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Thermodynamic modelling of synthetic communities predicts minimum free energy requirements for sulfate reduction and methanogenesis

Microbial communities are complex dynamical systems harbouring many species interacting together to implement higher-level functions. Among these higher-level functions, conversion of organic matter into simpler building blocks by microbial communities underpins biogeochemical cycles and animal and...

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Autores principales: Delattre, Hadrien, Chen, Jing, Wade, Matthew J., Soyer, Orkun S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7276542/
https://www.ncbi.nlm.nih.gov/pubmed/32370691
http://dx.doi.org/10.1098/rsif.2020.0053
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author Delattre, Hadrien
Chen, Jing
Wade, Matthew J.
Soyer, Orkun S.
author_facet Delattre, Hadrien
Chen, Jing
Wade, Matthew J.
Soyer, Orkun S.
author_sort Delattre, Hadrien
collection PubMed
description Microbial communities are complex dynamical systems harbouring many species interacting together to implement higher-level functions. Among these higher-level functions, conversion of organic matter into simpler building blocks by microbial communities underpins biogeochemical cycles and animal and plant nutrition, and is exploited in biotechnology. A prerequisite to predicting the dynamics and stability of community-mediated metabolic conversions is the development and calibration of appropriate mathematical models. Here, we present a generic, extendable thermodynamic model for community dynamics and calibrate a key parameter of this thermodynamic model, the minimum energy requirement associated with growth-supporting metabolic pathways, using experimental population dynamics data from synthetic communities composed of a sulfate reducer and two methanogens. Our findings show that accounting for thermodynamics is necessary in capturing the experimental population dynamics of these synthetic communities that feature relevant species using low energy growth pathways. Furthermore, they provide the first estimates for minimum energy requirements of methanogenesis (in the range of −30 kJ mol(−1)) and elaborate on previous estimates of lactate fermentation by sulfate reducers (in the range of −30 to −17 kJ mol(−1) depending on the culture conditions). The open-source nature of the developed model and demonstration of its use for estimating a key thermodynamic parameter should facilitate further thermodynamic modelling of microbial communities.
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spelling pubmed-72765422020-06-10 Thermodynamic modelling of synthetic communities predicts minimum free energy requirements for sulfate reduction and methanogenesis Delattre, Hadrien Chen, Jing Wade, Matthew J. Soyer, Orkun S. J R Soc Interface Life Sciences–Mathematics interface Microbial communities are complex dynamical systems harbouring many species interacting together to implement higher-level functions. Among these higher-level functions, conversion of organic matter into simpler building blocks by microbial communities underpins biogeochemical cycles and animal and plant nutrition, and is exploited in biotechnology. A prerequisite to predicting the dynamics and stability of community-mediated metabolic conversions is the development and calibration of appropriate mathematical models. Here, we present a generic, extendable thermodynamic model for community dynamics and calibrate a key parameter of this thermodynamic model, the minimum energy requirement associated with growth-supporting metabolic pathways, using experimental population dynamics data from synthetic communities composed of a sulfate reducer and two methanogens. Our findings show that accounting for thermodynamics is necessary in capturing the experimental population dynamics of these synthetic communities that feature relevant species using low energy growth pathways. Furthermore, they provide the first estimates for minimum energy requirements of methanogenesis (in the range of −30 kJ mol(−1)) and elaborate on previous estimates of lactate fermentation by sulfate reducers (in the range of −30 to −17 kJ mol(−1) depending on the culture conditions). The open-source nature of the developed model and demonstration of its use for estimating a key thermodynamic parameter should facilitate further thermodynamic modelling of microbial communities. The Royal Society 2020-05 2020-05-06 /pmc/articles/PMC7276542/ /pubmed/32370691 http://dx.doi.org/10.1098/rsif.2020.0053 Text en © 2020 The Authors. http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/http://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Life Sciences–Mathematics interface
Delattre, Hadrien
Chen, Jing
Wade, Matthew J.
Soyer, Orkun S.
Thermodynamic modelling of synthetic communities predicts minimum free energy requirements for sulfate reduction and methanogenesis
title Thermodynamic modelling of synthetic communities predicts minimum free energy requirements for sulfate reduction and methanogenesis
title_full Thermodynamic modelling of synthetic communities predicts minimum free energy requirements for sulfate reduction and methanogenesis
title_fullStr Thermodynamic modelling of synthetic communities predicts minimum free energy requirements for sulfate reduction and methanogenesis
title_full_unstemmed Thermodynamic modelling of synthetic communities predicts minimum free energy requirements for sulfate reduction and methanogenesis
title_short Thermodynamic modelling of synthetic communities predicts minimum free energy requirements for sulfate reduction and methanogenesis
title_sort thermodynamic modelling of synthetic communities predicts minimum free energy requirements for sulfate reduction and methanogenesis
topic Life Sciences–Mathematics interface
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7276542/
https://www.ncbi.nlm.nih.gov/pubmed/32370691
http://dx.doi.org/10.1098/rsif.2020.0053
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