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A study of a diauxic growth experiment using an expanded dynamic flux balance framework

Flux balance analysis (FBA) remains one of the most used methods for modeling the entirety of cellular metabolism, and a range of applications and extensions based on the FBA framework have been generated. Dynamic flux balance analysis (dFBA), the expansion of FBA into the time domain, still has iss...

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Autores principales: Karlsen, Emil, Gylseth, Marianne, Schulz, Christian, Almaas, Eivind
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9821518/
https://www.ncbi.nlm.nih.gov/pubmed/36607958
http://dx.doi.org/10.1371/journal.pone.0280077
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author Karlsen, Emil
Gylseth, Marianne
Schulz, Christian
Almaas, Eivind
author_facet Karlsen, Emil
Gylseth, Marianne
Schulz, Christian
Almaas, Eivind
author_sort Karlsen, Emil
collection PubMed
description Flux balance analysis (FBA) remains one of the most used methods for modeling the entirety of cellular metabolism, and a range of applications and extensions based on the FBA framework have been generated. Dynamic flux balance analysis (dFBA), the expansion of FBA into the time domain, still has issues regarding accessibility limiting its widespread adoption and application, such as a lack of a consistently rigid formalism and tools that can be applied without expert knowledge. Recent work has combined dFBA with enzyme-constrained flux balance analysis (decFBA), which has been shown to greatly improve accuracy in the comparison of computational simulations and experimental data, but such approaches generally do not take into account the fact that altering the enzyme composition of a cell is not an instantaneous process. Here, we have developed a decFBA method that explicitly takes enzyme change constraints (ecc) into account, decFBAecc. The resulting software is a simple yet flexible framework for using genome-scale metabolic modeling for simulations in the time domain that has full interoperability with the COBRA Toolbox 3.0. To assess the quality of the computational predictions of decFBAecc, we conducted a diauxic growth fermentation experiment with Escherichia coli BW25113 in glucose minimal M9 medium. The comparison of experimental data with dFBA, decFBA and decFBAecc predictions demonstrates how systematic analyses within a fixed constraint-based framework can aid the study of model parameters. Finally, in explaining experimentally observed phenotypes, our computational analysis demonstrates the importance of non-linear dependence of exchange fluxes on medium metabolite concentrations and the non-instantaneous change in enzyme composition, effects of which have not previously been accounted for in constraint-based analysis.
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spelling pubmed-98215182023-01-07 A study of a diauxic growth experiment using an expanded dynamic flux balance framework Karlsen, Emil Gylseth, Marianne Schulz, Christian Almaas, Eivind PLoS One Research Article Flux balance analysis (FBA) remains one of the most used methods for modeling the entirety of cellular metabolism, and a range of applications and extensions based on the FBA framework have been generated. Dynamic flux balance analysis (dFBA), the expansion of FBA into the time domain, still has issues regarding accessibility limiting its widespread adoption and application, such as a lack of a consistently rigid formalism and tools that can be applied without expert knowledge. Recent work has combined dFBA with enzyme-constrained flux balance analysis (decFBA), which has been shown to greatly improve accuracy in the comparison of computational simulations and experimental data, but such approaches generally do not take into account the fact that altering the enzyme composition of a cell is not an instantaneous process. Here, we have developed a decFBA method that explicitly takes enzyme change constraints (ecc) into account, decFBAecc. The resulting software is a simple yet flexible framework for using genome-scale metabolic modeling for simulations in the time domain that has full interoperability with the COBRA Toolbox 3.0. To assess the quality of the computational predictions of decFBAecc, we conducted a diauxic growth fermentation experiment with Escherichia coli BW25113 in glucose minimal M9 medium. The comparison of experimental data with dFBA, decFBA and decFBAecc predictions demonstrates how systematic analyses within a fixed constraint-based framework can aid the study of model parameters. Finally, in explaining experimentally observed phenotypes, our computational analysis demonstrates the importance of non-linear dependence of exchange fluxes on medium metabolite concentrations and the non-instantaneous change in enzyme composition, effects of which have not previously been accounted for in constraint-based analysis. Public Library of Science 2023-01-06 /pmc/articles/PMC9821518/ /pubmed/36607958 http://dx.doi.org/10.1371/journal.pone.0280077 Text en © 2023 Karlsen et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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
Karlsen, Emil
Gylseth, Marianne
Schulz, Christian
Almaas, Eivind
A study of a diauxic growth experiment using an expanded dynamic flux balance framework
title A study of a diauxic growth experiment using an expanded dynamic flux balance framework
title_full A study of a diauxic growth experiment using an expanded dynamic flux balance framework
title_fullStr A study of a diauxic growth experiment using an expanded dynamic flux balance framework
title_full_unstemmed A study of a diauxic growth experiment using an expanded dynamic flux balance framework
title_short A study of a diauxic growth experiment using an expanded dynamic flux balance framework
title_sort study of a diauxic growth experiment using an expanded dynamic flux balance framework
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9821518/
https://www.ncbi.nlm.nih.gov/pubmed/36607958
http://dx.doi.org/10.1371/journal.pone.0280077
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