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The Power of Metabolism for Predicting Microbial Community Dynamics

Quantitative understanding and prediction of microbial community dynamics are an outstanding challenge. We test the hypothesis that metabolic mechanisms provide a foundation for accurate prediction of dynamics in microbial systems. In our research, metabolic models have been able to accurately predi...

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Detalles Bibliográficos
Autores principales: Chacón, Jeremy M., Harcombe, William R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Microbiology 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6584880/
https://www.ncbi.nlm.nih.gov/pubmed/31186310
http://dx.doi.org/10.1128/mSystems.00146-19
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author Chacón, Jeremy M.
Harcombe, William R.
author_facet Chacón, Jeremy M.
Harcombe, William R.
author_sort Chacón, Jeremy M.
collection PubMed
description Quantitative understanding and prediction of microbial community dynamics are an outstanding challenge. We test the hypothesis that metabolic mechanisms provide a foundation for accurate prediction of dynamics in microbial systems. In our research, metabolic models have been able to accurately predict species interactions, evolutionary trajectories, and response to perturbation in simple synthetic consortia. However, metabolic models have many constraints and often serve best as null models to identify additional processes at play. We anticipate that major advances in metabolic systems biology will involve scaling bottom-up approaches to complex communities and expanding the processes that are incorporated in a metabolic perspective. Ultimately, cellular metabolism will inform predictive ecology that enables precision management of microbial systems.
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spelling pubmed-65848802019-07-03 The Power of Metabolism for Predicting Microbial Community Dynamics Chacón, Jeremy M. Harcombe, William R. mSystems Perspective Quantitative understanding and prediction of microbial community dynamics are an outstanding challenge. We test the hypothesis that metabolic mechanisms provide a foundation for accurate prediction of dynamics in microbial systems. In our research, metabolic models have been able to accurately predict species interactions, evolutionary trajectories, and response to perturbation in simple synthetic consortia. However, metabolic models have many constraints and often serve best as null models to identify additional processes at play. We anticipate that major advances in metabolic systems biology will involve scaling bottom-up approaches to complex communities and expanding the processes that are incorporated in a metabolic perspective. Ultimately, cellular metabolism will inform predictive ecology that enables precision management of microbial systems. American Society for Microbiology 2019-06-11 /pmc/articles/PMC6584880/ /pubmed/31186310 http://dx.doi.org/10.1128/mSystems.00146-19 Text en Copyright © 2019 Chacón and Harcombe. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Perspective
Chacón, Jeremy M.
Harcombe, William R.
The Power of Metabolism for Predicting Microbial Community Dynamics
title The Power of Metabolism for Predicting Microbial Community Dynamics
title_full The Power of Metabolism for Predicting Microbial Community Dynamics
title_fullStr The Power of Metabolism for Predicting Microbial Community Dynamics
title_full_unstemmed The Power of Metabolism for Predicting Microbial Community Dynamics
title_short The Power of Metabolism for Predicting Microbial Community Dynamics
title_sort power of metabolism for predicting microbial community dynamics
topic Perspective
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6584880/
https://www.ncbi.nlm.nih.gov/pubmed/31186310
http://dx.doi.org/10.1128/mSystems.00146-19
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