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Mechanistic models of microbial community metabolism
Microbial communities affect many facets of human health and well-being. Naturally occurring bacteria, whether in nature or the human body, rarely exist in isolation. A deeper understanding of the metabolic functions of these communities is now possible with emerging computational models. In this re...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202304/ https://www.ncbi.nlm.nih.gov/pubmed/34125127 http://dx.doi.org/10.1039/d0mo00154f |
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author | Dillard, Lillian R. Payne, Dawson D. Papin, Jason A. |
author_facet | Dillard, Lillian R. Payne, Dawson D. Papin, Jason A. |
author_sort | Dillard, Lillian R. |
collection | PubMed |
description | Microbial communities affect many facets of human health and well-being. Naturally occurring bacteria, whether in nature or the human body, rarely exist in isolation. A deeper understanding of the metabolic functions of these communities is now possible with emerging computational models. In this review, we summarize frameworks for constructing mechanistic models of microbial community metabolism and discuss available algorithms for model analysis. We highlight essential decision points that greatly influence algorithm selection, as well as model analysis. Polymicrobial metabolic models can be utilized to gain insights into host-pathogen interactions, bacterial engineering, and many more translational applications. |
format | Online Article Text |
id | pubmed-8202304 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Royal Society of Chemistry |
record_format | MEDLINE/PubMed |
spelling | pubmed-82023042021-06-29 Mechanistic models of microbial community metabolism Dillard, Lillian R. Payne, Dawson D. Papin, Jason A. Mol Omics Chemistry Microbial communities affect many facets of human health and well-being. Naturally occurring bacteria, whether in nature or the human body, rarely exist in isolation. A deeper understanding of the metabolic functions of these communities is now possible with emerging computational models. In this review, we summarize frameworks for constructing mechanistic models of microbial community metabolism and discuss available algorithms for model analysis. We highlight essential decision points that greatly influence algorithm selection, as well as model analysis. Polymicrobial metabolic models can be utilized to gain insights into host-pathogen interactions, bacterial engineering, and many more translational applications. The Royal Society of Chemistry 2021-03-23 /pmc/articles/PMC8202304/ /pubmed/34125127 http://dx.doi.org/10.1039/d0mo00154f Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by/3.0/ |
spellingShingle | Chemistry Dillard, Lillian R. Payne, Dawson D. Papin, Jason A. Mechanistic models of microbial community metabolism |
title | Mechanistic models of microbial community metabolism |
title_full | Mechanistic models of microbial community metabolism |
title_fullStr | Mechanistic models of microbial community metabolism |
title_full_unstemmed | Mechanistic models of microbial community metabolism |
title_short | Mechanistic models of microbial community metabolism |
title_sort | mechanistic models of microbial community metabolism |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202304/ https://www.ncbi.nlm.nih.gov/pubmed/34125127 http://dx.doi.org/10.1039/d0mo00154f |
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