ArticlePLoS computational biology2020
Compositional Lotka-Volterra describes microbial dynamics in the simplex.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
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- Format-Preserving Reduction of Canonical Nonlinear Models.Bulletin of mathematical biology · 2026Article
- Bayesian Pairwise Compositional Lotka-Volterra Modeling Infers Potential Rhizosphere Microbial Suppressors of Ralstonia pseudosolanacearum.The plant pathology journal · 2026Article
- Computational and multi-omics systems biology for precision microbiome therapeutics.Frontiers in microbiomes · 2026Review
- MCSPACE: inferring microbiome spatiotemporal dynamics from high-throughput co-localization data.Microbiome · 2025Article
- Quantifying microbial interactions based on compositional data using an iterative approach for solving generalized Lotka-Volterra equations.PLoS computational biology · 2025Article
- Unravelling three-way interactions betweenJournal of medical microbiology · 2025Review
- Learning ecosystem-scale dynamics from microbiome data with MDSINE2.Nature microbiology · 2025Article
- Directing microbial co-culture composition using cybernetic control.Cell reports methods · 2025Article
- Control of medical digital twins with artificial neural networks.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- An in silico framework for the rational design of vaginal probiotic therapy.PLoS computational biology · 2025Article
- Synthetic Ecosystems: From the Test Tube to the Biosphere.ACS synthetic biology · 2024Review
- Stochastic models allow improved inference of microbiome interactions from time series data.PLoS biology · 2024Article
- Kinetics-based inference of environment-dependent microbial interactions and their dynamic variation.mSystems · 2024Article
- Sparse species interactions reproduce abundance correlation patterns in microbial communities.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Dynamic social interactions and keystone species shape the diversity and stability of mixed-species biofilms - an example from dairy isolates.ISME communications · 2023Article
- A topic modeling approach reveals the dynamic T cell composition of peripheral blood during cancer immunotherapy.Cell reports methods · 2023Article
- Article
- Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome.Scientific reports · 2023Article
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5 authors.
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Abstract
Dynamic changes in microbial communities play an important role in human health and disease. Specifically, deciphering how microbial species in a community interact with each other and their environment can elucidate mechanisms of disease, a problem typically investigated using tools from community ecology. Yet, such methods require measurements of absolute densities, whereas typical datasets only provide estimates of relative abundances. Here, we systematically investigate models of microbial dynamics in the simplex of relative abundances. We derive a new nonlinear dynamical system for microbial dynamics, termed "compositional" Lotka-Volterra (cLV), unifying approaches using generalized Lotka-Volterra (gLV) equations from community ecology and compositional data analysis. On three real datasets, we demonstrate that cLV recapitulates interactions between relative abundances implied by gLV. Moreover, we show that cLV is as accurate as gLV in forecasting microbial trajectories in terms of relative abundances. We further compare cLV to two other models of relative abundance dynamics motivated by common assumptions in the literature-a linear model in a log-ratio transformed space, and a linear model in the space of relative abundances-and provide evidence that cLV more accurately describes community trajectories over time. Finally, we investigate when information about direct effects can be recovered from relative data that naively provide information about only indirect effects. Our results suggest that strong effects may be recoverable from relative data, but more subtle effects are challenging to identify.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.