The Power of Metabolism for Predicting Microbial Community Dynamics
ABSTRACT 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 accurat...
Guardado en:
Autores principales: | , |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
American Society for Microbiology
2019
|
Materias: | |
Acceso en línea: | https://doaj.org/article/9abd17139dd247dca0b7e624057123ac |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
id |
oai:doaj.org-article:9abd17139dd247dca0b7e624057123ac |
---|---|
record_format |
dspace |
spelling |
oai:doaj.org-article:9abd17139dd247dca0b7e624057123ac2021-12-02T18:44:39ZThe Power of Metabolism for Predicting Microbial Community Dynamics10.1128/mSystems.00146-192379-5077https://doaj.org/article/9abd17139dd247dca0b7e624057123ac2019-06-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00146-19https://doaj.org/toc/2379-5077ABSTRACT 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.Jeremy M. ChacónWilliam R. HarcombeAmerican Society for Microbiologyarticlemetabolismantibioticsbacteriophageecologyevolutiongenome-scale modelingMicrobiologyQR1-502ENmSystems, Vol 4, Iss 3 (2019) |
institution |
DOAJ |
collection |
DOAJ |
language |
EN |
topic |
metabolism antibiotics bacteriophage ecology evolution genome-scale modeling Microbiology QR1-502 |
spellingShingle |
metabolism antibiotics bacteriophage ecology evolution genome-scale modeling Microbiology QR1-502 Jeremy M. Chacón William R. Harcombe The Power of Metabolism for Predicting Microbial Community Dynamics |
description |
ABSTRACT 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 |
article |
author |
Jeremy M. Chacón William R. Harcombe |
author_facet |
Jeremy M. Chacón William R. Harcombe |
author_sort |
Jeremy M. Chacón |
title |
The Power of Metabolism for Predicting Microbial Community Dynamics |
title_short |
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_sort |
power of metabolism for predicting microbial community dynamics |
publisher |
American Society for Microbiology |
publishDate |
2019 |
url |
https://doaj.org/article/9abd17139dd247dca0b7e624057123ac |
work_keys_str_mv |
AT jeremymchacon thepowerofmetabolismforpredictingmicrobialcommunitydynamics AT williamrharcombe thepowerofmetabolismforpredictingmicrobialcommunitydynamics AT jeremymchacon powerofmetabolismforpredictingmicrobialcommunitydynamics AT williamrharcombe powerofmetabolismforpredictingmicrobialcommunitydynamics |
_version_ |
1718377694527225856 |