gNODE: gLV model-informed neural ordinary differential equations for modeling microbial community dynamics

Area of Science:

• Microbiology
• Computational Biology
• Systems Biology

Background:

• The human gut microbiota's complex dynamics are crucial for host health but poorly understood.
• Existing models for microbial community dynamics often require extensive data and lack interpretability.

Purpose of the Study:

• To develop a robust framework for modeling microbial community dynamics.
• To infer species interactions and quantify functional contributions of key taxa.
• To enable accurate predictions even with sparse temporal data.

Main Methods:

• Integration of the generalized Lotka-Volterra (gLV) model with neural ordinary differential equations (NeuralODEs).
• Embedding ecological principles into a neural network architecture (gNODE).
• Joint prediction of community dynamics, interaction inference, and functional contribution quantification.

Main Results:

• gNODE outperforms existing methods in parameter estimation, trajectory prediction, and perturbation response modeling.
• Accurate modeling of post-infection dynamics and identification of inhibitory taxa in a Clostridioides difficile dataset.
• Identification of keystone species and assessment of perturbation responses in a probiotic colonization dataset.

Conclusions:

• gNODE offers a powerful and interpretable framework for microbiome research.
• Provides mechanistic and functional insights into host-associated microbial ecology.
• Facilitates the design of microbial consortia and understanding of microbiome-host interactions.