Table 1.
An overview of the Context-specific reconstruction methods studied here.
Fig 1.
Distribution of relative error for prediction of growth rates for all algorithms using both general and cell-specific medium (designated by the superscript c). Each box-plot shows the distribution of error across all cell lines in NCI-60 panel. Only algorithms capable of predicting non-zero growth rates were depicted. Relative error was calculated according to the (Eq 2).
Fig 2.
Uptake/secretion rates prediction.
Spearman correlation between measured and predicted uptake/secretion flux rates of metabolites for (A) iMATc, (B) GIMMEc, (C) pFBAc, (D) TRFBAc, (E) PRIME, and (F) PRIMEc. Represented p-values were adjusted for False discovery rate (α = 0.05). Only methods with significant predictions are shown.
Fig 3.
Heatmap of significant Spearman correlations between simulated and experimental drug response data. The Spearman coefficients for each drug have been shown on the figure. Superscripts indicate drug response data taken from (1) Holbeck et al [38], (2) Garnett et al [39] and (3) Yang et al [40]. Only methods with significant drug responses are shown.
Fig 4.
Prediction of general cancer essential genes.
(A) Heatmap of enrichment p-values for predicted cell-line specific essential genes. The numbers indicate -log10 enrichment p-values. GEMs with insignificant p-values are shown in white. (B) Rank scores of the algorithms based on their significance and the number of GEMs with significant enrichment (as described in S2 Text).
Fig 5.
Prediction of oncogenes (OG), tumor suppressors (TS) and loss of function (LOF) mutations.
Mean enrichment of predicted (A) OGs and (B) TS and LOFs with experimental data. The error bars show the standard deviation across GEMs generated with general and cell-specific media. Hypergeometric p-values are shown above each figure. Model fraction represents the fraction of generated GEMs with significant p-values (<0.05). Only methods with significant predictions are shown.
Fig 6.
Network connectivity of generated GEMs.
The fast consistency evaluation method [16] was used to identify the fraction of blocked reactions in the GEMs reconstructed by each method. The presence of blocked reactions were assessed in both constrained and unconstrained states. Data shown as mean fraction of existing blocked reaction across all generated GEMs, and error bars represent the standard deviation.
Fig 7.
Similarity levels of generated GEMs between different tumors.
Average Jaccard similarity index computed for GEMs built by (A) CORDA, (B) FASTCORE, (C) FASTCORMICS, (D) GIMME, (E) GIMMEc, (F) INIT, (G) iMAT, and (H) mCADRE. Each square represents the average pairwise Jaccard value for each cancer type in the NCI-60 panel.
Table 2.
Cross-validation test results for the context-specific algorithms under study.
Fig 8.
Normalized growth prediction of GEMs generated using data from repeated 5-fold cross-validation.
(A) CORDA, (B) FASTCORE, (C) FASTCORMICS, (D) GIMME, (E) mCADRE, (F) PRIME, (G) TRFBA, (H) iMAT. Only algorithms capable of predicting growth are shown. For each algorithm, “model count” represents the GEMs generated by incomplete expression data or core reactions set in the input. For a better comparison, growth rates were normalized to the maximum value.
Fig 9.
Normalized growth prediction of GEMs generated using noisy expression data.
(A) CORDA, (B) FASTCORMICS, (C) GIMME, (D) PRIME, (E) TRFBA, and (F) iMAT. Only GEMs capable of predicting growth are shown. The x-axis shows the spearman correlation coefficient between each set of noisy data and original expression profile ranging from 1 (original) to R < 0.004 (random). For a better comparison, growth rates were normalized to the maximum value.
Fig 10.
Similarity levels of GEMs generated with different sets of noisy expression data (A) CORDA (B) FASTCORE (C) FASTCORMICS (D) GIMME (E) iMAT (F) mCADRE (G) INIT.
Fig 11.
Benchmark performance scores for algorithms under study.
Hierarchical clustering (Euclidean distance) of the scores each method received over different benchmarks. Three main clusters were identified: 1- GIMME, CORDA and mCADRE with an overall weak to moderate performance; 2- PRIME, TRFBA and pFBAc with strong performance in comparison tests, and 3- FASTCORE, INIT, iMAT and FASTCORMICS with strong performance in consistency tests. Numbers in column correspond to comparison (blue color) or consistency (red color) benchmarks: 1-growth rate, 2- metabolite uptake/secretion rates, 3- drug response, 4- essential genes, 5- enrichment of OG/TS/LOFs, 6- fraction of blocked reactions, 7- resolution power, 8- robustness to missing data, and 9- robustness to noise. Colorbar indicates normalized performance scores.
Fig 12.
TRFBA-CORE employs the stepwise version of TRFBA to identify a set of growth-associated reactions, build cell-specific models using modified FASTCORMICS, and generate tuned cell-specific GEMs.
Table 3.
Spearman correlation coefficients between predicted and measured growth rates for Ccorr and Copt.
Fig 13.
Hierarchical clustering of TRFBA-CORE performance scores.
Hierarchical clustering (Euclidean distance) of the scores TRFBA-CORE received in different benchmarks. Despite being a GEM extraction approach, TRFBA-CORE was clustered with algorithms that do not trim the input model. TRFBA-CORE scores were generally higher in comparison benchmarks. Numbers in column correspond to comparison (blue color) or consistency (red color) benchmarks: 1-growth rate, 2- metabolite uptake/secretion rates, 3- drug response, 4- essential genes, 5- enrichment of OG/TS/LOFs, 6- fraction of blocked reactions, 7- resolution power, 8- robustness to missing data, and 9- robustness to noise. Colorbar indicates normalized performance scores.