Annotation of SBML models through rule-based semantic integration

Allyson L Lister,Phillip Lord,Anil Wipat,Matthew Pocock

doi:10.1186/2041-1480-1-s1-s3

Abstract

BackgroundThe 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.ResultsHere 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.ConclusionsIntegrating 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.AvailabilityDetailed 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/.

Highlights

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 wellscoped model
The data from the syntactic ontologies is mapped to the core ontology, which stores the data in a semantically-homogeneous way
While other ontology languages such as the Open Biomedical Ontologies (OBO) [23] are widely used in the life sciences, they do not provide the same level of support for automated semantic reasoning as OWL constrained by Description Logics (OWL-DL) [24]

Summary

Introduction

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 wellscoped model. Annotation of systems biology models A quantitative model of molecular systems describes the dynamics of the interactions between biological entities involved. Such modelling is central to systems biology. The creation of systems biology models, such as those written in Systems Biology Markup Language (SBML) [1] or CellML [2], is a primarily manual process. Making use of the many data sources and formats relevant to model development is time-consuming for modellers. While a small number of core databases can be used to retrieve a large amount of biological information relevant to the modeller, accessing the “long tail” of information stored in other resources is where manual processes become difficult

Results

Discussion

Conclusion