AI Insight
This review article examines data-driven computational methods for discovering and modeling dynamical systems in biology, comparing three main approaches: regression-based methods, network-based architectures, and decomposition techniques. The authors evaluate how these methods perform across three key objectives—forecasting system behavior, identifying component interactions, and characterizing dynamical solutions like oscillations and steady states—using the Oregonator model as a common benchmark. The work provides a systematic framework for researchers to select appropriate computational tools for analyzing complex, nonlinear biological systems where traditional mechanistic modeling becomes impractical due to high-dimensional data.
Why it matters
As biological measurements become increasingly rich and complex, these data-driven approaches offer practical alternatives to traditional modeling, enabling researchers to extract meaningful insights from high-dimensional experimental data. This could accelerate discovery in fields ranging from molecular biology to ecology by making sophisticated dynamical systems analysis more accessible and applicable to real-world biological datasets.
Understand the Science
arXiv:2509.06735v2 Announce Type: replace
Abstract: 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 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. We compare how these approaches address three core objectives: forecasting future behavior, identifying interactions between system components, and characterizing qualitative dynamical solutions such as steady states, oscillations, and transitions between them. To enable a direct comparison, representative methods are applied to a common benchmark – the Oregonator model – a minimal nonlinear oscillator that captures shared design principles of chemical and biological systems. By highlighting practical strengths, limitations, and degrees of interpretability, this review aims to guide researchers in selecting appropriate tools for analyzing complex, nonlinear, and high-dimensional biological dynamics.
Source: Data-driven discovery of dynamical models in biology