Knowledge-Inclusive Adaptive Physics-Informed Neural Network for Microbial Interaction Modelling

• URL: http://arxiv.org/abs/2606.07686v1
• Date: Fri, 05 Jun 2026 03:13:03 GMT
• Title: Knowledge-Inclusive Adaptive Physics-Informed Neural Network for Microbial Interaction Modelling
• Abstract summary: We propose a new PINN framework that enriches parameter discovery by incorporating auxiliary knowledge sources.<n>Our framework improves over the state-of-the-art by up to 53%, even without knowledge.
• Score: 2.8872261254207103
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 $\mathrm{R}^2$.

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