Physics-constrained neural ordinary differential equation models to discover and predict microbial community dynamics.

ABSTRACT: Microbial communities play essential roles in shaping ecosystem functions and predictive modeling frameworks are crucial for understanding, controlling, and harnessing their properties. Competition and cross-feeding of metabolites drives microbiome dynamics and functions. Existing mechanistic models that capture metabolite-mediated interactions in microbial communities have limited flexibility due to rigid assumptions. While machine learning models provide flexibility, they require large datasets, are challenging to interpret, and can over-fit to experimental noise. To overcome these limitations, we develop a physics-constrained machine learning model, which we call the Neural Species Mediator (NSM), that combines a mechanistic model of metabolite dynamics with a machine learning component

SUBMITTER: Thompson J 

PROVIDER: S-EPMC12265519 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature