Surrogate modeling of interactions in microbial communities through Physics-Informed Neural Networks

Microorganisms form complex communities known as microbiota, influencing various aspects of host well-being. The Generalized Lotka-Volterra (GLV) model is commonly used to understand microorganism population dynamics, but its application to the microbiota faces challenges due to limited bacterial data and complex interactions. This preliminary work focuses on using a Physics-Informed Neural Network (PINN) and synthetic data to build a surrogate model of bacterial species evolution driven by a GLV model. The approach is calibrated and tested on several models differing in size and dynamic behavior.