Therefore, we created a workflow (Fig. .1) integrating metabolic reaction knowledge from three freely available pathway databases, suitable for fast queries on directed reaction paths between metabolites of interest in a scalable and flexible open-source graph database, including provenance. Harmonization across the information within the models (and databases used to annotate metabolites and proteins) is executed by semantic web technologies (RDF), after which data retrieval is performed (SPARQL). Data cleanup is required before storing the directed metabolic conversion knowledge in a graph database through Neo4j. Metabolic biomarkers are connected to relevant molecular reactions by the shortest path algorithm (Cypher), after which data visualization takes place in Cytoscape on the retrieved (sub)network. The cyNeo4j app was extended to query the shortest path for biomarkers directly from within Cytoscape. The workflow was tested using three independent datasets measuring biomarkers for healthy aging.