Open Access Highly Accessed Methodology article

Integrating systems biology models and biomedical ontologies

Robert Hoehndorf1*, Michel Dumontier23, John H Gennari4, Sarala Wimalaratne5, Bernard de Bono5, Daniel L Cook67 and Georgios V Gkoutos1

Author affiliations

1 Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK

2 Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, K1S 5B6, Canada

3 School of Computer Science, Carleton University, 1125 Colonel By Drive, Ottawa, K1S 5B6, Canada

4 Biomedical & Health Informatics, Department of Medical Education and Biomedical Informatics, University of Washington, 1959 NE Pacific Street, Box 357420, Seattle, Washington 98195, USA

5 European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK

6 Department of Physiology & Biophysics, University of Washington, 1705 NE Pacific Street, Box 357290, Seattle, Washington 98195, USA

7 Department of Biological Structure, University of Washington, 1959 NE Pacific Street, Box 357420, Seattle, Washington 98195, USA

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Citation and License

BMC Systems Biology 2011, 5:124  doi:10.1186/1752-0509-5-124

Published: 11 August 2011

Abstract

Background

Systems biology is an approach to biology that emphasizes the structure and dynamic behavior of biological systems and the interactions that occur within them. To succeed, systems biology crucially depends on the accessibility and integration of data across domains and levels of granularity. Biomedical ontologies were developed to facilitate such an integration of data and are often used to annotate biosimulation models in systems biology.

Results

We provide a framework to integrate representations of in silico systems biology with those of in vivo biology as described by biomedical ontologies and demonstrate this framework using the Systems Biology Markup Language. We developed the SBML Harvester software that automatically converts annotated SBML models into OWL and we apply our software to those biosimulation models that are contained in the BioModels Database. We utilize the resulting knowledge base for complex biological queries that can bridge levels of granularity, verify models based on the biological phenomenon they represent and provide a means to establish a basic qualitative layer on which to express the semantics of biosimulation models.

Conclusions

We establish an information flow between biomedical ontologies and biosimulation models and we demonstrate that the integration of annotated biosimulation models and biomedical ontologies enables the verification of models as well as expressive queries. Establishing a bi-directional information flow between systems biology and biomedical ontologies has the potential to enable large-scale analyses of biological systems that span levels of granularity from molecules to organisms.