A cellular automaton model for the proliferation of migrating contact-inhibited cells.

A cellular automaton is used to develop a model describing the proliferation dynamics of populations of migrating, contact-inhibited cells. Simulations are carried out on two-dimensional networks of computational sites that are finite-state automata. The discrete model incorporates all the essential features of the cell locomotion and division processes, including the complicated dynamic phenomena occurring when cells collide. In addition, model parameters can be evaluated by using data from long-term tracking and analysis of cell locomotion. Simulation results are analyzed to determine how the competing processes of contact inhibition and cell migration affect the proliferation rates. The relation between cell density and contact inhibition is probed by following the temporal evolution of the population-average speed of locomotion. Our results show that the seeding cell density, the population-average speed of locomotion, and the spatial distribution of the seed cells are crucial parameters in determining the temporal evolution of cell proliferation rates. The model successfully predicts the effect of cell motility on the growth of isolated megacolonies of keratinocytes, and simulation results agree very well with experimental data. Model predictions also agree well with experimentally measured proliferation rates of bovine pulmonary artery endothelial cells (BPAE) cultured in the presence of a growth factor (bFGF) that up-regulates cell motility.