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A low-complexity metabolic network model for the respiratory and fermentative metabolism of Escherichia coli

Over the last decades, predictive microbiology has made significant advances in the mathematical description of microbial spoiler and pathogen dynamics in or on food products. Recently, the focus of predictive microbiology has shifted from a (semi-)empirical population-level approach towards mechani...

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Autores principales: Tack, Ignace L. M. M., Nimmegeers, Philippe, Akkermans, Simen, Logist, Filip, Van Impe, Jan F. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6114798/
https://www.ncbi.nlm.nih.gov/pubmed/30157229
http://dx.doi.org/10.1371/journal.pone.0202565
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author Tack, Ignace L. M. M.
Nimmegeers, Philippe
Akkermans, Simen
Logist, Filip
Van Impe, Jan F. M.
author_facet Tack, Ignace L. M. M.
Nimmegeers, Philippe
Akkermans, Simen
Logist, Filip
Van Impe, Jan F. M.
author_sort Tack, Ignace L. M. M.
collection PubMed
description Over the last decades, predictive microbiology has made significant advances in the mathematical description of microbial spoiler and pathogen dynamics in or on food products. Recently, the focus of predictive microbiology has shifted from a (semi-)empirical population-level approach towards mechanistic models including information about the intracellular metabolism in order to increase model accuracy and genericness. However, incorporation of this subpopulation-level information increases model complexity and, consequently, the required run time to simulate microbial cell and population dynamics. In this paper, results of metabolic flux balance analyses (FBA) with a genome-scale model are used to calibrate a low-complexity linear model describing the microbial growth and metabolite secretion rates of Escherichia coli as a function of the nutrient and oxygen uptake rate. Hence, the required information about the cellular metabolism (i.e., biomass growth and secretion of cell products) is selected and included in the linear model without incorporating the complete intracellular reaction network. However, the applied FBAs are only representative for microbial dynamics under specific extracellular conditions, viz., a neutral medium without weak acids at a temperature of 37℃. Deviations from these reference conditions lead to metabolic shifts and adjustments of the cellular nutrient uptake or maintenance requirements. This metabolic dependency on extracellular conditions has been taken into account in our low-complex metabolic model. In this way, a novel approach is developed to take the synergistic effects of temperature, pH, and undissociated acids on the cell metabolism into account. Consequently, the developed model is deployable as a tool to describe, predict and control E. coli dynamics in and on food products under various combinations of environmental conditions. To emphasize this point,three specific scenarios are elaborated: (i) aerobic respiration without production of weak acid extracellular metabolites, (ii) anaerobic fermentation with secretion of mixed acid fermentation products into the food environment, and (iii) respiro-fermentative metabolic regimes in between the behaviors at aerobic and anaerobic conditions.
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spelling pubmed-61147982018-09-17 A low-complexity metabolic network model for the respiratory and fermentative metabolism of Escherichia coli Tack, Ignace L. M. M. Nimmegeers, Philippe Akkermans, Simen Logist, Filip Van Impe, Jan F. M. PLoS One Research Article Over the last decades, predictive microbiology has made significant advances in the mathematical description of microbial spoiler and pathogen dynamics in or on food products. Recently, the focus of predictive microbiology has shifted from a (semi-)empirical population-level approach towards mechanistic models including information about the intracellular metabolism in order to increase model accuracy and genericness. However, incorporation of this subpopulation-level information increases model complexity and, consequently, the required run time to simulate microbial cell and population dynamics. In this paper, results of metabolic flux balance analyses (FBA) with a genome-scale model are used to calibrate a low-complexity linear model describing the microbial growth and metabolite secretion rates of Escherichia coli as a function of the nutrient and oxygen uptake rate. Hence, the required information about the cellular metabolism (i.e., biomass growth and secretion of cell products) is selected and included in the linear model without incorporating the complete intracellular reaction network. However, the applied FBAs are only representative for microbial dynamics under specific extracellular conditions, viz., a neutral medium without weak acids at a temperature of 37℃. Deviations from these reference conditions lead to metabolic shifts and adjustments of the cellular nutrient uptake or maintenance requirements. This metabolic dependency on extracellular conditions has been taken into account in our low-complex metabolic model. In this way, a novel approach is developed to take the synergistic effects of temperature, pH, and undissociated acids on the cell metabolism into account. Consequently, the developed model is deployable as a tool to describe, predict and control E. coli dynamics in and on food products under various combinations of environmental conditions. To emphasize this point,three specific scenarios are elaborated: (i) aerobic respiration without production of weak acid extracellular metabolites, (ii) anaerobic fermentation with secretion of mixed acid fermentation products into the food environment, and (iii) respiro-fermentative metabolic regimes in between the behaviors at aerobic and anaerobic conditions. Public Library of Science 2018-08-29 /pmc/articles/PMC6114798/ /pubmed/30157229 http://dx.doi.org/10.1371/journal.pone.0202565 Text en © 2018 Tack 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
Tack, Ignace L. M. M.
Nimmegeers, Philippe
Akkermans, Simen
Logist, Filip
Van Impe, Jan F. M.
A low-complexity metabolic network model for the respiratory and fermentative metabolism of Escherichia coli
title A low-complexity metabolic network model for the respiratory and fermentative metabolism of Escherichia coli
title_full A low-complexity metabolic network model for the respiratory and fermentative metabolism of Escherichia coli
title_fullStr A low-complexity metabolic network model for the respiratory and fermentative metabolism of Escherichia coli
title_full_unstemmed A low-complexity metabolic network model for the respiratory and fermentative metabolism of Escherichia coli
title_short A low-complexity metabolic network model for the respiratory and fermentative metabolism of Escherichia coli
title_sort low-complexity metabolic network model for the respiratory and fermentative metabolism of escherichia coli
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6114798/
https://www.ncbi.nlm.nih.gov/pubmed/30157229
http://dx.doi.org/10.1371/journal.pone.0202565
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