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 54 articles )

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  1. Aguilera, Luis U.; Rodríguez-González, Jesús: Modeling the effect of Tat inhibitors on HIV latency (2019)
  2. Alvarez, Robinson F.; Barbuto, José A. M.; Venegeroles, Roberto: A nonlinear mathematical model of cell-mediated immune response for tumor phenotypic heterogeneity (2019)
  3. Chen, Minghan; Wang, Shuo; Cao, Yang: Accuracy analysis of hybrid stochastic simulation algorithm on linear chain reaction systems (2019)
  4. Houston, Matthew T.; Gutierrez, Juan B.: The FRiND model: a mathematical model for representing macrophage plasticity in muscular dystrophy pathogenesis (2019)
  5. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  6. Stalidzans, Egils; Landmane, Katrina; Sulins, Jurijs; Sahle, Sven: Misinterpretation risks of global stochastic optimisation of kinetic models revealed by multiple optimisation runs (2019)
  7. Alfonso Landeros, Timothy Stutz, Kevin L. Keys, Alexander Alekseyenko, Janet S. Sinsheimer, Kenneth Lange, Mary Sehl: BioSimulator.jl: Stochastic simulation in Julia (2018) arXiv
  8. Hinze, Thomas: The Java environment for nature-inspired approaches (JENA): a workbench for biocomputing and biomodelling enthusiasts (2018)
  9. Mendes, Pedro: Reproducible research using biomodels (2018)
  10. Revell, Jeremy; Zuliani, Paolo: Stochastic rate parameter inference using the cross-entropy method (2018)
  11. Saccomani, Maria Pia; Thomaseth, Karl: The union between structural and practical identifiability makes strength in reducing oncological model complexity: a case study (2018)
  12. von Stechow, Louise (ed.); Delgado, Alberto Santos (ed.): Computational cell biology. Methods and protocols (2018)
  13. Weilong Hu; Yannis Pantazis; Markos Katsoulakis: ISAP-MATLAB Package for Sensitivity Analysis of High-Dimensional Stochastic Chemical Networks (2018) not zbMATH
  14. Galochkina, T.; Chelushkin, M.; Sveshnikova, A.: Activation of contact pathway of blood coagulation on the lipopolysaccharide aggregates (2017)
  15. Marchetti, Luca; Lombardo, Rosario; Priami, Corrado: HSimulator: hybrid stochastic/deterministic simulation of biochemical reaction networks (2017)
  16. Schnoerr, David; Sanguinetti, Guido; Grima, Ramon: Approximation and inference methods for stochastic biochemical kinetics -- a tutorial review (2017)
  17. Snowden, Thomas J.; van der Graaf, Piet H.; Tindall, Marcus J.: Methods of model reduction for large-scale biological systems: a survey of current methods and trends (2017)
  18. Tuncer, Gökçe; Purutçuoğlu, Vilda: Application of impulsive deterministic simulation of biochemical networks via simulation tools (2017)
  19. Balabin, Fedor A.; Sveshnikova, Anastasia N.: Computational biology analysis of platelet signaling reveals roles of feedbacks through phospholipase C and inositol 1,4,5-trisphosphate 3-kinase in controlling amplitude and duration of calcium oscillations (2016)
  20. Eftimie, Raluca; Gillard, Joseph J.; Cantrell, Doreen A.: Mathematical models for immunology: current state of the art and future research directions (2016)

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