Supplementary data are available at Bioinformatics online.

Genome-scale metabolic modeling has emerged as a promising way to study the metabolic alterations underlying cancer by identifying novel drug targets and biomarkers. To date, several computational methods have been developed to integrate high-throughput data with existing human metabolic reconstructions to generate context-specific cancer metabolic models. Despite a number of studies focusing on benchmarking the context-specific algorithms, no quantitative assessment has been made to compare the predictive performance of these methods. Here, we integrated various and different datasets used in previous works to design a quantitative platform to examine functional and consistency performance of several existing genome-scale cancer modeling approaches. Next, we used the results obtained here to develop a method for the reconstruction of context-specific metabolic models. We then compared the predictive power and consistency of networks generated by our method to other computational approaches investigated here. Our results showed a satisfactory performance of the developed method in most of the benchmarks. This benchmarking platform is of particular use in algorithm selection and assessing the performance of newly developed algorithms. More importantly, it can serve as guidelines for designing and developing new methods focusing on weaknesses and strengths of existing algorithms.

Zymomonas mobilis is an ethanologenic bacterium and is known to be an example microorganism with energy-uncoupled growth. A genome-scale metabolic model could be applicable for understanding the characteristics of Z. mobilis with rapid catabolism and inefficient energy conversion. In this study, a charge balanced genome-scale metabolic model (iEM439) of Z. mobilis ATCC 10988 (ZM1) including 439 genes, 692 metabolic reactions and 658 metabolites was reconstructed based on genome annotation and previously published information. The model presents a much better prediction for biomass and ethanol concentrations in a batch culture by using dynamic flux balance analysis compared with the two previous genome-scale metabolic models. Furthermore, intracellular flux distribution obtained from the model was consistent with the fluxes for glucose fermentation determined by (13)C NMR. The model predicts that there is no difference in growth rates of Z. mobilis under aerobic and anaerobic conditions whereas ethanol production is decreased and production of other metabolites including acetate and acetoin is increased under aerobic conditions. Experimental data confirm the predicted differences between the aerobic and anaerobic growth of Z. mobilis. Finally, the model was used to study the energy-uncoupled growth of Z. mobilis and to predict its effect on flux distribution in the central metabolism. Flux distribution obtained from the model indicates that coupling growth and energy reduces ethanol secretion and changes the flux distribution to produce more biomass. This coupling is also associated with a significant increase in the proton uptake rate based on the prediction of the charge balanced model. Hence, resistance to intracellular pH reduction could be the main reason for uncoupled growth and Z. mobilis uses ATPase to pump out the proton. Experimental observations are in accordance with the predicted relationship between growth, ATP dissipation and proton exchange.

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