The DSMN addresses several issues known in biochemical graph modeling, by using a bipartite model including reaction directionality and excluding side metabolites. Defining which metabolites should be excluded from a graph model is difficult, especially if the data used to build the model does not include a clear mechanism to differentiate between "main reaction participants" and "side reaction participants". The DSMN was built using the harmonized semantic web format (RDF) from the native PathVisio data format (GPML). Figure .6 gives an example of a pathway drawing in GPML, where a single substrate is converted to a single product with one enzyme catalyzing the reaction, using ATP to start the reaction and releasing ADP as a side product (this reaction could have a reaction type "phosphorylation"). The reaction formula reads '1 Substrate + 1 ATP → (Enzyme) → 1 Product + 1 ADP'. In order to model this information in a machine-readable manner, no difference between side and main metabolites was made in the RDF structure, and therefore four possible combinations of source → target could be retrieved. From these combinations, only two were biologically correct (Substrate → Product and ATP → ADP), out of which only one was interesting to query for the shortest path (Substrate → Product). The presence of recurring side metabolites and metabolic reactions created so-called "hub nodes" in a network, which could lead to shortcuts for the shortest path calculation since all these conversions would be linked to each other through one ATP to ADP conversion. By using a different label on the edge for 'SideInteractions', the shortest path algorithm could skip these reactions leading to a faster query time. To interpret the visualizations from a shortest path calculation the following should be taken into account. First, the created DSMN is unweighted; therefore the same weight is assigned to each edge in the graph. Other paths could be (more) relevant, depending on enzyme kinetics. However, this data needs to be very accurate when used in modeling approaches, and protein kinetics are substantially different between model species (or cell lines) and humans . Furthermore, these kinetics are tissuedependent, which is difficult to model within pathway databases and directed graphs; including transport reactions would create paths pointing to the same compound, a known issue for shortest path algorithms . Gene regulation and protein-protein interactions are not part of the DSMN; when these dynamics are added (e.g. through Michaelis-Menten modeling or mass-actions kinetics), the resulting network will be far more complex due to the highly diverse set of flow patterns, complicating interpretation . To avoid shortcuts while calculating the shortest path, metabolites labeled as "side metabolites"-even though relevant for electron and energy exchangeare disregarded, and therefore also not part of the visualizations.