Novos algoritmos de simulação estocástica com atraso para redes gênicas

Abstract

Recently, the time efficiency of stochastic simulation algorithms for gene regulatory networks (GRN) has motivated several scientific works. Interest in such algorithms is because the new technologies in cell biology — called high-throughput technologies cell biology — have shown that gene expression is a stochastic process. In GRN with delays, the existing algorithms for stochastic simulation have some drawbacks — such as linear growth of complexity, excessive discard of random numbers, and the coding in a programming language can be hard — that result in poor performance during the simulation of very large GRN. This work presents an algorithm for stochastic simulation of GRN. We called it simplified next reaction method (SNRM). This algorithm was more efficient than other existing algorithms for stochastically simulation of GRN with delays. Besides SNRM, a new dependency graph for delayed reactions is also presented. The use of this new graph, which we named it delayed dependency graph (DDG), greatly increased the efficiency of all versions of the algorithms for stochastic simulation with delays presented in this work. Finally, a data structure that we named hashing sorted list is used to handle the waiting list of products in simulations of GRN with delays. This data structure was also more efficient than a heap in all tested simulations. With all the improvements mentioned, this work presents a set of strategies that contribute effectively to increasing performance of stochastic simulation algorithms with delays for gene regulatory networks.