo Nordisk Fdn, Ctr Biosustainabil, Horsholm, Denmark

年份：2017

卷号：13

期号：8

外文期刊名：MOLECULAR SYSTEMS BIOLOGY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000406943100001)】；

基金：The authors would like to thank Michael Gossing, Sunjae Lee, Johan Bjorkeroth, Amir Feizi, and Henning Redestig for valuable input. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreements No 686070 and 720824, the Novo Nordisk Foundation, the Knut and Alice Wallenberg Foundation, and the US Department of Energy, Office of Science, Office of Biological and Environmental Research, Genomic Science program (DE-SC0008744). B.J.S. gratefully acknowledges financial support from CONICYT (grant #6222/2014).

语种：英文

外文关键词：enzyme kinetics; flux balance analysis; molecular crowding; proteomics; Saccharomyces cerevisiae

摘要：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.