ArticleNPJ systems biology and applications2016
Dynamic models of the complex microbial metapopulation of lake mendota.
Article in NPJ systems biology and applications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Resilience of a stochastic generalized Lotka-Volterra model for microbiome studies.Mathematical biosciences and engineering : MBE · 2025Article
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- Ecological dynamics imposes fundamental challenges in community-based microbial source tracking.iMeta · 2023Article
- What's next for computational systems biology?Frontiers in systems biology · 2023Review
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- ARZIMM: A Novel Analytic Platform for the Inference of Microbial Interactions and Community Stability from Longitudinal Microbiome Study.Frontiers in genetics · 2022Article
- Inference of dynamic interaction networks: A comparison between Lotka-Volterra and multivariate autoregressive models.Frontiers in bioinformatics · 2022Article
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- Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes.Annual review of biomedical engineering · 2021Review
- IMPARO: inferring microbial interactions through parameter optimisation.BMC molecular and cell biology · 2020Article
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- A Primer for Microbiome Time-Series Analysis.Frontiers in genetics · 2020Article
- A theoretical framework for controlling complex microbial communities.Nature communications · 2019Article
- Understanding and Engineering Distributed Biochemical Pathways in Microbial Communities.Biochemistry · 2019Review
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- Web-gLV: A Web Based Platform for Lotka-Volterra Based Modeling and Simulation of Microbial Populations.Frontiers in microbiology · 2019Article
- From hairballs to hypotheses-biological insights from microbial networks.FEMS microbiology reviews · 2018Review
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Like many other environments, Lake Mendota, WI, USA, is populated by many thousand microbial species. Only about 1,000 of these constitute between 80 and 99% of the total microbial community, depending on the season, whereas the remaining species are rare. The functioning and resilience of the lake ecosystem depend on these microorganisms, and it is therefore important to understand their dynamics throughout the year. We propose a two-layered set of dynamic mathematical models that capture and interpret the yearly abundance patterns of the species within the metapopulation. The first layer analyzes the interactions between 14 subcommunities (SCs) that peak at different times of the year and together contain all species whereas the second layer focuses on interactions between individual species and SCs. Each SC contains species from numerous families, genera, and phyla in strikingly different abundances. The dynamic models quantify the importance of environmental factors in shaping the dynamics of the lake's metapopulation and reveal positive or negative interactions between species and SCs. Three environmental factors, namely temperature, ammonia/phosphorus, and nitrate+nitrite, positively affect almost all SCs, whereas by far the most interactions between SCs are inhibitory. As far as the interactions can be independently validated, they are supported by literature information. The models are quite robust and permit predictions of species abundances over many years both, under the assumption that conditions do not change drastically, or in response to environmental perturbations.
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