HRSSA - Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks

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HRSSA - Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks. / Marchetti, Luca; Priami, Corrado; Thanh, Vo Hong.

julkaisussa: Journal of Computational Physics, Vuosikerta 317, 2016, s. 301-317.

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

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Bibtex - Lataa

@article{b7ed024d7e11462cb1528b19996a6f76,
title = "HRSSA - Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks",
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.",
keywords = "Biochemical reaction networks, Hybrid simulation, Stochastic simulation, Systems biology",
author = "Luca Marchetti and Corrado Priami and Thanh, {Vo Hong}",
note = "cited By 8",
year = "2016",
doi = "10.1016/j.jcp.2016.04.056",
language = "English",
volume = "317",
pages = "301--317",
journal = "Journal of Computational Physics",
issn = "0021-9991",
publisher = "Academic Press Inc.",

}

RIS - Lataa

TY - JOUR

T1 - HRSSA - Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks

AU - Marchetti, Luca

AU - Priami, Corrado

AU - Thanh, Vo Hong

N1 - cited By 8

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Biochemical reaction networks

KW - Hybrid simulation

KW - Stochastic simulation

KW - Systems biology

UR - http://www.scopus.com/inward/record.url?scp=84964798260&partnerID=8YFLogxK

U2 - 10.1016/j.jcp.2016.04.056

DO - 10.1016/j.jcp.2016.04.056

M3 - Article

VL - 317

SP - 301

EP - 317

JO - Journal of Computational Physics

JF - Journal of Computational Physics

SN - 0021-9991

ER -

ID: 27839823