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An evolutionary algorithm for designing microbial communities via environmental modification
Despite a growing understanding of how environmental composition affects microbial communities, it remains difficult to apply this knowledge to the rational design of synthetic multispecies consortia. This is because natural microbial communities can harbour thousands of different organisms and envi...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8220269/ https://www.ncbi.nlm.nih.gov/pubmed/34157894 http://dx.doi.org/10.1098/rsif.2021.0348 |
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author | Pacheco, Alan R. Segrè, Daniel |
author_facet | Pacheco, Alan R. Segrè, Daniel |
author_sort | Pacheco, Alan R. |
collection | PubMed |
description | Despite a growing understanding of how environmental composition affects microbial communities, it remains difficult to apply this knowledge to the rational design of synthetic multispecies consortia. This is because natural microbial communities can harbour thousands of different organisms and environmental substrates, making up a vast combinatorial space that precludes exhaustive experimental testing and computational prediction. Here, we present a method based on the combination of machine learning and metabolic modelling that selects optimal environmental compositions to produce target community phenotypes. In this framework, dynamic flux balance analysis is used to model the growth of a community in candidate environments. A genetic algorithm is then used to evaluate the behaviour of the community relative to a target phenotype, and subsequently adjust the environment to allow the organisms to approach this target. We apply this iterative process to thousands of in silico communities of varying sizes, showing how it can rapidly identify environments that yield desired taxonomic compositions and patterns of metabolic exchange. Moreover, this combination of approaches produces testable predictions for the assembly of experimental microbial communities with specific properties and can facilitate rational environmental design processes for complex microbiomes. |
format | Online Article Text |
id | pubmed-8220269 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-82202692021-06-23 An evolutionary algorithm for designing microbial communities via environmental modification Pacheco, Alan R. Segrè, Daniel J R Soc Interface Life Sciences–Mathematics interface Despite a growing understanding of how environmental composition affects microbial communities, it remains difficult to apply this knowledge to the rational design of synthetic multispecies consortia. This is because natural microbial communities can harbour thousands of different organisms and environmental substrates, making up a vast combinatorial space that precludes exhaustive experimental testing and computational prediction. Here, we present a method based on the combination of machine learning and metabolic modelling that selects optimal environmental compositions to produce target community phenotypes. In this framework, dynamic flux balance analysis is used to model the growth of a community in candidate environments. A genetic algorithm is then used to evaluate the behaviour of the community relative to a target phenotype, and subsequently adjust the environment to allow the organisms to approach this target. We apply this iterative process to thousands of in silico communities of varying sizes, showing how it can rapidly identify environments that yield desired taxonomic compositions and patterns of metabolic exchange. Moreover, this combination of approaches produces testable predictions for the assembly of experimental microbial communities with specific properties and can facilitate rational environmental design processes for complex microbiomes. The Royal Society 2021-06-23 /pmc/articles/PMC8220269/ /pubmed/34157894 http://dx.doi.org/10.1098/rsif.2021.0348 Text en © 2021 The Authors. https://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/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Life Sciences–Mathematics interface Pacheco, Alan R. Segrè, Daniel An evolutionary algorithm for designing microbial communities via environmental modification |
title | An evolutionary algorithm for designing microbial communities via environmental modification |
title_full | An evolutionary algorithm for designing microbial communities via environmental modification |
title_fullStr | An evolutionary algorithm for designing microbial communities via environmental modification |
title_full_unstemmed | An evolutionary algorithm for designing microbial communities via environmental modification |
title_short | An evolutionary algorithm for designing microbial communities via environmental modification |
title_sort | evolutionary algorithm for designing microbial communities via environmental modification |
topic | Life Sciences–Mathematics interface |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8220269/ https://www.ncbi.nlm.nih.gov/pubmed/34157894 http://dx.doi.org/10.1098/rsif.2021.0348 |
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