GillespieSSA: Gillespie’s Stochastic Simulation Algorithm (SSA)

`GillespieSSA`: Gillespie’s Stochastic Simulation Algorithm (SSA)

GillespieSSA provides a simple to use, intuitive, and extensible interface to several stochastic simulation algorithms for generating simulated trajectories of finite population continuous-time model. Currently it implements Gillespie’s exact stochastic simulation algorithm (Direct method) and several approximate methods (Explicit tau-leap, Binomial tau-leap, and Optimized tau-leap).

The package also contains a library of template models that can be run as demo models and can easily be customized and extended. Currently the following models are included, decaying-dimerization reaction set, linear chain system, logistic growth model, Lotka predator-prey model, Rosenzweig-MacArthur predator-prey model, Kermack-McKendrick SIR model, and a metapopulation SIRS model.

Install

You can install GillespieSSA from CRAN using

install.packages("GillespieSSA")

Or, alternatively, you can install the development version of GillespieSSA from GitHub using

devtools::install_github("rcannood/GillespieSSA", build_vignettes = TRUE)

Examples

The following example models are available:

Decaying-Dimerization Reaction Set (Gillespie, 2001): vignette("decaying_dimer", package="GillespieSSA")
SIRS metapopulation model (Pineda-Krch, 2008): vignette("epi_chain", package="GillespieSSA")
Linear Chain System (Cao et al., 2004): vignette("linear_chain", package="GillespieSSA")
Pearl-Verhulst Logistic growth model (Kot, 2001): vignette("logistic_growth", package="GillespieSSA")
Lotka predator-prey model (Gillespie, 1977; Kot, 2001): vignette("lotka_predator_prey", package="GillespieSSA")
Radioactive decay model (Gillespie, 1977): vignette("radioactive_decay", package="GillespieSSA")
Rosenzweig-MacArthur predator-prey model (Pineda-Krch et al., 2007): vignette("rm_predator_prey", package="GillespieSSA")
Kermack-McKendrick SIR model (Brown & Rothery, 1993): vignette("sir", package="GillespieSSA")

Latest changes

Check out news(package = "GillespieSSA") or NEWS.md for a full list of changes.

Recent changes in GillespieSSA 0.6.2

MINOR CHANGE: Allow using .t as parameter in the propensity functions.

Recent changes in GillespieSSA 0.6.1

This release contains a major rewrite of the internal code, to make sure the code is readable and that the algorithm doesn’t continuously update the local environment.

MAJOR CHANGE: Instead of passing "D", "ETL", "OTL", or "BTL" to ssa(), it is expected to pass ssa.d(), ssa.etl(), ssa.otl(), or ssa.btl(). This cleans up parameter setting clutter in the ssa() function.
MAJOR CHANGE: Rewrite ssa.*() and ssa.*.diag() as ssa_step.ssa_*() and ssa_step_diag.ssa_*() S3 functions.
MAJOR CHANGE: Do not save the current state in the function environment. Instead, simply save it in a local variable.
MAJOR CHANGE: Precompile propensity functions instead of evaluating them as R code at each iteration.
MAJOR CHANGE: Clean up and merge ssa.run(), ssa.terminate(), ssa.check.args() and ssa.check.method() into ssa().

References

Brown D. and Rothery P. 1993. Models in biology: mathematics, statistics, and computing. John Wiley & Sons.
Cao Y., Li H., and Petzold L. 2004. Efficient formulation of the stochastic simulation algorithm for chemically reacting systems. J. Chem. Phys. 121:4059-4067. doi:10.1063/1.1778376
Cao Y., Gillespie D.T., and Petzold L.R. 2006. Efficient step size selection for the tau-leaping method. J. Chem. Phys. 124:044109. doi:10.1063/1.2159468
Cao Y., Gillespie D.T., and Petzold L.R. 2007. Adaptive explicit tau-leap method with automatic tau selection. J. Chem. Phys. 126:224101. doi:10.1063/1.2745299
Chatterjee A., Vlachos D.G., and Katsoulakis M.A. 2005. Binomial distribution based tau-leap accelerated stochastic simulation. J. Chem. Phys. 122:024112. doi:10.1063/1.1833357
Gillespie D.T. 1977. Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81:2340. doi:10.1021/j100540a008
Gillespie D.T. 2001. Approximate accelerated stochastic simulation of chemically reacting systems. J. Chem. Phys. 115:1716-1733. doi:10.1063/1.1378322
Gillespie D.T. 2007. Stochastic simulation of chemical kinetics. Annu. Rev. Chem. 58:35 doi:10.1146/annurev.physchem.58.032806.104637
Kot M. 2001. Elements of mathematical ecology. Cambridge University Press. doi:10.1017/CBO9780511608520
Pineda-Krch M. 2008. Implementing the stochastic simulation algorithm in R. Journal of Statistical Software 25(12): 1-18. doi: 10.18637/jss.v025.i12
Pineda-Krch M., Blok H.J., Dieckmann U., and Doebeli M. 2007. A tale of two cycles — distinguishing quasi-cycles and limit cycles in finite predator-prey populations. Oikos 116:53-64. doi:10.1111/j.2006.0030-1299.14940.x