gNODE: gLV model-informed neural ordinary differential equations for modeling microbial community dynamics
Background The human gut microbiota is a highly complex ecological system closely linked to host health, yet the functional mechanisms underlying its dynamic behavior remain poorly understood. Accurate modeling of microbial community dynamics is essential for elucidating these mechanisms. However, most existing approaches rely on densely sampled time-series data and often lack biological interpretability. Methods To address these challenges, we propose gNODE, a framework that integrates the generalized Lotka-Volterra (gLV) model with neural ordinary differential equations (NeuralODEs) to jointly predict microbial community dynamics, infer species interactions, and quantify the functional contributions of key taxa. By embedding ecological equations into a neural architecture, gNODE incorporates biological constraints directly into its model structure, enabling biologically meaningful parameter estimation and accurate inference even under sparse temporal sampling. Results Through simulations and real datasets, gNODE demonstrates superior performance in parameter estimation, trajectory prediction, and perturbation response modeling compared with existing methods. In a Clostridioides difficile infection dataset, gNODE accurately captured post-infection community trajectories and identified key inhibitory taxa, highlighting its potential to discover microbes that suppress pathogens. In a probiotic cocktail colonization dataset, gNODE identified diet-specific keystone species, underscoring its utility for assessing perturbation responses and guiding the design of probiotic consortia. Conclusion gNODE provides a robust and interpretable framework for modeling complex microbial community dynamics, offering new mechanistic and functional insights into the ecological processes that shape host-associated microbiomes.