Position: Biology is the Challenge Physics-Informed ML Needs to Evolve

TL;DR

This paper advocates evolving Physics-Informed Machine Learning into Biology-Informed Machine Learning to address biological data complexities, emphasizing probabilistic priors, uncertainty, and scalability for scientific advancement.

Contribution

It introduces BIML as an extension of PIML tailored for biology, outlining four foundational pillars and emphasizing the role of foundation models.

Findings

BIML adapts PIML to biological uncertainties and heterogeneity.

Framework emphasizes uncertainty quantification and scalable inference.

Guidelines for building a BIML ecosystem are proposed.

Abstract

Physics-Informed Machine Learning (PIML) has successfully integrated mechanistic understanding into machine learning, particularly in domains governed by well-known physical laws. This success has motivated efforts to apply PIML to biology, a field rich in dynamical systems but shaped by different constraints. Biological modeling, however, presents unique challenges: multi-faceted and uncertain prior knowledge, heterogeneous and noisy data, partial observability, and complex, high-dimensional networks. In this position paper, we argue that these challenges should not be seen as obstacles to PIML, but as catalysts for its evolution. We propose Biology-Informed Machine Learning (BIML): a principled extension of PIML that retains its structural grounding while adapting to the practical realities of biology. Rather than replacing PIML, BIML retools its methods to operate under softer, probabilistic forms of prior knowledge. We outline four foundational pillars as a roadmap for this transition: uncertainty quantification, contextualization, constrained latent structure inference, and scalability. Foundation Models and Large Language Models will be key enablers, bridging human expertise with computational modeling. We conclude with concrete recommendations to build the BIML ecosystem and channel PIML-inspired innovation toward challenges of high scientific and societal relevance.