Integrating systems biology models and biomedical ontologies

Area of Science:

• Systems biology
• Bioinformatics
• Computational biology

Background:

• Systems biology requires integrated data across domains and granularities.
• Biomedical ontologies facilitate data integration and annotation of biosimulation models.

Purpose of the Study:

• To develop a framework for integrating in silico systems biology with in vivo biology using biomedical ontologies.
• To create software for converting Systems Biology Markup Language (SBML) models to OWL.

Main Methods:

• Developed SBML Harvester software to automatically convert annotated SBML models to OWL.
• Applied the software to biosimulation models in the BioModels Database.
• Utilized the resulting knowledge base for complex biological queries and model verification.

Main Results:

• Established a framework integrating in silico and in vivo biological representations.
• Created a knowledge base from annotated SBML models enabling cross-granularity queries.
• Demonstrated model verification and qualitative semantic expression for biosimulation models.

Conclusions:

• An information flow between biomedical ontologies and biosimulation models was established.
• Integration enables model verification and expressive queries.
• Bi-directional flow facilitates large-scale analyses spanning molecular to organismal levels.