RSSA: a Rejection-based Stochastic Simulation Algorithm

In this study we propose an improvement for the stochastic simulation algorithm (SSA), a standard method to properly realize the stochastic nature of biochemical reactions. Our algorithm is named RSSA after “rejection-based SSA”, and is tailored for the efficient simulation of large models, which is typically done to understand the complex behavior of regulatory systems. The large models are highly coupled, and full of interconnection and feedback loops. In addition, in these models a complex propensity function is often used to express the stochastic rate of biochemical reactions. Evaluating and updating the reaction propensities is therefore rather computationally expensive. RSSA reduces this computational burden by postponing the full update and only computing the propensity as needed. We experiment with our algorithm on concrete biological models to demonstrate its efficiency.