ArticleRoyal Society open science2021
Structural identifiability of the generalized Lotka-Volterra model for microbiome studies.
Article in Royal Society open science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 53 citations in OpenAlex.
- Coexistence Theory for Microbial Ecology, and Vice Versa.Environmental microbiology · 2025Pooled it
- Dynamical Systems-Constrained Metabolic Modeling Enables Forecasting of Host-Microbiome Dynamics.bioRxiv : the preprint server for biology · 2026Article
- Physics-constrained neural ordinary differential equation models to discover and predict microbial community dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Emergent coexistence and the limits of reductionism in ecological communities.PLoS computational biology · 2026Article
- Design and application of synthetic human gut microbial communities.Gut microbes · 2025Review
- A temporal-aware machine learning framework enables microbial community dynamics prediction with personalized precision.Microbiome · 2025Article
- Identification of dynamic models of microbial communities: A workflow addressing identifiability and modeling pitfalls.PLoS computational biology · 2025Article
- Rigorous validation of ecological models against empirical time series.Nature ecology & evolution · 2025Article
- Quantifying microbial interactions based on compositional data using an iterative approach for solving generalized Lotka-Volterra equations.PLoS computational biology · 2025Article
- Scarce data, noisy inferences and overfitting: the hidden flaws in ecological dynamics modelling.Journal of the Royal Society, Interface · 2025Article
- Resilience of a stochastic generalized Lotka-Volterra model for microbiome studies.Mathematical biosciences and engineering : MBE · 2025Article
- MIMIC: a Python package for simulating, inferring, and predicting microbial community interactions and dynamics.Bioinformatics (Oxford, England) · 2025Article
- Mechanisms promoting biodiversity in ecosystems.Quantitative biology (Beijing, China) · 2025Review
- Optimal control of combination immunotherapy for a virtual murine cohort in a glioblastoma-immune dynamics model.Journal of theoretical biology · 2024Article
- Stochastic models allow improved inference of microbiome interactions from time series data.PLoS biology · 2024Article
- Optimal control of combination immunotherapy for a virtual murine cohort in a glioblastoma-immune dynamics model.bioRxiv : the preprint server for biology · 2024Article
- In vitro analysis of postbiotic antimicrobial activity against Candida Species in a minimal synthetic model simulating the gut mycobiota in obesity.Scientific reports · 2024Article
- Using ancient sedimentary DNA to forecast ecosystem trajectories under climate change.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Article
- Formation of a constructed microbial community in a nutrient-rich environment indicates bacterial interspecific competition.mSystems · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Population dynamic models can be used in conjunction with time series of species abundances to infer interactions. Understanding microbial interactions is a prerequisite for numerous goals in microbiome research, including predicting how populations change over time, determining how manipulations of microbiomes affect dynamics and designing synthetic microbiomes to perform tasks. As such, there is great interest in adapting population dynamic theory for microbial systems. Despite the appeal, numerous hurdles exist. One hurdle is that the data commonly obtained from DNA sequencing yield estimates of relative abundances, while population dynamic models such as the generalized Lotka-Volterra model track absolute abundances or densities. It is not clear whether relative abundance data alone can be used to infer parameters of population dynamic models such as the Lotka-Volterra model. We used structural identifiability analyses to determine the extent to which a time series of relative abundances can be used to parametrize the generalized Lotka-Volterra model. We found that only with absolute abundance data to accompany relative abundance estimates from sequencing can all parameters be uniquely identified. However, relative abundance data alone do contain information on relative interaction strengths, which is sufficient for many studies where the goal is to estimate key interactions and their effects on dynamics. Using synthetic data of a simple community for which we know the underlying structure, local practical identifiability analysis showed that modest amounts of both process and measurement error do not fundamentally affect these identifiability properties.
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Registered trials
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.