Once inside the human body, the parasite that causes malaria eventually invades the red blood cells, where it becomes vulnerable to an attack by the immune system. But the parasite is able to remain evasive by quickly and repeatedly reshuffling its genetic deck of cards to change the expression of the protein marker that signals to the immune system that there’s a problem.
Biologists understand the mechanism for this reshuffling, but they lack a computational tool that can model the parasite’s evasion efforts and allow researchers to ask the important scientific questions that could lead to more effective malaria treatments.
This is where Jacobs, who is not a biologist, comes in. Jacobs is a computer scientist who specializes in working on methods for networks and trying to develop tools at the interface of theory and data. It turns out that the same methods computer scientists use to describe a wide variety of networks, including social networks, can be useful when thinking about malaria.
“You can think about gene recombination as the shuffling of different units that you can describe with networks,” Jacobs said. “If you do this, the malaria network has some big clusters in it, with lots of exchanges between them. This starts to give us a map of the different ways malaria reshuffles and where it could reshuffle in the future.”
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