An Improved Simulation Of Hybrid Biological Models With Many Stochastic Events And Quasi-Disjoint Subnets

Hybrid simulation, combining exact and approximate algorithms, provides an alternative to a completely stochastic simulation. However, one challenge for the efficient implementation of hybrid simulations is the additional overhead due to frequent switches between the two regimes. The amount of additional overhead considerably increases with the number of discrete events in the stochastic regime. However, reactions that take place rather frequently cannot completely be avoided due to the accuracy requirements. In this paper, we present an improved hybrid simulation method which takes advantage of the Hybrid Rejection-based Stochastic Simulation Algorithm (HRSSA), a variant of the hybrid simulation approach. To reduce the overhead on account of the switches from the stochastic to the deterministic regime, we analyse and record the dependencies of reactions as well as species between the stochastic and deterministic subnetworks. Comparing our technique with existing ones shows a clear improvement in terms of runtime, while preserving accuracy.