Beads, springs and fields: particle-based vs continuum models in cell biophysics

TL;DR

This review compares particle-based and continuum modeling approaches in cell biophysics across various biological systems, emphasizing their strengths, limitations, and applicability.

Contribution

It provides a comprehensive framework for selecting suitable modeling strategies in biophysical research, connecting theoretical models with experimental systems.

Findings

Particle-based models explicitly represent discrete components.

Continuum models describe systems through spatially varying fields.

The review highlights the strengths and limitations of each approach.

Abstract

Quantitative modeling has become an essential tool in modern biophysics, driven by advances in both experimental techniques and theoretical frameworks. Powerful high-resolution techniques now provide detailed datasets spanning molecular to tissue scales, allowing to visualize cellular structures with unprecedented detail. In parallel, developments in soft and active matter physics have established a robust theoretical basis for describing biological systems. In this context, two main modeling paradigms have emerged: particle-based models, which explicitly represent discrete components and their interactions, and continuum models, which describe systems through spatially varying fields. We compare these approaches across biological scales, highlighting their respective strengths, limitations, and domains of applicability. To keep our discussion biologically relevant, we focus on five systems of fundamental importance: the cytoskeleton, membranes, chromatin, biomolecular condensates and tissues. With this Review, we thus aim to provide a framework for both theorists and experimentalists to select appropriate modeling strategies, and highlight future directions in biophysical modeling.