An Efficient Storage Model for the SBML Documents Using Object Databases

Abstract

As SBML is regarded as a de-facto standard to express the biological network data in systems biology, the amount of the SBML documents is exponentially increasing. We propose an SBML data management system (SMS) on top of an object database. Since the object database supports abundant data types like multi-valued attributes and object references, mapping from the SBML documents into the object database is straightforward. We adopt the event-based SAX parser instead of the DOM parser for dealing with huge SBML documents. Note that DOM parser suffers from excessive memory overhead for the document parsing. For high quality data, SMS supports data cleansing function by using gene ontology. Finally, SMS generates user query results in an SBML format (for data exchange) or in a visual graphs (for intuitive understanding). Real experiments show that our approach is superior to the one using conventional relational databases in the aspects of the modeling capability, storage requirements, and data quality.