COPASI

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 28 articles )

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  1. Marchetti, Luca; Lombardo, Rosario; Priami, Corrado: HSimulator: hybrid stochastic/deterministic simulation of biochemical reaction networks (2017)
  2. 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)
  3. Tuncer, Gökçe; Purutçuoğlu, Vilda: Application of impulsive deterministic simulation of biochemical networks via simulation tools (2017)
  4. 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)
  5. Eftimie, Raluca; Gillard, Joseph J.; Cantrell, Doreen A.: Mathematical models for immunology: current state of the art and future research directions (2016)
  6. Kyurkchiev, Nikolay; Markov, Svetoslav: On the numerical solution of the general kinetic “$K$-angle” reaction system (2016)
  7. Marchetti, Luca; Priami, Corrado; Thanh, Vo Hong: HRSSA - efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks (2016)
  8. Gábor, Attila; Hangos, Katalin M.; Banga, Julio R.; Szederkényi, Gábor: Reaction network realizations of rational biochemical systems and their structural properties (2015)
  9. Kircheis, Robert: Structure exploiting parameter estimation and optimum experimental design methods and applications in microbial enhanced oil recovery (2015)
  10. Samal, Satya Swarup; Grigoriev, Dima; Fröhlich, Holger; Weber, Andreas; Radulescu, Ovidiu: A geometric method for model reduction of biochemical networks with polynomial rate functions (2015)
  11. Zimmer, Christoph: Reconstructing the hidden states in time course data of stochastic models (2015)
  12. Banks, C.J.; Stark, I.: A logic of behaviour in context (2014)
  13. Kasabov, Nikola (ed.): Springer handbook of bio-/neuro-informatics (2014)
  14. Gratie, Diana-Elena; Iancu, Bogdan; Petre, Ion: ODE analysis of biological systems (2013)
  15. Li, Yongfeng; Wang, Minli; Carra, Claudio; Cucinotta, Francis A.: Modularized smad-regulated TGF$\beta $ signaling pathway (2012)
  16. Rodriguez-Fernandez, Maria; Banga, Julio R.; Doyle, Francis J. III: Novel global sensitivity analysis methodology accounting for the crucial role of the distribution of input parameters: application to systems biology models (2012)
  17. Chen, Ming; Hariharaputran, Sridhar; Hofestädt, Ralf; Kormeier, Benjamin: Petri net models for the semi-automatic construction of large scale biological networks (2011)
  18. Heiner, Monika; Gilbert, David: How Petri nets might enhance your systems biology toolkit (2011)
  19. Hinze, Thomas; Schumann, Mathias; Bodenstein, Christian; Heiland, Ines; Schuster, Stefan: Biochemical frequency control by synchronisation of coupled repressilators: an in silico study of modules for circadian clock systems (2011) ioport
  20. Koh, Geoffrey; Hsu, David; Thiagarajan, P.S.: Component-based construction of bio-pathway models: the parameter estimation problem (2011)

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