Oncogene inference optimization using constraint-based modelling incorporated with protein expression in normal and tumour tissues

Cancer cells are known to exhibit unusual metabolic activity and yet, few metabolic cancer driver genes are known. Genetic alterations and epigenetic modi cations of cancer cells result in the abnormal regulation of cellular metabolic pathways that are different when compared to normal cells. Such a metabolic reprogramming can be simulated using constraint-based modelling approaches towards predicting oncogenes. We introduced the tri-level optimization problem to use the metabolic reprogramming towards inferring oncogenes. The algorithm incorporated Recon 2.2 network with the Human Protein Atlas to reconstruct genome-scale metabolic network models of the tissue-speci fic cells at normal and cancer states, respectively. Such reconstructed models were applied to build the templates of the metabolic reprogramming between normal and cancer cell metabolism. The inference optimization problem was formulated to use the templates as a measure towards predicting oncogenes. The nested hybrid diff...