Bayesian inference captures metabolite-bacteria interactions in a microbial community.
Presents an ODE-based dynamic network model with a Bayesian inference workflow to infer metabolite–microbe interaction rates from time-series data and predict community responses to perturbations.
What the AI sees
Presents an ODE-based dynamic network model with a Bayesian inference workflow to infer metabolite–microbe interaction rates from time-series data and predict community responses to perturbations.
Research significance
Offers a rigorous, uncertainty-aware computational tool to quantify gut microbiome metabolic interactions and prioritize microbiota-targeted interventions relevant to the gut–brain axis in Parkinson's disease, but it lacks direct PD-specific mechanistic or clinical validation.
Source abstract
Macro-ecosystems, including the human gut, host a vast and diverse set of microbes that indirectly interact with each other through consuming and producing metabolites. Disruptions in this microbial network can affect macro-ecosystem functioning and, in the human gut, contribute to the onset and progression of various disorders, including diabetes, rheumatoid arthritis, and Parkinson's disease. A theoretical foundation for understanding the intricate and dynamic interactions between microbes and metabolites is essential for developing microbiota-targeted interventions to improve macro-ecosystem functioning and health. To this end, a precise mathematical framework is crucial to capture and quantify the complex dynamics of the microbial system. Here, we develop a dynamic network model of coupled ordinary differential equations and present a computational workflow that integrates a generative model with Bayesian inference for model identification. Our approach infers interaction rates, quantifying metabolite consumption and production from simulated time-series data within a Bayesian framework, incorporating prior knowledge and uncertainty quantification. We show that our approach is accurate and reliable in communities of various sizes, sparsity, and with different levels of observational noise. This workflow enables in silico predictions of system behaviour under perturbations and offers a robust method to integrate high-dimensional biological data with dynamic network models. By refining our understanding of microbial dynamics, this framework is capable of assessing microbiota-targeted interventions and their potential to improve the health of the macro-ecosystem.