Graph databases can be successfully applied in Systems Biology for managing extensive and complex heterogeneous information. Ultimately, graphs are a natural way of representing biological networks. A graph database like Neo4j can thus often provide a better response time and it enables efficient storage, processing and querying of biological networks. Here we present STON (SBGN TO Neo4j), a Java-based framework to import and translate metabolic, signalling and gene regulatory pathways presented in SBGN Process Description and SBGN Activity Flow languages to a graph-oriented format compatible with Neo4j. Exploiting the power of a graph database opens new opportunities for combining different layers of granularity and for identifying functional sub-modules in the network. Further extensions are planned and will allow merging pathways into larger networks while taking into account possible overlapping areas of the network. The framework is freely available on SourceForge: http://sourceforge.net/projects/ston/.