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