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Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics
Predicting the dynamics and functions of microbiomes constructed from the bottom-up is a key challenge in exploiting them to our benefit. Current models based on ecological theory fail to capture complex community behaviors due to higher order interactions, do not scale well with increasing complexi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9225007/ https://www.ncbi.nlm.nih.gov/pubmed/35736613 http://dx.doi.org/10.7554/eLife.73870 |
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author | Baranwal, Mayank Clark, Ryan L Thompson, Jaron Sun, Zeyu Hero, Alfred O Venturelli, Ophelia S |
author_facet | Baranwal, Mayank Clark, Ryan L Thompson, Jaron Sun, Zeyu Hero, Alfred O Venturelli, Ophelia S |
author_sort | Baranwal, Mayank |
collection | PubMed |
description | Predicting the dynamics and functions of microbiomes constructed from the bottom-up is a key challenge in exploiting them to our benefit. Current models based on ecological theory fail to capture complex community behaviors due to higher order interactions, do not scale well with increasing complexity and in considering multiple functions. We develop and apply a long short-term memory (LSTM) framework to advance our understanding of community assembly and health-relevant metabolite production using a synthetic human gut community. A mainstay of recurrent neural networks, the LSTM learns a high dimensional data-driven non-linear dynamical system model. We show that the LSTM model can outperform the widely used generalized Lotka-Volterra model based on ecological theory. We build methods to decipher microbe-microbe and microbe-metabolite interactions from an otherwise black-box model. These methods highlight that Actinobacteria, Firmicutes and Proteobacteria are significant drivers of metabolite production whereas Bacteroides shape community dynamics. We use the LSTM model to navigate a large multidimensional functional landscape to design communities with unique health-relevant metabolite profiles and temporal behaviors. In sum, the accuracy of the LSTM model can be exploited for experimental planning and to guide the design of synthetic microbiomes with target dynamic functions. |
format | Online Article Text |
id | pubmed-9225007 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-92250072022-06-24 Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics Baranwal, Mayank Clark, Ryan L Thompson, Jaron Sun, Zeyu Hero, Alfred O Venturelli, Ophelia S eLife Computational and Systems Biology Predicting the dynamics and functions of microbiomes constructed from the bottom-up is a key challenge in exploiting them to our benefit. Current models based on ecological theory fail to capture complex community behaviors due to higher order interactions, do not scale well with increasing complexity and in considering multiple functions. We develop and apply a long short-term memory (LSTM) framework to advance our understanding of community assembly and health-relevant metabolite production using a synthetic human gut community. A mainstay of recurrent neural networks, the LSTM learns a high dimensional data-driven non-linear dynamical system model. We show that the LSTM model can outperform the widely used generalized Lotka-Volterra model based on ecological theory. We build methods to decipher microbe-microbe and microbe-metabolite interactions from an otherwise black-box model. These methods highlight that Actinobacteria, Firmicutes and Proteobacteria are significant drivers of metabolite production whereas Bacteroides shape community dynamics. We use the LSTM model to navigate a large multidimensional functional landscape to design communities with unique health-relevant metabolite profiles and temporal behaviors. In sum, the accuracy of the LSTM model can be exploited for experimental planning and to guide the design of synthetic microbiomes with target dynamic functions. eLife Sciences Publications, Ltd 2022-06-23 /pmc/articles/PMC9225007/ /pubmed/35736613 http://dx.doi.org/10.7554/eLife.73870 Text en © 2022, Baranwal, Clark et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Computational and Systems Biology Baranwal, Mayank Clark, Ryan L Thompson, Jaron Sun, Zeyu Hero, Alfred O Venturelli, Ophelia S Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics |
title | Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics |
title_full | Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics |
title_fullStr | Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics |
title_full_unstemmed | Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics |
title_short | Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics |
title_sort | recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics |
topic | Computational and Systems Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9225007/ https://www.ncbi.nlm.nih.gov/pubmed/35736613 http://dx.doi.org/10.7554/eLife.73870 |
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