ArticleNature microbiology2025
Learning ecosystem-scale dynamics from microbiome data with MDSINE2.
Article in Nature microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- The cervicovaginal gut microbiota axis as a key determinant of systemic physiology.Gut microbes · 2026Review
- Review
- Foundation Models for Microbiome Research: From Sequence Semantics to Community Dynamics and Multimodal World Models.Advanced genetics (Hoboken, N.J.) · 2026Review
- Deciphering microbial community dynamics using cross-sectional data-informed NeuralODE.Microbiome · 2026Article
- Microbial and enzymatic synergy in fermented fish: impacts on flavor, texture, and safety with multi-omics and AI insights.Food chemistry: X · 2026Review
- Stochastic colonization and host-to-host transmission shape gut bacterial variability.bioRxiv : the preprint server for biology · 2026Article
- Dissimilarity measures for generalized Lotka-Volterra systems on networks.Scientific reports · 2026Article
- Dynamical Systems-Constrained Metabolic Modeling Enables Forecasting of Host-Microbiome Dynamics.bioRxiv : the preprint server for biology · 2026Article
- Phage diversity mirrors bacterial strain diversity in the honey bee gut microbiota.Nature communications · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Although dynamical systems models are a powerful tool for analysing microbial ecosystems, challenges in learning these models from complex microbiome datasets and interpreting their outputs limit use. We introduce the Microbial Dynamical Systems Inference Engine 2 (MDSINE2), a Bayesian method that learns compact and interpretable ecosystems-scale dynamical systems models from microbiome timeseries data. Microbial dynamics are modelled as stochastic processes driven by interaction modules, or groups of microbes with similar interaction structure and responses to perturbations, and additionally, noise characteristics of data are modelled. Our open-source software package provides multiple tools for interpreting learned models, including phylogeny/taxonomy of modules, and stability, interaction topology and keystoneness. To benchmark MDSINE2, we generated microbiome timeseries data from two murine cohorts that received faecal transplants from human donors and were then subjected to dietary and antibiotic perturbations. MDSINE2 outperforms state-of-the-art methods and identifies interaction modules that provide insights into ecosystems-scale interactions in the gut microbiome.
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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.