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| | | | | | | | | | | | | Issues and Goals for the Coming Year | |
Author:
Autumn A Cuellar (Bioengineering Institute, University of Auckland)
Contributors:
David Bullivant (Bioengineering Institute, University of Auckland)
Michael Dunstan (Bioengineering Institute, University of Auckland)
Matt Halstead (Bioengineering Institute, University of Auckland)
Peter Hunter (Bioengineering Institute, University of Auckland)
Catherine Lloyd (Bioengineering Institute, University of Auckland)
Mike Lovell-Smith (Bioengineering Institute, University of Auckland)
Poul Nielsen (Bioengineering Institute, University of Auckland)
David Nickerson (Bioengineering Institute, University of Auckland)
- Currently, the availability of software that allows one to do anything useful with CellML is limited and limiting. Matt and Michael are here to help.
- Heaps of models are currently available on the cellml.org website, but it is difficult to find the one you want.
- Metadata: specification is almost complete. Is there any metadata information that we're missing?
- Ontologies: need to be worked out. What's the best way to incorporate them into CellML so that they are most useful? How do we ensure that they are extensible, i.e. anyone can add to an existing ontology or create their own? What limits need to be placed on the creation and use of ontologies? What specific information needs to be defined in an ontology so as to be most useful to software developers? How about modellers?
- Solidify the Metadata Specification.
- Set up a searchable database of CellML models.
- Begin the CellML 2.0 Specification: incorporate Ontologies and typing mechanisms.
- Complete documentation of the CellML API and implementations of the API.
- Create useful tools for CellML including authoring tools.
An ontology is essentially a dictionary of terms. By defining an ontology, terms are related to one another to allow a processor the ability to infer. We expect ontologies to be a powerful typing mechanism in CellML. Several uses could be made of ontologies, including assigning graph or glyph information to a component, providing the capabilities of stronger validation of models, and associating a model to other existing models. For instance, a component representing the "L-type calcium channel" could be referred to with an ontology as a type of "channel". The ontology's "channel" class has an associated gif image that software can retrieve to show the user. It also tells the software that the channel is physically located in the "membrane", so to type another component as "membrane", the CellML model should not contain any information that contradicts this known relationship (for example the "L-type calcium channel" component should not "contain" the component typed as "membrane"). Furthermore, the ontology can allude to other CellML models or templates that might be useful to a modeller.
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