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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Microbiology
2019
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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. |
format | Online Article Text |
id | pubmed-6584880 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | American Society for Microbiology |
record_format | MEDLINE/PubMed |
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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