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Stochastic logistic models reproduce experimental time series of microbial communities
We analyze properties of experimental microbial time series, from plankton and the human microbiome, and investigate whether stochastic generalized Lotka-Volterra models could reproduce those properties. We show that this is the case when the noise term is large and a linear function of the species...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7410486/ https://www.ncbi.nlm.nih.gov/pubmed/32687052 http://dx.doi.org/10.7554/eLife.55650 |
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author | Descheemaeker, Lana de Buyl, Sophie |
author_facet | Descheemaeker, Lana de Buyl, Sophie |
author_sort | Descheemaeker, Lana |
collection | PubMed |
description | We analyze properties of experimental microbial time series, from plankton and the human microbiome, and investigate whether stochastic generalized Lotka-Volterra models could reproduce those properties. We show that this is the case when the noise term is large and a linear function of the species abundance, while the strength of the self-interactions varies over multiple orders of magnitude. We stress the fact that all the observed stochastic properties can be obtained from a logistic model, that is, without interactions, even the niche character of the experimental time series. Linear noise is associated with growth rate stochasticity, which is related to changes in the environment. This suggests that fluctuations in the sparsely sampled experimental time series may be caused by extrinsic sources. |
format | Online Article Text |
id | pubmed-7410486 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-74104862020-08-10 Stochastic logistic models reproduce experimental time series of microbial communities Descheemaeker, Lana de Buyl, Sophie eLife Computational and Systems Biology We analyze properties of experimental microbial time series, from plankton and the human microbiome, and investigate whether stochastic generalized Lotka-Volterra models could reproduce those properties. We show that this is the case when the noise term is large and a linear function of the species abundance, while the strength of the self-interactions varies over multiple orders of magnitude. We stress the fact that all the observed stochastic properties can be obtained from a logistic model, that is, without interactions, even the niche character of the experimental time series. Linear noise is associated with growth rate stochasticity, which is related to changes in the environment. This suggests that fluctuations in the sparsely sampled experimental time series may be caused by extrinsic sources. eLife Sciences Publications, Ltd 2020-07-20 /pmc/articles/PMC7410486/ /pubmed/32687052 http://dx.doi.org/10.7554/eLife.55650 Text en © 2020, Descheemaeker and de Buyl http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://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 Descheemaeker, Lana de Buyl, Sophie Stochastic logistic models reproduce experimental time series of microbial communities |
title | Stochastic logistic models reproduce experimental time series of microbial communities |
title_full | Stochastic logistic models reproduce experimental time series of microbial communities |
title_fullStr | Stochastic logistic models reproduce experimental time series of microbial communities |
title_full_unstemmed | Stochastic logistic models reproduce experimental time series of microbial communities |
title_short | Stochastic logistic models reproduce experimental time series of microbial communities |
title_sort | stochastic logistic models reproduce experimental time series of microbial communities |
topic | Computational and Systems Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7410486/ https://www.ncbi.nlm.nih.gov/pubmed/32687052 http://dx.doi.org/10.7554/eLife.55650 |
work_keys_str_mv | AT descheemaekerlana stochasticlogisticmodelsreproduceexperimentaltimeseriesofmicrobialcommunities AT debuylsophie stochasticlogisticmodelsreproduceexperimentaltimeseriesofmicrobialcommunities |