Most people are familiar with social networks. It's the web of connections between you and the people you know.
Marketers have mastered the art of decoding these connections to target advertisements tailored to their audiences.
Researchers at the University of Arizona's Cancer Center are exploring if it's possible to analyze our cell's genetic network in the same way to find new drug targets for cancer and other diseases.
What if someone told you we could track conversations between genes in the same way marketers track social networks?
It might sound a little odd, but scientists at UA say it is certainly possible.
Megha Padi Ph.D., Director of the UA Cancer Center Bioinformatics Shared Resource, developed a computer algorithm called ALPACA that reveals which gene networks are activated in a diseased cell, an approach that could lead to better treatments for various diseases.
The genes in our cells talk to each other and like members of a social network, however some talk to each other more than others. When genes talk to each other a lot, they form natural groups we call communities.
"They function like an efficient factory taking in raw materials and processing them and manufacturing goods the cell needs to thrive," Padi said.
In cancer cells however, the conversations can be strange, which leads to defective products and disease.
Understanding how conversations between genes change as cells shift from a healthy state to disease can provide clues about how cancer and other deadly diseases form.
Unfortunately, with so many genes in our chromosomes, it's a massive task.
It's different from methods used in the past, because it looks at the entire picture and focusing on finding conversations that happened in diseased cells but not on normal cells. As a result, identifying genes communities that are active in diseased cells can guide researchers in developing drugs that target those communities.
Padi hopes results will be ready in the coming years.