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Literature DB >> 19368432

An exact accelerated stochastic simulation algorithm.

Eric Mjolsness¹, David Orendorff, Philippe Chatelain, Petros Koumoutsakos.

Abstract

An exact method for stochastic simulation of chemical reaction networks, which accelerates the stochastic simulation algorithm (SSA), is proposed. The present "ER-leap" algorithm is derived from analytic upper and lower bounds on the multireaction probabilities sampled by SSA, together with rejection sampling and an adaptive multiplicity for reactions. The algorithm is tested on a number of well-quantified reaction networks and is found experimentally to be very accurate on test problems including a chaotic reaction network. At the same time ER-leap offers a substantial speedup over SSA with a simulation time proportional to the 23 power of the number of reaction events in a Galton-Watson process.

Mesh：

Year: 2009 PMID： 19368432 PMCID： PMC2852436 DOI： 10.1063/1.3078490

Source DB: PubMed Journal: J Chem Phys ISSN： 0021-9606 Impact factor: 3.488

10 in total

1. Time accelerated Monte Carlo simulations of biological networks using the binomial tau-leap method.

Authors: Abhijit Chatterjee; Kapil Mayawala; Jeremy S Edwards; Dionisios G Vlachos
Journal: Bioinformatics Date: 2005-02-04 Impact factor: 6.937

2. Nested stochastic simulation algorithm for chemical kinetic systems with disparate rates.

Authors: Weinan E; Di Liu; Eric Vanden-Eijnden
Journal: J Chem Phys Date: 2005-11-15 Impact factor: 3.488

3. An equation-free probabilistic steady-state approximation: dynamic application to the stochastic simulation of biochemical reaction networks.

Authors: Howard Salis; Yiannis N Kaznessis
Journal: J Chem Phys Date: 2005-12-01 Impact factor: 3.488

4 in total

An exact accelerated stochastic simulation algorithm.

1. Time accelerated Monte Carlo simulations of biological networks using the binomial tau-leap method.

2. Nested stochastic simulation algorithm for chemical kinetic systems with disparate rates.

3. An equation-free probabilistic steady-state approximation: dynamic application to the stochastic simulation of biochemical reaction networks.

4. Overcoming stiffness in stochastic simulation stemming from partial equilibrium: a multiscale Monte Carlo algorithm.

5. Avoiding negative populations in explicit Poisson tau-leaping.

6. R-leaping: accelerating the stochastic simulation algorithm by reaction leaps.

7. The SBML discrete stochastic models test suite.

8. Efficient step size selection for the tau-leaping simulation method.

9. Multiresolution stochastic simulations of reaction-diffusion processes.

10. A constant-time kinetic Monte Carlo algorithm for simulation of large biochemical reaction networks.

1. Efficient stochastic simulation of chemical kinetics networks using a weighted ensemble of trajectories.

2. Prospects for Declarative Mathematical Modeling of Complex Biological Systems.

3. A hierarchical exact accelerated stochastic simulation algorithm.

4. Time-ordered product expansions for computational stochastic system biology.