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PLoS Comput Biol. 2018 Feb 16;14(2):e1006010. doi: 10.1371/journal.pcbi.1006010. eCollection 2018 Feb.

Metabolic enzyme cost explains variable trade-offs between microbial growth rate and yield.

Author information

1
Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Oslo, Norway.
2
Systems Bioinformatics Section, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Vrije Universiteit, Amsterdam, The Netherlands.
3
Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule, Zürich, Switzerland.
4
Computer Sciences Department and Wisconsin Institute for Discovery, University of Wisconsin, Madison, Wisconsin, United States of America.
5
INRA, UR1404, MaIAGE, Université Paris-Saclay, Jouy-en-Josas, France.
6
Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany.

Abstract

Microbes may maximize the number of daughter cells per time or per amount of nutrients consumed. These two strategies correspond, respectively, to the use of enzyme-efficient or substrate-efficient metabolic pathways. In reality, fast growth is often associated with wasteful, yield-inefficient metabolism, and a general thermodynamic trade-off between growth rate and biomass yield has been proposed to explain this. We studied growth rate/yield trade-offs by using a novel modeling framework, Enzyme-Flux Cost Minimization (EFCM) and by assuming that the growth rate depends directly on the enzyme investment per rate of biomass production. In a comprehensive mathematical model of core metabolism in E. coli, we screened all elementary flux modes leading to cell synthesis, characterized them by the growth rates and yields they provide, and studied the shape of the resulting rate/yield Pareto front. By varying the model parameters, we found that the rate/yield trade-off is not universal, but depends on metabolic kinetics and environmental conditions. A prominent trade-off emerges under oxygen-limited growth, where yield-inefficient pathways support a 2-to-3 times higher growth rate than yield-efficient pathways. EFCM can be widely used to predict optimal metabolic states and growth rates under varying nutrient levels, perturbations of enzyme parameters, and single or multiple gene knockouts.

PMID:
29451895
PMCID:
PMC5847312
DOI:
10.1371/journal.pcbi.1006010
[Indexed for MEDLINE]
Free PMC Article

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