This article is part of the supplement: Proceedings of the Bio-Ontologies Special Interest Group Meeting 2009: Knowledge in Biology
Proceedings
Annotation of SBML models through rule-based semantic integration
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* Corresponding authors: Allyson L Lister a.l.lister@newcastle.ac.uk - Anil Wipat anil.wipat@newcastle.ac.uk
1 Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, UK
2 School of Computing Science, Newcastle University, NE1 7RU, UK
Journal of Biomedical Semantics 2010, 1(Suppl 1):S3 doi:10.1186/2041-1480-1-S1-S3
Published: 22 June 2010Abstract
Background
The creation of accurate quantitative Systems Biology Markup Language (SBML) models is a time-intensive, manual process often complicated by the many data sources and formats required to annotate even a small and well-scoped model. Ideally, the retrieval and integration of biological knowledge for model annotation should be performed quickly, precisely, and with a minimum of manual effort.
Results
Here we present rule-based mediation, a method of semantic data integration applied to systems biology model annotation. The heterogeneous data sources are first syntactically converted into ontologies, which are then aligned to a small domain ontology by applying a rule base. We demonstrate proof-of-principle of this application of rule-based mediation using off-the-shelf semantic web technology through two use cases for SBML model annotation. Existing tools and technology provide a framework around which the system is built, reducing development time and increasing usability.
Conclusions
Integrating resources in this way accommodates multiple formats with different semantics, and provides richly-modelled biological knowledge suitable for annotation of SBML models. This initial work establishes the feasibility of rule-based mediation as part of an automated SBML model annotation system.
Availability
Detailed information on the project files as well as further information on and comparisons with similar projects is available from the project page at http://cisban-silico.cs.ncl.ac.uk/RBM/ webcite.