S-Leaping: An Adaptive, Accelerated Stochastic Simulation Algorithm, Bridging τ-Leaping and R-Leaping
Abstract
We propose the S-leaping algorithm for the acceleration of Gillespie’s stochastic sim ulation algorithm that combines the advantages of the two main accelerated methods;
the τ -leaping and R-leaping algorithms. These algorithms are known to be efficient
under different conditions; the τ -leaping is efficient for non-stiff systems or systems
with partial equilibrium, while the R-leaping performs better in stiff system thanks to
an efficient sampling procedure. However, even a small change in a system’s set up
can critically affect the nature of the simulated system and thus reduce the efficiency
of an accelerated algorithm. The proposed algorithm combines the efficient time step
selection from the τ -leaping with the effective sampling procedure from the R-leaping
algorithm. The S-leaping is shown to maintain its efficiency under different conditions
and in the case of large and stiff systems or systems with fast dynamics, the S-leaping
outperforms both methods. We demonstrate the performance and the accuracy of the
S-leaping in comparison with the τ -leaping and R-leaping on a number of benchmark
systems involving biological reaction networks. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000276447Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
Bulletin of Mathematical BiologyBand
Seiten / Artikelnummer
Verlag
SpringerThema
Stochastic simulation algorithms; Stiff systems; Accelerated simulationOrganisationseinheit
03499 - Koumoutsakos, Petros (ehemalig) / Koumoutsakos, Petros (former)
02803 - Collegium Helveticum / Collegium Helveticum
Förderung
341117 - Fluid Mechanics in Collective Behaviour: Multiscale Modelling and Applications (EC)
Anmerkungen
It was possible to publish this article open access thanks to a Swiss National Licence with the publisher.