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. © 2016 Elsevier Inc. All rights reserved
HRSSA – Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks / Marchetti, Luca; Priami, Corrado; Vo Hong, Thanh. - In: JOURNAL OF COMPUTATIONAL PHYSICS. - ISSN 0021-9991. - 317:(2016), pp. 301-317. [10.1016/j.jcp.2016.04.056]
HRSSA – Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks
Marchetti, Luca;Priami, Corrado;Thanh, Vo Hong
2016-01-01
Abstract
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. © 2016 Elsevier Inc. All rights reservedFile | Dimensione | Formato | |
---|---|---|---|
HRSSA.pdf
Solo gestori archivio
Tipologia:
Versione editoriale (Publisher’s layout)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
1.2 MB
Formato
Adobe PDF
|
1.2 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione