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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...

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Autores principales: Descheemaeker, Lana, de Buyl, Sophie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2020
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.
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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
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AT debuylsophie stochasticlogisticmodelsreproduceexperimentaltimeseriesofmicrobialcommunities