CERENA (ChEmical REaction Network Analyzer) is a MATLAB-based toolbox for the simulation and analysis of chemical reaction networks using approximations of the Chemical Master Equation solution statistics. CERENA includes a variety of methods for the analysis of stochastic biochemical reaction networks, focusing on mesoscopic and macroscopic descriptions, namely Reaction Rate Equation (RRE), Method of Moments (MM), and System Size Expansion (SSE). Also, CERENA provides the first implementation of the Method of Conditional Moments (MCM), and offers a wide range of options, amongst others variable truncation orders and different closure schemes.

References in zbMATH (referenced in 13 articles )

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  1. Loos, Carolin; Hasenauer, Jan: Robust calibration of hierarchical population models for heterogeneous cell populations (2020)
  2. Bortolussi, Luca; Lanciani, Roberta; Nenzi, Laura: Model checking Markov population models by stochastic approximations (2018)
  3. Feigelman, Justin; Weindl, Daniel; Theis, Fabian J.; Marr, Carsten; Hasenauer, Jan: LNA++: linear noise approximation with first and second order sensitivities (2018)
  4. Gupta, Ankit; Khammash, Mustafa: Computational identification of irreducible state-spaces for stochastic reaction networks (2018)
  5. Kurasov, Pavel; Lück, Alexander; Mugnolo, Delio; Wolf, Verena: Stochastic hybrid models of gene regulatory networks -- a PDE approach (2018)
  6. Pájaro, Manuel; Alonso, Antonio A.; Otero-Muras, Irene; Vázquez, Carlos: Stochastic modeling and numerical simulation of gene regulatory networks with protein bursting (2017)
  7. Schnoerr, David; Sanguinetti, Guido; Grima, Ramon: Approximation and inference methods for stochastic biochemical kinetics -- a tutorial review (2017)
  8. Boiger, R.; Hasenauer, J.; Hroß, S.; Kaltenbacher, B.: Integration based profile likelihood calculation for PDE constrained parameter estimation problems (2016)
  9. Duncan, Andrew; Erban, Radek; Zygalakis, Konstantinos: Hybrid framework for the simulation of stochastic chemical kinetics (2016)
  10. Ganguly, Arnab; Altintan, Derya; Koeppl, Heinz: Jump-diffusion approximation of stochastic reaction dynamics: error bounds and algorithms (2015)
  11. Ruess, Jakob; Lygeros, John: Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks (2015)
  12. Zimmer, Christoph: Reconstructing the hidden states in time course data of stochastic models (2015)
  13. Hasenauer, J.; Wolf, V.; Kazeroonian, A.; Theis, F. J.: Method of conditional moments (MCM) for the chemical master equation (2014)