For using Personalized genome-scale metabolic models, please refer to
Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Mol Syst Biol 10, 721.
Genome-scale metabolic models (GEMs) serve as scaffolds for integration of omics data for understanding the genotype-phenotype relationship. Here, we evaluated the presence/absence of proteins encoded by 15,841 genes in 27 hepatocellular carcinoma (HCC) patients using immunohistochemistry and reconstructed personalized GEMs for six HCC patients using the proteomics data, task-driven model reconstruction (tINIT) algorithm and HMR 2.0. The personalized GEMs were employed to identify anticancer drugs using the concept of antimetabolites i.e. drugs that are structural analogs to metabolites. The toxicity of each antimetabolite was predicted by assessing the in silico functionality of 83 healthy cell typespecific GEMs, which were also reconstructed with the tINIT algorithm. We predicted 101 antimetabolites that are effective in preventing tumor growth in all HCC patients and 46 antimetabolites that are specific to individual patients. 22 of the 101 predicted antimetabolites have already been used in different cancer treatment strategies, while the remaining antimetabolites represent new potential drugs. Finally, one of the identified targets was validated experimentally, and it was confirmed to attenuate growth of the HepG2 cell line.
Download Healthymodels 3 from here HealthyModels.zip