Proposed running head: Parallelization of SSA on GPU

The small number of some reactant molecules in biological systems formed by living cells can result in dynamical behavior which cannot be captured by the traditional deterministic approaches. In that case, a more accurate simulation can be obtained with Gillespie’s Stochastic Simulation Algorithm (SSA). Since for realistic practical biochemical systems, the simulation by the SSA carries a high computational cost, specifically for large systems, several formulations have been proposed to increase the efficiency of this algorithm. In this paper we propose a highly efficient and scalable formulation of SSA with an optimal static heap structure to achieve logarithmic computational complexity for the whole simulation