This App predicts auxotrophy based on the number of gaps that occur in the peripheral metabolic pathways to each biomass component. A genome is considered auxotrophic for a compound if the number of gene-associated critical reactions (required for growth on minimal but not complete media and whose fluxes are related to yield) is below a compound-specific threshold or if the number of gapfilled critical reactions exceeds compound-specific thresholds.

To use the Predict Genome Auxotrophies App with a genome uploaded into KBase, the genome must first be annotated or re-annotated using the RAST functional ontology (Annotate Microbial Assembly or Annotate Microbial Genome). This is necessary because the SEED functional annotations generated by RAST [2] are linked directly to the biochemical reactions in the ModelSEED biochemistry database, which is used by KBase for metabolic modeling.

Once a genome has been annotated, the Predict Genome Auxotrophies App can be run. A draft metabolic model is created (but not stored) and flux balance analysis is run under two conditions: 1) on complete media containing all elements of the biomass equation in the draft model, and 2) on a minimal media which lacks these elements. Comparisons of these FBA results are performed (but not stored) to determine reactions that are essential for growth in minimal media but not complete media. If the number of gene-associated critical reactions is below a compound-specific threshold or if the number of gapfilled critical reactions exceeds a compound-specific threshold, the genome is predicted to be auxotrophic for that compound.

Note: While the Predict Genome Auxotrophies App will run successfully on genomes that have other annotations (e.g. Prokka), this app depends on RAST-based modeling tools and the thresholds for auxotrophy predictions were developed using RAST annotations, so results based on other annotations are not recommended and should be used with caution.

For additional information about metabolic modeling, visit the Metabolic Modeling in KBase FAQ.

Team members who developed & deployed algorithm in KBase: Christopher Henry, Pamela Weisenhorn, Janaka Edirisinghe, and Jose Faria. For questions, please contact us.