HRSSA - efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks. This paper introduces HRSSA (Hybrid Rejection-based Stochastic Simulation Algorithm), a new efficient hybrid stochastic simulation algorithm for spatially homogeneous biochemical reaction networks. HRSSA is built on top of RSSA, an exact stochastic simulation algorithm which relies on propensity bounds to select next reaction firings and to reduce the average number of reaction propensity updates needed during the simulation. HRSSA exploits the computational advantage of propensity bounds to manage time-varying transition propensities and to apply dynamic partitioning of reactions, which constitute the two most significant bottlenecks of hybrid simulation. A comprehensive set of simulation benchmarks is provided for evaluating performance and accuracy of HRSSA against other state of the art algorithms.
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References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Anderson, David F.; Yuan, Chaojie: Low variance couplings for stochastic models of intracellular processes with time-dependent rate functions (2019)
- Thanh, Vo Hong: A critical comparison of rejection-based algorithms for simulation of large biochemical reaction networks (2019)
- Kurasov, Pavel; Lück, Alexander; Mugnolo, Delio; Wolf, Verena: Stochastic hybrid models of gene regulatory networks -- a PDE approach (2018)
- Thanh, Vo Hong; Zunino, Roberto; Priami, Corrado: Efficient finite-difference method for computing sensitivities of biochemical reactions (2018)
- Marchetti, Luca; Lombardo, Rosario; Priami, Corrado: HSimulator: hybrid stochastic/deterministic simulation of biochemical reaction networks (2017)
- Marchetti, Luca; Priami, Corrado; Thanh, Vo Hong: HRSSA - efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks (2016)