COPASI: biochemical network simulator. COPASI is a software application for simulation and analysis of biochemical networks and their dynamics. COPASI is a stand-alone program that supports models in the SBML standard and can simulate their behavior using ODEs or Gillespie’s stochastic simulation algorithm; arbitrary discrete events can be included in such simulations. COPASI carries out several analyses of the network and its dynamics and has extensive support for parameter estimation and optimization. COPASI provides means to visualize data in customizable plots, histograms and animations of network diagrams. (list of features).

References in zbMATH (referenced in 69 articles )

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  1. Ciaran Welsh, Jin Xu, Lucian Smith, Matthias König, Kiri Choi, Herbert M. Sauro: libRoadRunner 2.0: A High-Performance SBML Simulation and Analysis Library (2022) arXiv
  2. Allart, Emilie; Niehren, Joachim; Versari, Cristian: Computing difference abstractions of linear equation systems (2021)
  3. Austen Bernardi, Jessica M.J. Swanson: CycFlowDec: A Python module for decomposing flow networks using simple cycles (2021) not zbMATH
  4. Jakob Vanhoefer, Marta R. A. Matos, Dilan Pathirana, Yannik Schälte, Jan Hasenauer: yaml2sbml: Human-readable and -writable specification of ODE models and their conversion to SBML (2021) not zbMATH
  5. Kraikivski, Pavel: Computational software (2021)
  6. Hinze, Thomas: Coping with dynamical reaction system topologies using deterministic P modules: a case study of photosynthesis (2020)
  7. Leonard Schmiester, Yannik Schälte, Frank T. Bergmann, Tacio Camba, Erika Dudkin, Janine Egert, Fabian Fröhlich, Lara Fuhrmann, Adrian L. Hauber, Svenja Kemmer, Polina Lakrisenko, Carolin Loos, Simon Merkt, Wolfgang Müller, Dilan Pathirana, Elba Raimúndez, Lukas Refisch, Marcus Rosenblatt, Paul L. Stapor, Philipp Städter, Dantong Wang, Franz-Georg Wieland, Julio R. Banga, Jens Timmer, Alejandro F. Villaverde, Sven Sahle, Clemens Kreutz, Jan Hasenauer, Daniel Weindl: PEtab - interoperable specification of parameter estimation problems in systems biology (2020) arXiv
  8. Schmiester, Leonard; Weindl, Daniel; Hasenauer, Jan: Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach (2020)
  9. Takeshi Abe; Yoshiyuki Asai: Flint: a simulator for biological and physiological models in ordinary and stochastic differential equations (2020) not zbMATH
  10. Aguilera, Luis U.; Rodríguez-González, Jesús: Modeling the effect of Tat inhibitors on HIV latency (2019)
  11. Alvarez, Robinson F.; Barbuto, José A. M.; Venegeroles, Roberto: A nonlinear mathematical model of cell-mediated immune response for tumor phenotypic heterogeneity (2019)
  12. Chen, Minghan; Wang, Shuo; Cao, Yang: Accuracy analysis of hybrid stochastic simulation algorithm on linear chain reaction systems (2019)
  13. Eshan D. Mitra, Ryan Suderman, Joshua Colvin, Alexander Ionkov, Andrew Hu, Herbert M. Sauro, Richard G. Posner, William S. Hlavacek: PyBioNetFit and the Biological Property Specification Language (2019) arXiv
  14. Houston, Matthew T.; Gutierrez, Juan B.: The FRiND model: a mathematical model for representing macrophage plasticity in muscular dystrophy pathogenesis (2019)
  15. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  16. Stalidzans, Egils; Landmane, Katrina; Sulins, Jurijs; Sahle, Sven: Misinterpretation risks of global stochastic optimisation of kinetic models revealed by multiple optimisation runs (2019)
  17. Alfonso Landeros, Timothy Stutz, Kevin L. Keys, Alexander Alekseyenko, Janet S. Sinsheimer, Kenneth Lange, Mary Sehl: BioSimulator.jl: Stochastic simulation in Julia (2018) arXiv
  18. Hinze, Thomas: The Java environment for nature-inspired approaches (JENA): a workbench for biocomputing and biomodelling enthusiasts (2018)
  19. Revell, Jeremy; Zuliani, Paolo: Stochastic rate parameter inference using the cross-entropy method (2018)
  20. Saccomani, Maria Pia; Thomaseth, Karl: The union between structural and practical identifiability makes strength in reducing oncological model complexity: a case study (2018)

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