dorsal/arxiv
View SchemaAn "All Possible Steps" Approach to the Accelerated Use of Gillespie's Algorithm
| Authors | Azi Lipshtat |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0703048 |
| URL | https://arxiv.org/abs/q-bio/0703048 |
| DOI | 10.1063/1.2730507 |
Abstract
Many physical and biological processes are stochastic in nature. Computational models and simulations of such processes are a mathematical and computational challenge. The basic stochastic simulation algorithm was published by D. Gillespie about three decades ago [D.T. Gillespie, J. Phys. Chem. {\bf 81}, 2340, (1977)]. Since then, intensive work has been done to make the algorithm more efficient in terms of running time. All accelerated versions of the algorithm are aimed at minimizing the running time required to produce a stochastic trajectory in state space. In these simulations, a necessary condition for reliable statistics is averaging over a large number of simulations. In this study I present a new accelerating approach which does not alter the stochastic algorithm, but reduces the number of required runs. By analysis of collected data I demonstrate high precision levels with fewer simulations. Moreover, the suggested approach provides a good estimation of statistical error, which may serve as a tool for determining the number of required runs.
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"abstract": "Many physical and biological processes are stochastic in nature.\nComputational models and simulations of such processes are a mathematical and\ncomputational challenge. The basic stochastic simulation algorithm was\npublished by D. Gillespie about three decades ago [D.T. Gillespie, J. Phys.\nChem. {\\bf 81}, 2340, (1977)]. Since then, intensive work has been done to make\nthe algorithm more efficient in terms of running time. All accelerated versions\nof the algorithm are aimed at minimizing the running time required to produce a\nstochastic trajectory in state space. In these simulations, a necessary\ncondition for reliable statistics is averaging over a large number of\nsimulations. In this study I present a new accelerating approach which does not\nalter the stochastic algorithm, but reduces the number of required runs. By\nanalysis of collected data I demonstrate high precision levels with fewer\nsimulations. Moreover, the suggested approach provides a good estimation of\nstatistical error, which may serve as a tool for determining the number of\nrequired runs.",
"arxiv_id": "q-bio/0703048",
"authors": [
"Azi Lipshtat"
],
"categories": [
"q-bio.QM",
"physics.comp-ph"
],
"doi": "10.1063/1.2730507",
"title": "An \"All Possible Steps\" Approach to the Accelerated Use of Gillespie\u0027s Algorithm",
"url": "https://arxiv.org/abs/q-bio/0703048"
},
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