Literature DB >> 21727139

StochKit2: software for discrete stochastic simulation of biochemical systems with events.

Kevin R Sanft1, Sheng Wu, Min Roh, Jin Fu, Rone Kwei Lim, Linda R Petzold.   

Abstract

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/. CONTACT: petzold@engineering.ucsb.edu.

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Mesh：

Year:  2011        PMID： 21727139      PMCID： PMC3157925          DOI： 10.1093/bioinformatics/btr401

Source DB:  PubMed          Journal:  Bioinformatics        ISSN： 1367-4803            Impact factor:   6.937


  4 in total

1.  The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models.

Authors:  M Hucka; A Finney; H M Sauro; H Bolouri; J C Doyle; H Kitano; A P Arkin; B J Bornstein; D Bray; A Cornish-Bowden; A A Cuellar; S Dronov; E D Gilles; M Ginkel; V Gor; I I Goryanin; W J Hedley; T C Hodgman; J-H Hofmeyr; P J Hunter; N S Juty; J L Kasberger; A Kremling; U Kummer; N Le Novère; L M Loew; D Lucio; P Mendes; E Minch; E D Mjolsness; Y Nakayama; M R Nelson; P F Nielsen; T Sakurada; J C Schaff; B E Shapiro; T S Shimizu; H D Spence; J Stelling; K Takahashi; M Tomita; J Wagner; J Wang
Journal:  Bioinformatics       Date:  2003-03-01       Impact factor: 6.937

2.  Efficient formulation of the stochastic simulation algorithm for chemically reacting systems.

Authors:  Yang Cao; Hong Li; Linda Petzold
Journal:  J Chem Phys       Date:  2004-09-01       Impact factor: 3.488

3.  Efficient step size selection for the tau-leaping simulation method.

Authors:  Yang Cao; Daniel T Gillespie; Linda R Petzold
Journal:  J Chem Phys       Date:  2006-01-28       Impact factor: 3.488

4.  A constant-time kinetic Monte Carlo algorithm for simulation of large biochemical reaction networks.

Authors:  Alexander Slepoy; Aidan P Thompson; Steven J Plimpton
Journal:  J Chem Phys       Date:  2008-05-28       Impact factor: 3.488
  4 in total
  50 in total

1.  Constant-complexity stochastic simulation algorithm with optimal binning.

Authors:  Kevin R Sanft; Hans G Othmer
Journal:  J Chem Phys       Date:  2015-08-21       Impact factor: 3.488

2.  Theory of bi-molecular association dynamics in 2D for accurate model and experimental parameterization of binding rates.

Authors:  Osman N Yogurtcu; Margaret E Johnson
Journal:  J Chem Phys       Date:  2015-08-28       Impact factor: 3.488

3.  Note: Parameter-independent bounding of the stochastic Michaelis-Menten steady-state intrinsic noise variance.

Authors:  L A Widmer; J Stelling; F J Doyle
Journal:  J Chem Phys       Date:  2013-10-28       Impact factor: 3.488

4.  The pseudo-compartment method for coupling partial differential equation and compartment-based models of diffusion.

Authors:  Christian A Yates; Mark B Flegg
Journal:  J R Soc Interface       Date:  2015-05-06       Impact factor: 4.118

5.  Amplitude metrics for cellular circadian bioluminescence reporters.

Authors:  Peter C St John; Stephanie R Taylor; John H Abel; Francis J Doyle
Journal:  Biophys J       Date:  2014-12-02       Impact factor: 4.033

6.  Adaptive deployment of model reductions for tau-leaping simulation.

Authors:  Sheng Wu; Jin Fu; Linda R Petzold
Journal:  J Chem Phys       Date:  2015-05-28       Impact factor: 3.488

7.  A geometric analysis of fast-slow models for stochastic gene expression.

Authors:  Nikola Popović; Carsten Marr; Peter S Swain
Journal:  J Math Biol       Date:  2015-04-02       Impact factor: 2.259

8.  Lazy Updating of hubs can enable more realistic models by speeding up stochastic simulations.

Authors:  Kurt Ehlert; Laurence Loewe
Journal:  J Chem Phys       Date:  2014-11-28       Impact factor: 3.488

9.  Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art.

Authors:  David J Warne; Ruth E Baker; Matthew J Simpson
Journal:  J R Soc Interface       Date:  2019-02-28       Impact factor: 4.118

10.  Smart computational exploration of stochastic gene regulatory network models using human-in-the-loop semi-supervised learning.

Authors:  Fredrik Wrede; Andreas Hellander
Journal:  Bioinformatics       Date:  2019-12-15       Impact factor: 6.937
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