# 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:

- The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy)
- (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. Fri .
"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 = {Fri Jul 15 00:00:00 EDT 2016},

month = {Fri Jul 15 00:00:00 EDT 2016}

}