Interplay between constraints, objectives, and optimality for genome-scale stoichiometric models.

High-throughput data generation and genome-scale stoichiometric models have greatly facilitated the comprehensive study of metabolic networks. The computation of all feasible metabolic routes with these models, given stoichiometric, thermodynamic, and steady-state constraints, provides important ins...

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Main Authors: Timo R Maarleveld, Meike T Wortel, Brett G Olivier, Bas Teusink, Frank J Bruggeman
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2015-04-01
Series:PLoS Computational Biology
Online Access:https://doi.org/10.1371/journal.pcbi.1004166
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spelling doaj-55d768059d3b4c1097b03e175ec8a6b32021-04-21T15:34:33ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582015-04-01114e100416610.1371/journal.pcbi.1004166Interplay between constraints, objectives, and optimality for genome-scale stoichiometric models.Timo R MaarleveldMeike T WortelBrett G OlivierBas TeusinkFrank J BruggemanHigh-throughput data generation and genome-scale stoichiometric models have greatly facilitated the comprehensive study of metabolic networks. The computation of all feasible metabolic routes with these models, given stoichiometric, thermodynamic, and steady-state constraints, provides important insights into the metabolic capacities of a cell. How the feasible metabolic routes emerge from the interplay between flux constraints, optimality objectives, and the entire metabolic network of a cell is, however, only partially understood. We show how optimal metabolic routes, resulting from flux balance analysis computations, arise out of elementary flux modes, constraints, and optimization objectives. We illustrate our findings with a genome-scale stoichiometric model of Escherichia coli metabolism. In the case of one flux constraint, all feasible optimal flux routes can be derived from elementary flux modes alone. We found up to 120 million of such optimal elementary flux modes. We introduce a new computational method to compute the corner points of the optimal solution space fast and efficiently. Optimal flux routes no longer depend exclusively on elementary flux modes when we impose additional constraints; new optimal metabolic routes arise out of combinations of elementary flux modes. The solution space of feasible metabolic routes shrinks enormously when additional objectives---e.g. those related to pathway expression costs or pathway length---are introduced. In many cases, only a single metabolic route remains that is both feasible and optimal. This paper contributes to reaching a complete topological understanding of the metabolic capacity of organisms in terms of metabolic flux routes, one that is most natural to biochemists and biotechnologists studying and engineering metabolism.https://doi.org/10.1371/journal.pcbi.1004166
collection DOAJ
language English
format Article
sources DOAJ
author Timo R Maarleveld
Meike T Wortel
Brett G Olivier
Bas Teusink
Frank J Bruggeman
spellingShingle Timo R Maarleveld
Meike T Wortel
Brett G Olivier
Bas Teusink
Frank J Bruggeman
Interplay between constraints, objectives, and optimality for genome-scale stoichiometric models.
PLoS Computational Biology
author_facet Timo R Maarleveld
Meike T Wortel
Brett G Olivier
Bas Teusink
Frank J Bruggeman
author_sort Timo R Maarleveld
title Interplay between constraints, objectives, and optimality for genome-scale stoichiometric models.
title_short Interplay between constraints, objectives, and optimality for genome-scale stoichiometric models.
title_full Interplay between constraints, objectives, and optimality for genome-scale stoichiometric models.
title_fullStr Interplay between constraints, objectives, and optimality for genome-scale stoichiometric models.
title_full_unstemmed Interplay between constraints, objectives, and optimality for genome-scale stoichiometric models.
title_sort interplay between constraints, objectives, and optimality for genome-scale stoichiometric models.
publisher Public Library of Science (PLoS)
series PLoS Computational Biology
issn 1553-734X
1553-7358
publishDate 2015-04-01
description High-throughput data generation and genome-scale stoichiometric models have greatly facilitated the comprehensive study of metabolic networks. The computation of all feasible metabolic routes with these models, given stoichiometric, thermodynamic, and steady-state constraints, provides important insights into the metabolic capacities of a cell. How the feasible metabolic routes emerge from the interplay between flux constraints, optimality objectives, and the entire metabolic network of a cell is, however, only partially understood. We show how optimal metabolic routes, resulting from flux balance analysis computations, arise out of elementary flux modes, constraints, and optimization objectives. We illustrate our findings with a genome-scale stoichiometric model of Escherichia coli metabolism. In the case of one flux constraint, all feasible optimal flux routes can be derived from elementary flux modes alone. We found up to 120 million of such optimal elementary flux modes. We introduce a new computational method to compute the corner points of the optimal solution space fast and efficiently. Optimal flux routes no longer depend exclusively on elementary flux modes when we impose additional constraints; new optimal metabolic routes arise out of combinations of elementary flux modes. The solution space of feasible metabolic routes shrinks enormously when additional objectives---e.g. those related to pathway expression costs or pathway length---are introduced. In many cases, only a single metabolic route remains that is both feasible and optimal. This paper contributes to reaching a complete topological understanding of the metabolic capacity of organisms in terms of metabolic flux routes, one that is most natural to biochemists and biotechnologists studying and engineering metabolism.
url https://doi.org/10.1371/journal.pcbi.1004166
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