Knowledge-Inclusive Adaptive Physics-Informed Neural Network for Microbial Interaction Modelling
This work addresses the problem of improving microbial community modeling by integrating diverse knowledge sources, which is important for microbiology researchers.
The paper proposes a knowledge-inclusive PINN framework that enriches parameter discovery by incorporating auxiliary knowledge sources (text and network structure) for microbial community modeling. The framework improves over state-of-the-art by up to 53% without knowledge, and knowledge addition yields gains of up to 23% in Bray-Curtis Dissimilarity-based accuracy and 47% in R².
Physics-Informed Neural Network (PINN) is a way of including knowledge in the form of equations in Machine Learning methods. Beyond equations, knowledge exists in other forms, such as text and network structure. While existing PINN-based approaches discover equation parameters from data, they rely solely on experimental measurements. We propose a new PINN framework that enriches parameter discovery by incorporating auxiliary knowledge sources. We instantiate our framework for microbiology, where generalised Lotka-Volterra (gLV) serves as a biological foundation for modelling microbial communities. We demonstrate that incorporating knowledge improves microbial community modelling. Our framework enriches the gLV parameters using peer-reviewed metagenomics literature, as text provides biological context on external influences that gLV alone cannot capture. We combine this knowledge with experimental measurements of microbial abundance using a data-driven integration approach. We integrate network-based structural knowledge by explicitly modelling microbial interactions. Our knowledge-inclusive framework infers microbial networks, revealing ecological insights. We validate these findings against ecological roles documented in the literature. We evaluate on real and simulated datasets spanning human- and plant-associated microbial communities. Our framework improves over the state-of-the-art by up to 53%, even without knowledge. Knowledge addition yields gains of up to 23% in Bray-Curtis Dissimilarity-based accuracy and 47% in $\mathrm{R}^2$.