StochKit

StochKit is an extensible stochastic simulation framework developed in C++ that aims to make stochastic simulation accessible to practicing biologists and chemists, while remaining open to extension via new stochastic and multiscale algorithms.

Keywords for this software

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References in zbMATH (referenced in 10 articles )

Showing results 1 to 10 of 10.
Sorted by year (citations)

  1. Cinquemani, Eugenio: Stochastic reaction networks with input processes: analysis and application to gene expression inference (2019)
  2. Munsky, Brian (ed.); Hlavacek, William S. (ed.); Tsimring, Lev S. (ed.): Quantitative biology. Theory, computational methods, and models (2018)
  3. Schnoerr, David; Sanguinetti, Guido; Grima, Ramon: Approximation and inference methods for stochastic biochemical kinetics -- a tutorial review (2017)
  4. Popović, Nikola; Marr, Carsten; Swain, Peter S.: A geometric analysis of fast-slow models for stochastic gene expression (2016)
  5. Safta, Cosmin; Sargsyan, Khachik; Debusschere, Bert; Najm, Habib N.: Hybrid discrete/continuum algorithms for stochastic reaction networks (2015)
  6. Zunino, Roberto; Nikolić, Đurica; Priami, Corrado; Kahramanoğulları, Ozan; Schiavinotto, Tommaso: (\ell): an imperative DSL to stochastically simulate biological systems (2015) ioport
  7. Dayar, Tuǧrul: Analyzing Markov chains using Kronecker products. Theory and applications (2012)
  8. Dayar, Tuğrul; Orhan, M. Can: Kronecker-based infinite level-dependent QBD processes (2012)
  9. Buti, F.; Cacciagrano, D.; Corradini, F.; Merelli, E.; Tesei, L.; Pani, M.: Bone remodelling in \textscBioShape (2010)
  10. Mario Pineda-Krch: GillespieSSA: Implementing the Gillespie Stochastic Simulation Algorithm in R (2008) not zbMATH