Phenotype microarray (PM) simulation and analysis.

(A) Venn diagram showing the overlap of unique metabolites from the PM dataset and the extracellular metabolites from the GENREs. (B) The toxin concentration distribution for the 65 overlapping growth conditions from panel (A). (C) Simulated reaction flux through each in silico PM condition (n = 65). The flux data was min-max normalized and reactions with flux variance across all conditions 420 ng/uL) as in Lei, XH and Bochner, BR (2013). (TIFF)</p

Metabolic differences between toxin states are driven by strain.

(A) Summary table of the RIPTiDe contextualized models including the strain, toxin production level, and number of genes, reactions, and metabolites. (B) Normalized, absolute flux values for reactions indicated by Random Forest classifier as important for distinguishing between toxin levels. C. difficile strains 630 and R20291 are shown by light and dark purple respectively. Toxin transcript levels are shown by light (low) and dark (high) teal. Starred reactions are contextualized in panel (C). (C) Map of reactions in the metabolic model. Reactions identified by Random Forest analysis in panel (B) are starred. Arg: Arginine, Orn: Ornithine, Pro: Proline, Suc: Sucrose, UDP-Glc: UDP-Glucose, Glc1P: Glucose-1-phospate, ManNAc: N-acetyl-D-mannosamine, Guo: Guanosine, dGuo: Deoxyguanosine, G: Guanine.</p

Shadow prices of metabolites that decrease flux through reactions from Random Forest.

For each objective function (OF) listed in Fig 3A, the metabolites categorized as decreasing and with a shadow price (TIFF)</p

Comparison of metabolic flux through reactions in the Reference and Target state.

Comparison of metabolic flux through reactions in the Reference and Target state.</p

MTA knockout flux data.

Flux data for each reaction knockout (columns) with the first two columns showing the flux data for the Target and Control (Reference). (CSV)</p

Shadow pricing data.

Metabolite shadow prices with the first five columns set as simulation descriptors: condition (RIPTiDe model), strain (CD630 or CDR20291), toxin (low or high), OF (reaction ID for objective function), and OF_name (name of OF). (CSV)</p

Flux through arginine and ornithine reactions is sensitive to intracellular metabolite concentrations.

(A) Summary of the shadow pricing analysis with the top 20 reactions from the Random Forest classifier set as the objective function. The number in the "models" column (blue) corresponds to the fraction of contextualized models that were able to carry flux with the indicated reaction set as the objective function (OF). The values in the orange columns indicate the following: Increase: the number of metabolites for which an increased level results in increased flux through the OF (median shadow price > 0, range 2). For example, in the first row of panel (A), the OF was able to carry flux in all of the models, 294 metabolites increased flux through the OF in all of the models, 1 metabolite limited flux through the OF in all of the models, and 5 metabolites had different effects on flux through the OF across all of the models. (B) Shadow prices for limiting metabolites in arginine/ornithine and aspartate metabolism reactions. The metabolites categorized as sensitive in panel (A) for these OFs and with a shadow price < -5 are shown. Increasing negative values indicates increasing reaction flux sensitivity to the metabolite.</p

Random Forest validation metrics.

(A) Visualization of the random stratified group k-fold splits used for cross validation of the Random Forest classifier. (B-C) K-fold cross validation (k = 5) of the Random Forest classifier testing ROC (B) and accuracy (C), with an average accuracy of 95% in cross validation. (D) Confusion matrix for model predictions with train and test sets selected in a 75–25 ratio using random stratified group splits. The model trained on this set had a 97% accuracy. (E) The top 20 features for model predictions by Gini score. (TIFF)</p

RIPTiDe model flux sampling data.

Down-sampled flux data (n = 100 samples per RIPTiDe model), with the first three columns set as sample descriptors (condition (RIPTiDe model), strain, and toxin category). (CSV)</p

Modified MTA identifies key reaction knockouts and pathways for transformation from a high to low toxin state.

The mMTA algorithm runs a reaction KO simulation to optimize changes in reaction flux that transform the model from the reference metabolic state (high toxin) to the target metabolic state (low toxin). The reaction knockouts with the highest transformation scores are shown on the y-axis. The reactions whose flux changed under these KO conditions are shown on the x-axis. Successfully changed reactions are defined as those whose flux changed from the reference in the desired direction by a minimum threshold of significance (successful: dark blue; unsuccessful: light grey). The metabolic pathways for these reactions are shown beneath the clustering dendrogram at the top.</p