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The number of active metabolic pathways is bounded by the number of cellular constraints at maximal metabolic rates

Growth rate is a near-universal selective pressure across microbial species. High growth rates require hundreds of metabolic enzymes, each with different nonlinear kinetics, to be precisely tuned within the bounds set by physicochemical constraints. Yet, the metabolic behaviour of many species is ch...

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Autores principales: de Groot, Daan H., van Boxtel, Coco, Planqué, Robert, Bruggeman, Frank J., Teusink, Bas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6428345/
https://www.ncbi.nlm.nih.gov/pubmed/30856167
http://dx.doi.org/10.1371/journal.pcbi.1006858
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author de Groot, Daan H.
van Boxtel, Coco
Planqué, Robert
Bruggeman, Frank J.
Teusink, Bas
author_facet de Groot, Daan H.
van Boxtel, Coco
Planqué, Robert
Bruggeman, Frank J.
Teusink, Bas
author_sort de Groot, Daan H.
collection PubMed
description Growth rate is a near-universal selective pressure across microbial species. High growth rates require hundreds of metabolic enzymes, each with different nonlinear kinetics, to be precisely tuned within the bounds set by physicochemical constraints. Yet, the metabolic behaviour of many species is characterized by simple relations between growth rate, enzyme expression levels and metabolic rates. We asked if this simplicity could be the outcome of optimisation by evolution. Indeed, when the growth rate is maximized—in a static environment under mass-conservation and enzyme expression constraints—we prove mathematically that the resulting optimal metabolic flux distribution is described by a limited number of subnetworks, known as Elementary Flux Modes (EFMs). We show that, because EFMs are the minimal subnetworks leading to growth, a small active number automatically leads to the simple relations that are measured. We find that the maximal number of flux-carrying EFMs is determined only by the number of imposed constraints on enzyme expression, not by the size, kinetics or topology of the network. This minimal-EFM extremum principle is illustrated in a graphical framework, which explains qualitative changes in microbial behaviours, such as overflow metabolism and co-consumption, and provides a method for identification of the enzyme expression constraints that limit growth under the prevalent conditions. The extremum principle applies to all microorganisms that are selected for maximal growth rates under protein concentration constraints, for example the solvent capacities of cytosol, membrane or periplasmic space.
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spelling pubmed-64283452019-04-01 The number of active metabolic pathways is bounded by the number of cellular constraints at maximal metabolic rates de Groot, Daan H. van Boxtel, Coco Planqué, Robert Bruggeman, Frank J. Teusink, Bas PLoS Comput Biol Research Article Growth rate is a near-universal selective pressure across microbial species. High growth rates require hundreds of metabolic enzymes, each with different nonlinear kinetics, to be precisely tuned within the bounds set by physicochemical constraints. Yet, the metabolic behaviour of many species is characterized by simple relations between growth rate, enzyme expression levels and metabolic rates. We asked if this simplicity could be the outcome of optimisation by evolution. Indeed, when the growth rate is maximized—in a static environment under mass-conservation and enzyme expression constraints—we prove mathematically that the resulting optimal metabolic flux distribution is described by a limited number of subnetworks, known as Elementary Flux Modes (EFMs). We show that, because EFMs are the minimal subnetworks leading to growth, a small active number automatically leads to the simple relations that are measured. We find that the maximal number of flux-carrying EFMs is determined only by the number of imposed constraints on enzyme expression, not by the size, kinetics or topology of the network. This minimal-EFM extremum principle is illustrated in a graphical framework, which explains qualitative changes in microbial behaviours, such as overflow metabolism and co-consumption, and provides a method for identification of the enzyme expression constraints that limit growth under the prevalent conditions. The extremum principle applies to all microorganisms that are selected for maximal growth rates under protein concentration constraints, for example the solvent capacities of cytosol, membrane or periplasmic space. Public Library of Science 2019-03-11 /pmc/articles/PMC6428345/ /pubmed/30856167 http://dx.doi.org/10.1371/journal.pcbi.1006858 Text en © 2019 de Groot et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
de Groot, Daan H.
van Boxtel, Coco
Planqué, Robert
Bruggeman, Frank J.
Teusink, Bas
The number of active metabolic pathways is bounded by the number of cellular constraints at maximal metabolic rates
title The number of active metabolic pathways is bounded by the number of cellular constraints at maximal metabolic rates
title_full The number of active metabolic pathways is bounded by the number of cellular constraints at maximal metabolic rates
title_fullStr The number of active metabolic pathways is bounded by the number of cellular constraints at maximal metabolic rates
title_full_unstemmed The number of active metabolic pathways is bounded by the number of cellular constraints at maximal metabolic rates
title_short The number of active metabolic pathways is bounded by the number of cellular constraints at maximal metabolic rates
title_sort number of active metabolic pathways is bounded by the number of cellular constraints at maximal metabolic rates
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6428345/
https://www.ncbi.nlm.nih.gov/pubmed/30856167
http://dx.doi.org/10.1371/journal.pcbi.1006858
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