SED-ML

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Filename extension
.xml
Initial release25 March 2011; 14 years ago (2011-03-25)
Latest release
Level 1 Version 2, Release 1
2 December 2013; 12 years ago (2013-12-02)
Type of formatMarkup language
Simulation Experiment Description Markup Language
SED-ML logo
Filename extension
.xml
Initial release25 March 2011; 14 years ago (2011-03-25)
Latest release
Level 1 Version 2, Release 1
2 December 2013; 12 years ago (2013-12-02)
Type of formatMarkup language
Extended fromXML
Open format?Yes
Websitesed-ml.org

The Simulation Experiment Description Markup Language (SED-ML) is a representation format, based on XML, for the encoding and exchange of simulation descriptions on computational models of biological systems.[1] It is a free and open community development project.

SED-ML Level 1 Version 1,[2] the first version of SED-ML, enables descriptions of time course simulation experiments.

The SED-ML format is built of five major blocks:

  • The Model entity is used to reference the models used in the simulation experiment and to define pre-processing procedures on these models before simulation. Models must be in standard representation formats (e.g., SBML, CellML, NeuroML). Examples for pre-processing are, e.g., changing the value of an observable, computing the change of a value using mathematics, or general changes on any XML element of the model representation.
  • The Simulation entity contains all information about the simulation settings and the steps taken during simulation, e.g., the particular type of simulation and the algorithm used for the execution of the simulation. The simulation algorithm is specified with a Kinetic Simulation Algorithm Ontology term.
  • The Task entity applies one of the defined simulations with one of the referenced models at a time.
  • The DataGenerator entity encodes post-processing procedures which need to be applied to the simulation result before output, e.g., normalisation of data.
  • The Output entity specifies the simulation output, e.g., the particular plots to be shown.

More information on the SED-ML structure is available from the SED-ML home page[1] and the reference publication.[3]

History

SED-ML Community

References

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