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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.

Authors:
 [1];  [1];  [2];  [1]
  1. The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy)
  2. (Italy)
Publication Date:
OSTI Identifier:
22572337
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Computational Physics; Journal Volume: 317; Other Information: Copyright (c) 2016 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; ACCURACY; ALGORITHMS; BENCHMARKS; BIOLOGY; PARTITION; PERFORMANCE; SIMULATION; STOCHASTIC PROCESSES

Citation Formats

Marchetti, Luca, E-mail: marchetti@cosbi.eu, Priami, Corrado, E-mail: priami@cosbi.eu, University of Trento, Department of Mathematics, and Thanh, Vo Hong, E-mail: vo@cosbi.eu. HRSSA – Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks. United States: N. p., 2016. Web. doi:10.1016/J.JCP.2016.04.056.
Marchetti, Luca, E-mail: marchetti@cosbi.eu, Priami, Corrado, E-mail: priami@cosbi.eu, University of Trento, Department of Mathematics, & Thanh, Vo Hong, E-mail: vo@cosbi.eu. HRSSA – Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks. United States. doi:10.1016/J.JCP.2016.04.056.
Marchetti, Luca, E-mail: marchetti@cosbi.eu, Priami, Corrado, E-mail: priami@cosbi.eu, University of Trento, Department of Mathematics, and Thanh, Vo Hong, E-mail: vo@cosbi.eu. 2016. "HRSSA – Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks". United States. doi:10.1016/J.JCP.2016.04.056.
@article{osti_22572337,
title = {HRSSA – Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks},
author = {Marchetti, Luca, E-mail: marchetti@cosbi.eu and Priami, Corrado, E-mail: priami@cosbi.eu and University of Trento, Department of Mathematics and Thanh, Vo Hong, E-mail: vo@cosbi.eu},
abstractNote = {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.},
doi = {10.1016/J.JCP.2016.04.056},
journal = {Journal of Computational Physics},
number = ,
volume = 317,
place = {United States},
year = 2016,
month = 7
}
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  • Direct solutions of the Chemical Master Equation (CME) governing Stochastic Reaction Networks (SRNs) are generally prohibitively expensive due to excessive numbers of possible discrete states in such systems. To enhance computational efficiency we develop a hybrid approach where the evolution of states with low molecule counts is treated with the discrete CME model while that of states with large molecule counts is modeled by the continuum Fokker–Planck equation. The Fokker–Planck equation is discretized using a 2nd order finite volume approach with appropriate treatment of flux components. The numerical construction at the interface between the discrete and continuum regions implements themore » transfer of probability reaction by reaction according to the stoichiometry of the system. The performance of this novel hybrid approach is explored for a two-species circadian model with computational efficiency gains of about one order of magnitude.« less