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MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses
Predicting dynamics of host-microbial ecosystems is crucial for the rational design of bacteriotherapies. We present MDSINE, a suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors. Using simulated data, we demonstrate that MDSI...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4893271/ https://www.ncbi.nlm.nih.gov/pubmed/27259475 http://dx.doi.org/10.1186/s13059-016-0980-6 |
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author | Bucci, Vanni Tzen, Belinda Li, Ning Simmons, Matt Tanoue, Takeshi Bogart, Elijah Deng, Luxue Yeliseyev, Vladimir Delaney, Mary L. Liu, Qing Olle, Bernat Stein, Richard R. Honda, Kenya Bry, Lynn Gerber, Georg K. |
author_facet | Bucci, Vanni Tzen, Belinda Li, Ning Simmons, Matt Tanoue, Takeshi Bogart, Elijah Deng, Luxue Yeliseyev, Vladimir Delaney, Mary L. Liu, Qing Olle, Bernat Stein, Richard R. Honda, Kenya Bry, Lynn Gerber, Georg K. |
author_sort | Bucci, Vanni |
collection | PubMed |
description | Predicting dynamics of host-microbial ecosystems is crucial for the rational design of bacteriotherapies. We present MDSINE, a suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors. Using simulated data, we demonstrate that MDSINE significantly outperforms the existing inference method. We then show MDSINE’s utility on two new gnotobiotic mice datasets, investigating infection with Clostridium difficile and an immune-modulatory probiotic. Using these datasets, we demonstrate new capabilities, including accurate forecasting of microbial dynamics, prediction of stable sub-communities that inhibit pathogen growth, and identification of bacteria most crucial to community integrity in response to perturbations. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-016-0980-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4893271 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-48932712016-06-05 MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses Bucci, Vanni Tzen, Belinda Li, Ning Simmons, Matt Tanoue, Takeshi Bogart, Elijah Deng, Luxue Yeliseyev, Vladimir Delaney, Mary L. Liu, Qing Olle, Bernat Stein, Richard R. Honda, Kenya Bry, Lynn Gerber, Georg K. Genome Biol Method Predicting dynamics of host-microbial ecosystems is crucial for the rational design of bacteriotherapies. We present MDSINE, a suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors. Using simulated data, we demonstrate that MDSINE significantly outperforms the existing inference method. We then show MDSINE’s utility on two new gnotobiotic mice datasets, investigating infection with Clostridium difficile and an immune-modulatory probiotic. Using these datasets, we demonstrate new capabilities, including accurate forecasting of microbial dynamics, prediction of stable sub-communities that inhibit pathogen growth, and identification of bacteria most crucial to community integrity in response to perturbations. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-016-0980-6) contains supplementary material, which is available to authorized users. BioMed Central 2016-06-03 /pmc/articles/PMC4893271/ /pubmed/27259475 http://dx.doi.org/10.1186/s13059-016-0980-6 Text en © Bucci et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Method Bucci, Vanni Tzen, Belinda Li, Ning Simmons, Matt Tanoue, Takeshi Bogart, Elijah Deng, Luxue Yeliseyev, Vladimir Delaney, Mary L. Liu, Qing Olle, Bernat Stein, Richard R. Honda, Kenya Bry, Lynn Gerber, Georg K. MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses |
title | MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses |
title_full | MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses |
title_fullStr | MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses |
title_full_unstemmed | MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses |
title_short | MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses |
title_sort | mdsine: microbial dynamical systems inference engine for microbiome time-series analyses |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4893271/ https://www.ncbi.nlm.nih.gov/pubmed/27259475 http://dx.doi.org/10.1186/s13059-016-0980-6 |
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