Simulation of Scale Free Gene Regulatory Networks Based on Threshold Functions on GPU

Abstract

Gene regulatory networks have been used to study diseases and cell evolution, whereRandom Boolean graphs are one of computational approaches.A Boolean graph is a simple and effective model, and its dynamic behavior has been used in several works. This article proposes an efficient environment to simulate Boolean graphs on GPU (Graphics Processing Units). The dynamic behavior of a Boolean graph is computed by visiting the whole or a subset of state space. The proposed tool is based on statistical approaches toevaluate large graphs. Moreover, it can take into account scale free graphs withthreshold functions. The experimental results show a speed-up factor of up to three orders of magnitude greater than previous approaches.