The slow-scale stochastic simulation algorithm.

By Yang Cao, Daniel T. Gillespie, Linda R. Petzold. Journal of Chemical Physics, 2005

557 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/c684fc1b58cdfaf8bd910b7f06d1b29d4bdf4740

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Citations in the corpus

Stochastic simulation of chemical kinetics. (2007, background): “With the stochastic stiffness conditions satisfied, we now invoke the slow-scale approximation—a result that can be mathematically derived from the fundamental premise (Equation 2) ( 42 ).”
A Dynamical Low-Rank Approach to the Chemical Master Equation (2008, methodology): “that many reaction events will occur without significantly changing the reaction propensities, allowing to locally approximate the inhomogenous Poisson process by a simpler distribution [17, 36, 7, 5].”
The finite state projection algorithm for the solution of the chemical master equation. (2006, background): “proximations to reduce the number of stochastic events [ 1 , 10].”
Separation of time-scales and model reduction for stochastic reaction networks. (2010, background): “As observed in Cao, Gillespie, and Petzold (2005) and E, Liu, and Vanden-Eijnden (2007), the variables in the approximate models may correspond to linear combinations of species numbers.”
An overview of spatial microscopic and accelerated kinetic Monte Carlo methods (2007, methodology): “Time scale separation among various processes (stiffness; low and large barriers) Diffusion too fast in comparison to surface reactions; multiple diffusion paths with different barriers Net-event MC method [73];b Probability-weighted MC method [75];c Rescaling of probabilities [76];d Partial equilib”
Simulating the evolution of soot mixing state with a particle-resolved aerosol model (2008, methodology): “Multirate versions of Gillespie’s method have been developed previously by Cao et al. [2005] and E et al.”