Abstract
BackgroundReaction gap filling is a computational technique for proposing the addition of reactions to genome-scale metabolic models to permit those models to run correctly. Gap filling completes what are otherwise incomplete models that lack fully connected metabolic networks. The models are incomplete because they are derived from annotated genomes in which not all enzymes have been identified. Here we compare the results of applying an automated likelihood-based gap filler within the Pathway Tools software with the results of manually gap filling the same metabolic model. Both gap-filling exercises were applied to the same genome-derived qualitative metabolic reconstruction for Bifidobacterium longum subsp. longum JCM 1217, and to the same modeling conditions — anaerobic growth under four nutrients producing 53 biomass metabolites.ResultsThe solution computed by the gap-filling program GenDev contained 12 reactions, but closer examination showed that solution was not minimal; two of the twelve reactions can be removed to yield a set of ten reactions that enable model growth. The manually curated solution contained 13 reactions, eight of which were shared with the 12-reaction computed solution. Thus, GenDev achieved recall of 61.5% and precision of 66.6%. These results suggest that although computational gap fillers are populating metabolic models with significant numbers of correct reactions, automatically gap-filled metabolic models also contain significant numbers of incorrect reactions.ConclusionsOur conclusion is that manual curation of gap-filler results is needed to obtain high-accuracy models. Many of the differences between the manual and automatic solutions resulted from using expert biological knowledge to direct the choice of reactions within the curated solution, such as reactions specific to the anaerobic lifestyle of B. longum.
Highlights
Reaction gap filling is a computational technique for proposing the addition of reactions to genome-scale metabolic models to permit those models to run correctly
The MetaFlux gap filler, General development mode (GenDev), computed a minimum-cost solution to the problem of gap-filling the network: it proposed adding 12 new reactions to the Pathway/Genome Database (PGDB), which resulted in production of all 54 of the biomass metabolites via 241 reactions carrying non-zero flux
GenDev is a parsimony-based gap filler that seeks minimum-cost solutions. For this solution all reactions added by GenDev had the same cost because the taxonomic range and directionality information stored in MetaCyc for all added reactions were compatible with B. longum
Summary
Reaction gap filling is a computational technique for proposing the addition of reactions to genome-scale metabolic models to permit those models to run correctly. Because the reactions within most metabolic models are derived from the enzyme annotations assigned to genes within a sequenced genome, and because methods for genome annotation are incomplete and fail to assign functions to many genes (and in some cases assign incorrect functions), genome-derived metabolic networks usually contain multiple gaps (missing reactions). Because filling these gaps manually can take months of effort, computational gap-filling methods have been developed [1,2,3,4,5]. Its performance results will not be comparable to most gap fillers that aim to enable biomass production (by resolving all gap-filling problems we believe the authors mean enabling production of all dead-end metabolites)
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