StochKit2

StochKit2: software for discrete stochastic simulation of biochemical systems with events. Summary: StochKit2 is the first major upgrade of the popular StochKit stochastic simulation software package. StochKit2 provides highly efficient implementations of several variants of Gillespie’s stochastic simulation algorithm (SSA), and tau-leaping with automatic step size selection. StochKit2 features include automatic selection of the optimal SSA method based on model properties, event handling, and automatic parallelism on multicore architectures. The underlying structure of the code has been completely updated to provide a flexible framework for extending its functionality. Availability: StochKit2 runs on Linux/Unix, Mac OS X and Windows. It is freely available under GPL version 3 and can be downloaded from http://sourceforge.net/projects/stochkit/.


References in zbMATH (referenced in 11 articles )

Showing results 1 to 11 of 11.
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  1. Bentriou, Mahmoud; Ballarini, Paolo; Cournède, Paul-Henry: Automaton-ABC: a statistical method to estimate the probability of spatio-temporal properties for parametric Markov population models (2021)
  2. David Doty, Eric Severson: ppsim: A software package for efficiently simulating and visualizing population protocols (2021) arXiv
  3. Cinquemani, Eugenio: Stochastic reaction networks with input processes: analysis and application to gene expression inference (2019)
  4. Alfonso Landeros, Timothy Stutz, Kevin L. Keys, Alexander Alekseyenko, Janet S. Sinsheimer, Kenneth Lange, Mary Sehl: BioSimulator.jl: Stochastic simulation in Julia (2018) arXiv
  5. Feigelman, Justin; Weindl, Daniel; Theis, Fabian J.; Marr, Carsten; Hasenauer, Jan: LNA++: linear noise approximation with first and second order sensitivities (2018)
  6. Schnoerr, David; Sanguinetti, Guido; Grima, Ramon: Approximation and inference methods for stochastic biochemical kinetics -- a tutorial review (2017)
  7. Justin Feigelman, Stefan Ganscha, Manfred Claassen: matLeap: A fast adaptive Matlab-ready tau-leaping implementation suitable for Bayesian inference (2016) arXiv
  8. Popović, Nikola; Marr, Carsten; Swain, Peter S.: A geometric analysis of fast-slow models for stochastic gene expression (2016)
  9. Safta, Cosmin; Sargsyan, Khachik; Debusschere, Bert; Najm, Habib N.: Hybrid discrete/continuum algorithms for stochastic reaction networks (2015)
  10. Zunino, Roberto; Nikolić, Đurica; Priami, Corrado; Kahramanoğulları, Ozan; Schiavinotto, Tommaso: (\ell): an imperative DSL to stochastically simulate biological systems (2015) ioport
  11. Dayar, Tuğrul; Orhan, M. Can: Kronecker-based infinite level-dependent QBD processes (2012)