- Waltemath D., Adams R., Bergmann F.T., Hucka M., Kolpakov F., Miller A.K., Moraru I.I., Nickerson D., Snoep J.L.,Le Novère, N.
Reproducible computational biology experiments with SED-ML - The Simulation Experiment Description Markup Language.
BMC Systems Biology 2011, 5:198
- [BMC Syst Biol]
- Dagmar Köhn, Nicolas Le Novère
SED-ML – An XML Format for the Implementation of the MIASE Guidelines.
Lecture Notes in Computer Science, Computational Methods in Systems Biology 2008
The Model Class
The Model class is used to reference the models used in the simulation experiment. SED-ML itself is independent of the model encoding underlying the models. The only requirement is that the model needs to be referenced by using an unambiguous identifier which allows for finding it, for example using a MIRIAM URI. To specify the language in which the model is encoded, a set of predefined language URNs is provided.
The SED-ML Change class allows the application of changes to the referenced models, including changes on the XML attributes, 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 that is addressable by XPath expressions, e.g. substituting a piece of XML by an updated one.
The Simulation Class
The Simulation class defines the simulation settings and the steps taken during simulation. These include the particular type of simulation and the algorithm used for the execution of the simulation; preferably an unambiguous reference to such an algorithm should be given, using a controlled vocabulary, or ontologies. One example for an ontology of simulation algorithms is the Kinetic Simulation Algorithm Ontology KiSAO. Further information encodable in the Simulation class includes the step size, simulation duration, and other simulation-type dependent information.
The Task Class
SED-ML makes use of the notion of a Task class to combine a defined model (from the Model class) and a defined simulation setting (from the Simulation class). A task always holds one reference each. To refer to a specific model and to a specific simulation, the corresponding IDs are used.
The DataGenerator Class
The raw simulation result sometimes does not correspond to the desired output of the simulation, e.g. one might want to normalise a plot before output, or apply post-processing like mean-value calculation. The DataGenerator class allows for the encoding of such post-processings which need to be applied to the simulation result before output. To define data generators, any addressable variable or parameter of any defined model (from instances of the Model class) may be referenced, and new entities might be specified using MathML definitions.
The Output Class
The Output class defines the output of the simulation, in the sense that it specifies what shall be plotted in the output. To do so, an output type is defined, e.g. 2D-plot, 3D-plot or data table, and the according axes or columns are all assigned to one of the formerly specified instances of the DataGenerator class.
If you are interested in using SED-ML, the following resources might be useful to you:
- SED-ML project home on SourceForge: SED-ML project
- Full SED-ML XML Schema definition: SED-ML Schema
- SED-ML Online Validator (developed and maintained by
Richard Adams): SED-ML Validator
BMC Systems Biology, 4:92
- SED-ML Java Library (developed and maintained by Richard Adams): jlibsedml
- SED-ED graphical editor for SED-ML (developed and maintained by Richard Adams): SED-ED
- SED-ML Library (developed and maintained by Frank Bergmann): libsedml
- SED-ML Processing Service (developed by Andrew Miller): SProS
Please let us know if you want your tool listed here.
The following languages have to our knowledge so far
been used with SED-ML. To specify the language of a model
in the Model class, please use the according URN given.
The general scheme for a language URN in SED-ML is
name and version.
|NeuroML Version 1.8.1 Level 1||
|NeuroML Version 1.8.1 Level 2||
|SBML Level 1 Version 1||
|SBML Level 1 Version 2||
|SBML Level 2 Version 1||
|SBML Level 2 Version 2||
|SBML Level 2 Version 3||
|SBML Level 2 Version 4||
|SBML Level 3 Version 1||
As described in the SED-ML specification some modeling languages use predefined symbols or implicit variables. In order to
allow these variables to be adressed the SED-ML language
introduced symbol URNs. The general scheme for a symbol
URN in SED-ML is
||Time in SBML is an intrinsic model variable that is addressable in model equations via a csymbol 'time'.|