Fast Exact Stochastic Simulation Algorithms Using Partial Propensities

We review the class of partial-propensity exact stochastic simulation algorithms (SSA) for chemical reaction networks. We show which modules partial-propensity SSAs are composed of and how partial-propensity variants of known SSAs can be constructed by adjusting the sampling strategy used. We demonstrate this on the example of two instances, namely the partial-propensity variant of Gillespie’s original direct method and that of the SSA with composition-rejection sampling (SSA-CR). Partialpropensity methods may outperform the corresponding classical SSA, particularly on strongly coupled reaction networks. Changing the different modules of partial-propensity SSAs provides flexibility in tuning them to perform particularly well on certain classes of reaction networks. The framework presented here defines the design space of such adaptations. DOI: https://doi.org/10.1063/1.3497968 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-79209 Published Version Originally published at: Ramaswamy, Rajesh; Sbalzarini, Ivo F (2010). Fast Exact Stochastic Simulation Algorithms Using Partial Propensities. In: International Conference of Numerical Analysis and Applied Mathematics, Rhodes, Greece, 19 September 2010 25 September 2010, 1338-1341. DOI: https://doi.org/10.1063/1.3497968 Fast Exact Stochastic Simulation Algorithms Using Partial Propensities Rajesh Ramaswamy and Ivo F. Sbalzarini Citation: AIP Conference Proceedings 1281, 1338 (2010); doi: 10.1063/1.3497968 View online: http://dx.doi.org/10.1063/1.3497968 View Table of