Inferring stability and persistence in the vaginal microbiome: A stochastic model of ecological dynamics
Ponciano, J. M.; Gomez, J. P.; Ravel, J.; Forney, L. J.
Show abstract
The vaginal microbiome is dynamic, yet the ecological and stochastic forces shaping its stability and persistence remain poorly understood. We developed a multi-species stochastic population model to analyze time-series data from 135 individuals sampled daily over 70 days, integrating ecological theory with microbial dynamics. Our framework explicitly incorporates stochasticity, ecological feedback, and sampling error to quantify community stability. We show that intra- and inter-species interactions and environmental fluctuations critically shape microbial population trajectories. This approach enabled the estimation of species competition coefficients and the identification of distinct stability regimes across individuals. We also introduce a Risk Prediction Monitoring (RPM) tool to track persistence probabilities of key taxa, particularly Lactobacillus spp., mirroring extinction risk models in conservation biology. Our findings challenge static "healthy" vs. "dysbiotic" categorizations and offer a quantitative framework for assessing microbiome resilience. This has direct implications for microbiome-targeted therapies aimed at promoting ecological stability in vaginal bacterial communities.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Accuracy of the Lotka-Volterra Model fails in strongly coupled microbial consumer-resource systems 97%
- Quantifying the impact of ecological memory on the dynamics of interacting communities 94%
- The stochastic logistic model with correlated carrying capacities reproduces beta-diversity metrics of microbial 94%
Similar papers in this journal
- EcologicalNetworksDynamics.jl: A Julia package to simulate the temporal dynamics of complex ecological networks 94%
- Inference of ecological networks and possibilistic dynamics based on Boolean networks from observations and prior knowledge 94%
- Identifying flow modules in ecological networks using Infomap 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.