Stochastic Simulations of Cellular Biological Processes

Abstract reaction event that occurs in the system, the accuracy of the method comes at a high computational cost. This is At the functional level, all biological processes in especially true for systems with a large number of cells can be represented as a series of biochemical molecular species, where reactions occur numerous timesreactions that are stochastic in nature. We have in a short period of time. To accelerate discrete stochasticdeveloped a software package called Biomolecular simulation, Gillespie proposed the tau-leaping method asNetwork Simulator (BNS) that uses a stochastic approach an approximate simulation strategy[21. By using PoissonNetwrk imuator(BN) t t ues stchasic pprach random numbers, the tau-leaping method can leap over to model and simulate complex biomolecular reaction ran on s t thod cae o networks. Two simulation algorithms-the exact many reactions without a significant loss of accuracy. Gillespie stochastic simulation algorithm and the We developed a software package-the BNS-that approximate adaptive tau-leaping algorithm-are can use the Gillespie stochastic algorithm or the tau-implemented for generating Monte Carlo trajectories that leaping algorithm to simulate the behavior of a system of describe the evolution of a system of biochemical biochemical reactions. It allows scientists to build a reactions. The software uses a combination of M4TLAB synthetic biomolecular network and explore its and C-coded functions and is parallelized with the performance utilizing the capacities of high performanceMessage Passing Interface (MPI) library to run on computing (HPC). BNS contains tools for both multiprocessor architectures. simulating the system and for analyzing the results of the simulation. Since the simulations are stochastic in nature, the user must run thousands of simulations to characteriz