Long time-scale simulations of in vivo diffusion using GPU hardware

To address the problem of performing long time simula-tions of biochemical pathways under in vivo cellular condi-tions, we have developed a lattice-based, reaction-diffusion model that uses the graphics processing unit (GPU) as a computational co-processor. The method has been specifi-cally designed from the beginning to take advantage of the GPU’s capacity to perform massively parallel calculations by not only executing a core set of mathematical calcula-tions, but also running much of the underlying algorith-mic logic on the GPU. In this study we present our three-dimensional model for in vivo diffusion that exploits the cal-culation capabilities of the GPU. The implementation of the diffusion operator on the GPU is subject to architectural constraints, and we discuss its structure and the trade-offs made to accommodate the GPU hardware. 1. Background and Significance The cell is a crowded space [5, 18] with proteins, nu-cleic acids, and other macromolecules constantly in con-tact with and colliding into each other. In the midst of this chaotic and turbulent scene, extensive and intricate net-works of biochemical reactions operate, in many cases, by random Brownian diffusion of one molecular species to its reaction counterpart. Often the concentration of one or bot