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

Researchers propose a Physics-Informed Neural Network (PINN) framework that incorporates multiple knowledge sources—including peer-reviewed literature and network structures—to improve microbial community modeling beyond traditional equation-based approaches. The framework, applied to generalized Lotka-Volterra modeling, demonstrates significant performance improvements of up to 53% over existing methods, with additional gains of up to 23-47% when knowledge is integrated.

This research addresses a fundamental challenge in computational biology: improving predictive models of microbial communities by moving beyond purely data-driven or equation-based approaches. The work demonstrates that integrating diverse knowledge sources—scientific literature, network structures, and experimental data—creates more robust machine learning models. By enriching Physics-Informed Neural Networks with textual context from metagenomics research, the framework captures biological nuances that mathematical equations alone cannot represent, particularly external environmental influences affecting microbial interactions.

The advancement builds on existing PINN methodologies but extends them into a more holistic knowledge integration paradigm. Rather than treating machine learning models as black boxes that learn solely from experimental measurements, this framework leverages peer-reviewed domain expertise as structured inputs. The application to microbial ecology is particularly relevant given the complexity of microbiome research in human health, agriculture, and environmental science.

For the broader AI and computational biology sectors, this work validates the hypothesis that hybrid knowledge-inclusive machine learning approaches outperform single-modality solutions. The 23-47% performance gains when incorporating auxiliary knowledge suggest significant potential for similar frameworks across other domains requiring integration of structured knowledge, experimental data, and domain expertise. The framework's success on both simulated and real datasets spanning different ecological contexts indicates generalizability.

Future development likely involves scaling this approach to larger, more complex microbial systems and exploring similar knowledge-integration strategies in other scientific disciplines where domain expertise exists in multiple formats.

• →Physics-Informed Neural Networks enhanced with multiple knowledge sources outperform traditional equation-based or data-only approaches by up to 53%
• →Integrating peer-reviewed literature as auxiliary knowledge improved model accuracy by 23-47% compared to standard PINN methods
• →The framework successfully infers microbial interaction networks while capturing ecological insights validated against published literature
• →Knowledge-inclusive machine learning shows promise for computational biology applications requiring integration of structured expertise and experimental data
• →The approach demonstrates effectiveness across diverse microbial communities including human and plant-associated microbiomes