Gillespie algorithm
In probability theory, the Gillespie algorithm (or the Doob-Gillespie algorithm or Stochastic Simulation Algorithm, the SSA) generates a statistically correct trajectory (possible solution) of a stochastic equation system for which the reaction rates are known. It was created by Joseph L. Doob and others (circa 1945), presented by Dan Gillespie in 1976, and popularized in 1977 in a paper where he uses it to simulate chemical or biochemical systems of reactions efficiently and accurately using limited computational power (see stochastic simulation). As computers have become faster, the algorithm has been used to simulate increasingly complex systems. The algorithm is particularly useful for simulating reactions within cells, where the number of reagents is low and keeping track of the posit
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- enIn probability theory, the Gillespie algorithm (or the Doob-Gillespie algorithm or Stochastic Simulation Algorithm, the SSA) generates a statistically correct trajectory (possible solution) of a stochastic equation system for which the reaction rates are known. It was created by Joseph L. Doob and others (circa 1945), presented by Dan Gillespie in 1976, and popularized in 1977 in a paper where he uses it to simulate chemical or biochemical systems of reactions efficiently and accurately using limited computational power (see stochastic simulation). As computers have become faster, the algorithm has been used to simulate increasingly complex systems. The algorithm is particularly useful for simulating reactions within cells, where the number of reagents is low and keeping track of the posit
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- enIn probability theory, the Gillespie algorithm (or the Doob-Gillespie algorithm or Stochastic Simulation Algorithm, the SSA) generates a statistically correct trajectory (possible solution) of a stochastic equation system for which the reaction rates are known. It was created by Joseph L. Doob and others (circa 1945), presented by Dan Gillespie in 1976, and popularized in 1977 in a paper where he uses it to simulate chemical or biochemical systems of reactions efficiently and accurately using limited computational power (see stochastic simulation). As computers have become faster, the algorithm has been used to simulate increasingly complex systems. The algorithm is particularly useful for simulating reactions within cells, where the number of reagents is low and keeping track of the position and behaviour of individual molecules is computationally feasible. Mathematically, it is a variant of a dynamic Monte Carlo method and similar to the kinetic Monte Carlo methods. It is used heavily in computational systems biology.
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- www.zora.uzh.ch/id/eprint/39866/1/PSSACR.pdf
- www.zora.uzh.ch/id/eprint/79206/1/pub8.pdf
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- Andrei Kolmogorov
- Category:Chemical kinetics
- Category:Computational chemistry
- Category:Monte Carlo methods
- Category:Stochastic simulation
- Computational systems biology
- Dan Gillespie
- David George Kendall
- Dynamic Monte Carlo method
- File:Example calculation illustrating the Gillespie algorithm for reversible dimerising molecules.png
- Joseph L. Doob
- Kinetic Monte Carlo
- Kolmogorov equations (Markov jump process)
- Manchester Mark 1
- Master equation
- Maurice S. Bartlett
- Probability theory
- Rate equation
- Reaction rates
- Reagent
- Stochastic
- Stochastic process
- Stochastic simulation
- Tau-leaping
- William Feller
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- Gillespie algorithm
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- Category:Chemical kinetics
- Category:Computational chemistry
- Category:Monte Carlo methods
- Category:Stochastic simulation
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