Harnessing the Landscape of Cultivable Bacterial Growth for Predicting New Organism-Growth Media Pairings

Microbial media represent a landscape of practical knowledge into the cultivable universe. Here, we build a map of this landscape by parsing natural-language media recipes into a Known Media Database (KOMODO), which includes >18,000 strain-media combinations, >3300 media variants, and compound concentrations (the entire collection of the Leibniz Institute -- DSMZ). KOMODO reveals that although media are usually tuned for individual strains using biologically common salts, trace metals and vitamins/cofactors are the most differentiating components between defined media of strains within a genus. We leverage KOMODO to predict new organism-media pairings using a transitivity property (74% growth in new in vitro experiments), and a phylogeny-based collaborative filtering tool (83% growth in new in vitro experiments, and stronger growth on predicted well- vs. poorly-scored media (p=1.6e-3, ranksum)). These resources are integrated into a web-based platform that predicts media given an organism’s 16S rDNA sequence, facilitating future cultivation efforts.