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<p>Dynamical systems theory provides a mathematical framework for describing how interacting biological components evolve over time and space, from molecular oscillators to large-scale biological patterns. Such systems often involve nonlinear feedbacks, delays and multiscale interactions, making mechanistic model construction increasingly challenging as experimental measurements become richer and higher dimensional. This has motivated the development of data-driven approaches that infer model structure directly from data, offering alternative routes to constructing dynamical models. In this Technical Review, we discuss and compare data-driven approaches for model discovery in biological dynamical systems, focusing on three major methodological families: regression-based methods, network-based architectures and decomposition techniques.</p>

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Data-driven discovery of dynamical models in biology

  • 期刊论文

  • 发表日期：

    2026年6月25日

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  • Bartosz Prokop,
  • Lendert Gelens

摘要

Dynamical systems theory provides a mathematical framework for describing how interacting biological components evolve over time and space, from molecular oscillators to large-scale biological patterns. Such systems often involve nonlinear feedbacks, delays and multiscale interactions, making mechanistic model construction increasingly challenging as experimental measurements become richer and higher dimensional. This has motivated the development of data-driven approaches that infer model structure directly from data, offering alternative routes to constructing dynamical models. In this Technical Review, we discuss and compare data-driven approaches for model discovery in biological dynamical systems, focusing on three major methodological families: regression-based methods, network-based architectures and decomposition techniques.