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

Area of Science:

• Microbiology
• Computational Biology
• Ecology

Background:

• Microbial communities are vital for ecosystem functions.
• Understanding microbial dynamics requires predictive models.
• Current models (mechanistic and machine learning) have limitations in flexibility, data requirements, interpretability, and overfitting.

Purpose of the Study:

• To develop a novel hybrid modeling framework that overcomes limitations of existing approaches.
• To improve the prediction accuracy and interpretability of microbial community dynamics.

Main Methods:

• Developed a physics-constrained machine learning model named the neural species mediator (NSM).
• NSM integrates a mechanistic model of metabolite dynamics with a machine learning component.
• Validated the NSM using in vitro experimental datasets.

Main Results:

• The NSM demonstrated superior performance compared to purely mechanistic or machine learning models.
• The model provided valuable insights into direct biological interactions within microbial communities.
• Improved prediction performance and interpretability were achieved.

Conclusions:

• Embedding neural networks within mechanistic models offers a powerful approach for microbial community modeling.
• The NSM framework enhances predictive capabilities and biological understanding of microbiomes.
• This hybrid approach represents a significant advancement in modeling complex microbial ecosystems.