Computational modeling of cell spreading dynamics on micropatterned substrates: From geometric constraints to mechanistic insights
Phys. Rev. E 114, 024406 – Published 19 August, 2026
DOI: https://doi.org/10.1103/2z22-fync
Abstract
Cell spreading is a fundamental biophysical process that regulates cellular responses to extracellular signals, influencing essential functions such as migration and polarization. To quantitatively investigate how geometric constraints govern spreading dynamics, we develop a particle-based computational framework that integrates cellular deformation, stochastic adhesion kinetics, and actin-driven traction. The framework employs smoothed dissipative particle dynamics to model the fluid dynamics of both suspending medium and cytoplasm, coarse-grained vertex modeling for membrane mechanics, receptor-ligand binding for substrate adhesion, and a Brownian ratchet mechanism for traction generation. We validate the model against experiments involving human dental pulp stem cells confined to circular, square, and triangular geometries, and the simulations quantitatively reproduce geometry-driven spreading dynamics. Our results reveal that cell spreading is driven toward a dynamic mechanical equilibrium, where cell-substrate adhesion balances substrate repulsion at their contact interface, and in-plane traction forces at the periphery are counterbalanced by the resultant in-plane adhesive and sidewall repulsive forces, while internal deformation forces remain self-balanced. Moreover, the confinement area scales adhesion and traction forces through formed bond and generated leading particles, while acute vertices amplify traction via curvature-enhanced cytoskeletal contractility. Consequently, triangular confinement induces vertex-directed spreading through perimeter-dependent adhesion and localized traction, whereas circular geometries preserve directional isotropy, yielding uniform radial spreading. The computationally efficient framework provides mechanistic insights into how spatial constraints translate to cellular responses, although limitations persist in modeling nuclear mechanics, detailed cytoskeletal dynamics, and biochemical signaling.
