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dc.contributor.authorSpill, Fabian
dc.contributor.authorMaini, Philip K.
dc.contributor.authorByrne, Helen M.
dc.date.accessioned2022-05-30T11:29:49Z
dc.date.available2022-05-30T11:29:49Z
dc.date.issued2016-02-24
dc.identifier.citationSpill, F., Maini, P. K., & Byrne, H. M. (2016). Optimisation of simulations of stochastic processes by removal of opposing reactions. The Journal of Chemical Physics, 144(8), 084105. doi:10.1063/1.4942413
dc.identifier.issn1089-7690
dc.identifier.issn0021-9606
dc.identifier.doi10.1063/1.4942413
dc.identifier.urihttp://hdl.handle.net/10754/678327
dc.description.abstractModels invoking the chemical master equation are used in many areas of science, and, hence, their simulation is of interest to many researchers. The complexity of the problems at hand often requires considerable computational power, so a large number of algorithms have been developed to speed up simulations. However, a drawback of many of these algorithms is that their implementation is more complicated than, for instance, the Gillespie algorithm, which is widely used to simulate the chemical master equation, and can be implemented with a few lines of code. Here, we present an algorithm which does not modify the way in which the master equation is solved, but instead modifies the transition rates. It works for all models in which reversible reactions occur by replacing such reversible reactions with effective net reactions. Examples of such systems include reaction-diffusion systems, in which diffusion is modelled by a random walk. The random movement of particles between neighbouring sites is then replaced with a net random flux. Furthermore, as we modify the transition rates of the model, rather than its implementation on a computer, our method can be combined with existing algorithms that were designed to speed up simulations of the stochastic master equation. By focusing on some specific models, we show how our algorithm can significantly speed up model simulations while maintaining essential features of the original model.
dc.description.sponsorshipThis publication was based on the work supported in part by Award No. KUK-C1-013-04, made by King Abdullah University of Science and Technology (KAUST). We are grateful to R. Erban, M. Flegg, A. McKane, and M. Robinson for helpful discussions, and the anonymous referees for their helpful suggestions.
dc.publisherAMER INST PHYSICS
dc.relation.urlhttp://aip.scitation.org/doi/10.1063/1.4942413
dc.titleOptimisation of simulations of stochastic processes by removal of opposing reactions
dc.typeArticle
dc.identifier.journalJOURNAL OF CHEMICAL PHYSICS
dc.identifier.wosutWOS:000371618800008
dc.contributor.institutionBoston Univ, Dept Biomed Engn, 44 Cummington St, Boston, MA 02215 USA
dc.contributor.institutionMIT, Dept Mech Engn, 77 Massachusetts Ave, Cambridge, MA 02139 USA
dc.contributor.institutionUniv Oxford, Math Inst, Wolfson Ctr Math Biol, Oxford OX2 6GG, England
dc.identifier.volume144
dc.identifier.issue8
dc.identifier.pages084105
kaust.grant.numberKUK-C1-013-04
dc.identifier.eid2-s2.0-84959450093


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