A Two-State Eukaryotic Cell Migration Model

Abstract

Cells are dynamic and motile objects and the way they make decisions about migratory directions has been an area of intensive research. To explain the intricate effects of genetic mutations and environmental changes on cell migration, we have devised a discrete stochastic trajectory decision model from simple mechanochemical principles. In the current model, the cells have been treated as mobile machines with two states of migration: persistent and random. The state transitions are assumed to obey Markovian dynamics. We used molecular diffusion and polymer analogies to estimate likelihoods of the trajectories. The model has been tested on human fibrosarcoma and fibroblast wild type cells, various knockdowns, and cells performing chemotaxis and galvanotaxis. The results demonstrate the adequacy of the model in fitting the experimental cell speed distribution, velocity autocorrelation, mean squared displacement and angles between consecutive velocity steps. The model has the potential to better explain more complex trajectory dynamics of cells including cancer invasion, wound healing, and cellular development.