Parallelization of a Bio-inspired Computational Model for the Simulation of 3-D Multicellular Tissue Growth

The use of parallelism may overcome some of the constraints imposed by single processor computing systems. Besides offering faster solutions, applications that are parallelized can solve bigger or more complex problems. For instance, simulations can be run at finer resolutions while physical phenomena can be potentially modeled more realistically. We describe in this paper the development of a bio-inspired parallel algorithm used in the three-dimensional simulation of multicellular tissue growth. We report on the different components of the model where cellular automata is used to model different types of cell populations that execute persistent random walks on the computational grid, collide, and proliferate until they reach confluence. We also discuss the main issues encountered in the parallelization of the model and its implementation on a parallel machine.