Using the model, researchers can input which genes are expressed in a diseased tissue and get as an output the metabolic pathways in which these genes are involved–as opposed to painstakingly searching the scientific literature for information one gene at a time. For example, in the case of the liver, the model might tell researchers that a gene overexpressed in cancerous liver tissue is involved in specific metabolic reactions, creating particular products. Researchers might then look for a drug that targets these pathways or products. “There are very large-scale metabolic shifts in cancer tissue,” Regev says.
James Collins, professor of biomedical engineering at Boston University, has already begun using network-level approaches to understanding cancer, and he says he will use Palsson’s model in his research. “You can look at differentially expressed genes in a patient with prostate cancer,” says Collins. “Among those, are there pathways that indicate the underlying processes of the disease?” It will “enable us to filter and condense complex data and identify drug targets.”
What’s more, the model could help researchers better understand and optimize existing drugs. “It’s difficult to figure out which genes are affected indirectly by a drug,” says Collins. “You want to know what you’re hitting to get better chemistry, stronger intellectual property, and understand side effects.” Palsson points out that because the network can identify multiple ways to generate the same outcome, it may help drug companies come up with compounds that have the same effects–alternatives to statin drugs like Lipitor, for example–without violating their competitors’ patents.