Towards Data-Driven Modeling of Cell Cycle and Wound Closure Processes

TL;DR

This paper develops a computational model integrating cell cycle stages, migration, and proliferation to better understand fibroblast-driven wound closure, linking single-cell dynamics with tissue-level repair processes.

Contribution

It introduces a data-driven, cell-based mechanical model that incorporates cell cycle arrest and spatial arrangement effects on wound healing.

Findings

G1 arrest significantly affects wound closure dynamics.

Initial spatial distribution of cell cycle states influences healing outcomes.

Model reproduces key experimental trends in fibroblast migration and proliferation.

Abstract

Effective wound repair treatments rely on a clear picture of how cell proliferation and migration are coordinated during tissue restoration. Fibroblasts are key contributors to tissue restoration in the dermis, and modern imaging tools allow their cell-cycle progression to be observed directly, enabling comparison between experiments and computational models. Here we investigate how different stages of the cell cycle influence fibroblast-driven wound closure using the Discrete Laplacian Cell Mechanics (DLCM) framework driven by time-lapse microscopy data. \textit{In vitro} assays provide cell positions, migration behaviour, and cycle-stage information, and we show that incorporating proliferation, migration, and cell cycle arrest allows the computational model to reproduce the essential experimental trends. The results reveal that arrest in the G1 phase notably impacts the cell cycle dynamics and that the initial spatial arrangement of cycle states significantly affects wound closure. By linking single-cell cycle dynamics with emergent tissue behaviour this work establishes a quantitative approach for exploring how intracellular processes shape repair processes. More broadly, it demonstrates the value of integrating high-resolution data with cell-based mechanical models and provides a foundation for systematic \textit{in silico} evaluation of therapeutic interventions.