An agent-based model of wound healing in fibroblast scratch assays

Abstract

Wound healing assays are widely used to study fibroblast migration and proliferation in the lab. Although traditional scratch assays provide valuable insight, they can be time-intensive, inflexible, and difficult to reproduce. Computational models provide an efficient and reproducible way to study fibroblast behavior in silico. We developed an agent-based simulation of fibroblast scratch assays that models migration, proliferation, contact inhibition, cell-cell mechanical interactions, and the extracellular matrix (ECM) through a particle representation of fibroblasts and the ECM. To validate our model, we performed two tests: a leave-one-out test, where model parameters were calibrated to 6 imaging time series and tested on the remaining one, and a prediction test, where model parameters were calibrated to the first 24 hours of data and the remaining 66 hours of data were predicted. In our leave-one-out test, our model achieved a mean normalized root mean squared error (mean NRMSE) of 3.34%, and a Pearsons R of > 0.98 across all tested wound geometries time series. In the prediction test, the model predicted the remaining 66 hours of wound closure with an NRMSE of 2.08%. These results demonstrate that experimentally observed wound healing behavior of fibroblasts across different geometries can be modeled by local, agent-based rules. Our model provides an efficient platform for the experimentation and in silico study of fibroblast behavior.