STON.pdf
Vasundra Touré1, Alexander Mazein2, Dagmar Waltemath1, Irina Balaur2, Ron Henkel1, Mansoor Saqi2, Johann Pellet2 and Charles Auffray2
1Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany.
2European Institute for Systems Biology and Medicine (EISBM), Centre National de la Recherche Scientifique (CNRS), Campus Charles Mérieux - Université de Lyon - 50 Avenue Tony Garnier, 69007 Lyon, France; IMI-eTRIKS consortium.
Abstract
Background: Graph databases can be successfully applied in Systems Biology and in Systems Medicine for managing extensive and complex information. Ultimately, graphs are a natural way of representing biological networks. The use of graph databases enables efficient storing and processing of biological relationships, and it can lead to a better response time when querying the data.
Objectives: We would like to use graph databases structure to store and explore biological pathways and networks.
Method: Translation rules have been determined to represent biological reaction networks in a graph model, that is to say as nodes, relationships and properties. The reaction networks are provided in the graphical standard Systems Biology Graphical Notation (SBGN). The graph model is stored in a Neo4j database.
Results: We present the Java-based framework STON (SBGN TO Neo4j) to import and translate metabolic, signalling and gene regulatory pathways. On the poster, we show examples of networks representing parts of the Asthma Map, the iNOS pathway (a SBGN use case network).
Conclusion: STON exploits the power of a graph database for the search in complex biological pathways. Importing biological pathways in a graph database enables:
1) identification of functional sub-modules and comparing different networks in order to discover common patterns.
2) merging multiple diagrams for creating large comprehensive networks for empowering systems medicine approaches.
Availability: The STON framework is available here: http://sourceforge.net/projects/ston/.
