Quantifying cumulative phenotypic and genomic evidence for procedural generation of metabolic network reconstructions
Fig 1
The CANYUNs pipeline integrates biochemical, phenotypic, and genomic data to quantitatively identify reactions that are likely catalyzed by an organism.
(A) Genomic annotation data and phenotypic growth data for a specific organism are used to influence the flux distribution through a curated universal biochemical network to build an organism-specific metabolic network model. Parallel growth simulations using Data Guided Flux Balance Analysis for each known experimental growth condition allows for a model building process that is not influenced by the order in which growth conditions are integrated. This process allows for the explicit quantification of reaction Certainty Values, determined by the ratio of times a reaction carries flux across all of the condition-specific solutions to the total number of conditions. (B) The universal biochemical network used in this study consists of reactions from the CarveMe dataset as well as novel reactions added from the manually curated E. coli metabolic network, iML1515. (C) The phenotypic data used in this study includes Biolog minimal media growth data from ~275 different conditions. (D) The sequence-to-reaction dataset used to calculate reaction annotation evidence consists of over 4,000 reactions with 1 to 800 sequences associated with each reaction. (E) The distribution of reaction bitscores for E. coli K-12 shows that there are reactions in the universal network with high evidence that are not included in iML1515. There are also many reactions with low evidence that are not included in iML1515, as expected. The annotation evidence generated for E. coli K-12 shows that there are 1,460 reactions in the universal biochemical network that have no genetic evidence associated with them (left of the dashed orange line), 260 of these reactions are in iML1515 and 1,200 of them are not.