Learning biological dynamics beyond pairwise networks

Abstract

Systems biology has traditionally relied on network abstractions and mechanistic models to study complex biological systems. However, advances in model expressiveness and data availability have not translated into proportional improvements in mechanistic understanding. We suggest that a key limitation may arise from a structural mismatch: prevailing pairwise interaction models fail to capture the inherently higher-order organization of biological systems. Across molecular, cellular, and ecological scales, system behavior is governed by cooperative, conditional, and context-dependent multi-body interactions that cannot be faithfully represented by pairwise projections alone. To address this challenge, we advocate a paradigm shift toward explicit higher-order structural representations combined with data-driven, learnable dynamical models. Within this framework, artificial intelligence enables the inference of governing dynamical rules and the discovery of mechanisms operating on higher-order structures, while large language models can accelerate hypothesis generation and the integration of prior knowledge. Together, these advances point toward a unified, generative approach to systems biology that moves beyond descriptive networks toward an interpretable, mechanism-driven understanding of the processes underlying biological function.

Keywords

Systems biology / higher-order networks / complex networks / dynamical system identification / data-driven dynamics

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