Simulating cell-cell interactions using a multicellular three-dimensional computational model of tissue growth

Abstract

We report simulation results describing cell-cell interactions using a versatile computational model to simulate the growth of multicellular tissues employing a discrete approach based on cellular automata. In particular, we present results of cell collision and aggregation for three cell populations each having its own division and motion characteristics based on experimental data. The developed model allows us to study the tissue growth rates and population dynamics of different populations of migrating and proliferating mammalian cells in a mixed and segmented seeding distribution. In this regard, the model assumes that nutrient and growth factor concentrations remain constant in space and time. Cell migration is modeled using a discrete-time Markov chain approach. Both heterotypic and homotypic cell-cell interactions play important roles in cell and tissue functions. The temporal evolution of the frequency of cell collision and aggregation and their relations to other variables that quantify some of the dynamics of cell populations can be predicted by this model for different cell seeding distributions.