Predicting selective drug targets in cancer through metabolic networks

Predicting selective drug targets in cancer through metabolic networks

The authors generated a general metabolic model of cancer, by looking for metabolic genes which are highly expressed across many cancer cell lines. Using a Model Building Algorithm from a previous publication, they created a minimal set of reactions which are needed to activate these highly expressed genes. From this, they investigated the effects of in silico knockdown for each gene, on the viability of the network relative to a model of healthy cells. They found significant agreement with shRNA gene silencing data in the literature. They also explored synthetic lethality, by comparing the synergistic effect of knocking down pairs of genes relative to knockdown of each individually. They identified important metabolic pathways for anticancer activity, as well as potential genetic targets for drug therapy.