Stochastic simulation of chemical kinetics.

By Daniel T. Gillespie. Annual review of physical chemistry (Print), 2007

2,182 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d64f64829ca2b04ec3fab50d58b7f5c83c95c1a3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

Citations in the corpus

Growth of Epitaxial Graphene: Theory and Experiment (2014, background): “KMC simulations are capable of providing a realistic platform for studying system dynamics (kinetics) related to performing activation events, and this is modeled as a sequence of consecutive jumps between free energy minima, in real time [217, 218, 219, 220].”
Simulating the evolution of soot mixing state with a particle-resolved aerosol model (2008, methodology): “Variants of Gillespie’s algorithm are widely used in different fields, including simulations of gene regulatory networks [El Samad et al., 2005], chemical kinetics [Gillespie, 2007], and sintering in flames [Wells et al., 2006].”
Fundamental limits on dynamic inference from single-cell snapshots (2017, positive): “Although Fokker–Planck descriptions are necessarily approximations, their emergence from first-principles descriptions of transcriptional dynamics in terms of chemical master equations (21), and their ubiquity in describing chemical reaction systems (22), help justify their use instead of the more g”
Functional Motifs in Biochemical Reaction Networks (2010, methodology): “Protein regulatory networks formulated in this way can be simulated either deterministically by solving Equation 2 with standard nonlinear ODE integrators, or stochastically by using Gillespie’s stochastic simulation algorithm (12).”
REVIEW: Predictive ecology in a changing world (2015, methodology): “simulations of probabilistic models (Gillespie 2007) or automatic verification of dynamical properties using”
Stochastic mechano-chemical kinetics of molecular motors: A multidisciplinary enterprise from a physicist’s perspective (2012, methodology): “Gillespie algorithm [2200, 71, 72] is the most popular technique for simulating the CME.”
Mathematical Modeling of Microbial Community Dynamics: A Methodological Review (2014, methodology): “Taking this concept a step further, new methods have been reported [90,91] that use statistical thermodynamics directly to solve a thermodynamic master equation similar to the kinetic master equation approach developed by Gillespie [92] and others.”
Modelling and analysis of gene regulatory networks (2008, methodology): “Here we present models that incorporate the stochastic nature of regulation by accounting for the fluctuations that occur on the molecular level (reviewed in ref”
Aerosol Mixing State: Measurements, Modeling, and Impacts (2019, methodology): “Variants of Gillespie's algorithm are widely used in different fields, including simulations of gene regulatory networks (Samad et al., 2005), chemical kinetics (Gillespie, 2007), and sintering in flames (Wells et al., 2006).”
Computation with finite stochastic chemical reaction networks (2008, methodology): “Our results may imply certain bounds on the speed of stochastic simulation algorithms (such as variants of τ -leaping [32]), suggesting an area of further study.”