Discovering life’s directed metabolic (sub)paths to interpret biochemical markers using the DSMN

Metabolomics for phenotypic analysis commonly reveals only a small set of biochemical markers, often containing overlapping metabolites for individual phenotypes; differentiation requires understanding the underlying causes for the metabolic changes. However, combining biomarker data with knowledge on metabolic conversions from pathway databases is still a time-consuming process due to their scattered availability. Here we integrate several resources through ontological linking into one queryable database - the Directed Small Molecules Network (DSMN) - to generate (sub)networks of explainable biochemical relationships; this approach was tested on biomarkers for healthy aging.