A short introduction to CellML. CellML is an XML-based language designed to facilitate the exchange of biological models across the World Wide Web. Processing applications are able to appropriately render models based on the definition of model structure given in a CellML document, and run simulations based on the definition of the underlying mathematics. CellML is designed to be a general framework upon which a wide variety of models may be built. The basic constituents and structure are simple, providing a common basis for describing models and facilitating the creation of complex models from simpler ones by combining models and/or adding detail to existing models.CellML models are represented as a collection of discrete components linked by connections to form a network. A component is a functional unit that may correspond to a physical compartment, a collection of entities engaged in similar tasks, or a convenient modelling abstraction. Components may contain variables, mathematical relationships that specify the interactions between those variables, and metadata. Variables may be local to a component, or made visible to other components via interface attributes. All interactions between variables within a component are described using MathML content markup. The interface attributes describe the external view of the component, specifying those variables visible to other components. A connection is a directed mapping from externally visible variables in one component to those of another. Every variable has a set of units associated with it, making it possible to connect together components with variables defined using different units. CellML offers additional facilities, such as metadata, for adding context information to a model, and component grouping. These assist in the creation and maintenance of models but do not alter the mathematics of the model. All models described using CellML can be reduced to the canonical form: a set of connected components. (Source: http://freecode.com/)

References in zbMATH (referenced in 27 articles , 1 standard article )

Showing results 1 to 20 of 27.
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  1. Jæger, Karoline Horgmo; Tveito, Aslak: Derivation of a cell-based mathematical model of excitable cells (2021)
  2. Cooper et al.: Chaste: Cancer, Heart and Soft Tissue Environment (2020) not zbMATH
  3. 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
  4. Takeshi Abe; Yoshiyuki Asai: Flint: a simulator for biological and physiological models in ordinary and stochastic differential equations (2020) not zbMATH
  5. Green, Kevin R.; Bohn, Tanner A.; Spiteri, Raymond J.: Direct function evaluation versus lookup tables: when to use which? (2019)
  6. Green, Kevin R.; Spiteri, Raymond J.: Gating-enhanced IMEX splitting methods for cardiac monodomain simulation (2019)
  7. Cervi, Jessica; Spiteri, Raymond J.: High-order operator splitting for the bidomain and monodomain models (2018)
  8. Edwin Tye, Tom Finnie, Ian Hall, Steve Leach: PyGOM - A Python Package for Simplifying Modelling with Systems of Ordinary Differential Equations (2018) arXiv
  9. Mirshekari, Elham; Spiteri, Raymond J.: Extending BACOLI to solve the monodomain model (2016)
  10. Spiteri, Raymond J.; Torabi Ziaratgahi, Saeed: Operator splitting for the bidomain model revisited (2016)
  11. De Matteis, Giovanni; Graudenzi, Alex; Antoniotti, Marco: A review of spatial computational models for multi-cellular systems, with regard to intestinal crypts and colorectal cancer development (2013)
  12. Francis, Febe; García, Míriam R.; Middleton, Richard H.: A single compartment model of pacemaking in dissasociated substantia nigra neurons. Stability and energy analysis (2013)
  13. Heidlauf, Thomas; Röhrle, Oliver: Modeling the chemoelectromechanical behavior of skeletal muscle using the parallel open-source software library OpenCMISS (2013)
  14. Øyehaug, Leiv; Østby, Ivar; Lloyd, Catherine M.; Omholt, Stig W.; Einevoll, Gaute T.: Dependence of spontaneous neuronal firing and depolarisation block on astroglial membrane transport mechanisms (2012)
  15. Kannon, Takayuki; Inagaki, Keiichiro; Kamiji, Nilton L.; Makimura, Kouji; Usui, Shiro: PLATO: data-oriented approach to collaborative large-scale brain system modeling (2011) ioport
  16. Miller, Andrew K.; Yu, Tommy; Britten, Randall; Cooling, Mike T.; Lawson, James R.; Cowan, Dougal; Garny, Alan; Halstead, Matt D. B.; Hunter, Peter J.; Nickerson, David P.; Nunns, Geoff; Wimalaratne, Sarala M.; Nielsen, Poul M. F.: Revision history aware repositories of computational models of biological systems (2011) ioport
  17. Tveito, Aslak; Lines, Glenn; Artebrant, Robert; Skavhaug, Ola; Maleckar, Mary M.: Existence of excitation waves for a collection of cardiomyocytes electrically coupled to fibroblasts (2011)
  18. Waltemath, Dagmar; Henkel, Ron; Meyer, Holger; Heuer, Andreas: Das sombi-framework zum ermitteln geeigneter suchfunktionen für biologische modelldatenbasen (2011) ioport
  19. Cooper, Jonathan; Whiteley, Jonathan P.; Gavaghan, David J.: A posteriori error analysis for the use of lookup tables in cardiac electrophysiology simulations (2010)
  20. Bernabeu, Miguel O.; Bordas, Rafel; Pathmanathan, Pras; Pitt-Francis, Joe; Cooper, Jonathan; Garny, Alan; Gavaghan, David J.; Rodriguez, Blanca; Southern, James A.; Whiteley, Jonathan P.: Chaste: incorporating a novel multi-scale spatial and temporal algorithm into a large-scale open source library (2009)

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