Recent headlines have cast suspicion on social network analysis, which can mine data from the internet to target advertisements or potentially influence elections.
But what if we could use those same tools not for the economic or political gain of a few, but for the health of all humankind? Scientists now can harness the tools of social network analysis to understand connections among genes, an advance that someday could lead to medical advancements.
Megha Padi, PhD, director of the UA Cancer Center Bioinformatics Shared Resource and an assistant professor of molecular and cellular biology, 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 results were published online April 19 in the open-access Nature Partner journal Systems Biology and Applications.
Cancer researchers usually focus on specific genes when comparing healthy cells to tumor cells, an approach that does not completely explain what occurs behind the scenes to cause cancer.
“You can get a list of the parts in your car, but you won’t understand what makes the car run until you understand how all the parts are connected to each other,” Dr. Padi said.
Likewise, it is essential to study how genes work together as part of a larger network, so Dr. Padi is analyzing these gene communities in the same way one would examine a social network composed of connections among people who know one another.
Dr. Padi is the first author on the study, in collaboration with John Quackenbush, PhD, director of the Center for Cancer Computational Biology at Dana-Farber Cancer Institute. The study was conducted when she was a postdoctoral fellow at Dana-Farber; Dr. Padi joined the UA in January 2018.
Genes in a community, like people in a social network, talk to one another. In a healthy cell, gene communities function like factory workers, cooperating to process raw materials into goods the cell needs to thrive. In a diseased cell, miscommunication along the “assembly line” results in defective products. Tracking how genes’ conversations change over time might provide clues how cancer arises. These conversations can be analyzed using tools developed to study social networks.
“A classic example is a phone network,” said Dr. Padi. “I’m calling my mom, my mom may be calling my sister, my sister’s calling me, and I may call you, but you won’t call my mom. Natural communities are formed, like your work community and family community.”