The word mutation is often associated with the fantastical in fiction or, in real life, with illness or disease, like the BRCA mutation that predisposes those with it to breast cancer. Recently, one of the newer ACCESS-allocated resources, Neocortex at the Pittsburgh Supercomputing Center (PSC), was used to better understand mutations.

Matthew Andres Moreno, a Schmidt AI in Science Fellow at the University of Michigan (U-M), studies something called hypermutation. Hypermutation is what it sounds like – unusually high levels of mutation. Moreno is researching why these hypermutations are so rare.

Mutations are random changes to DNA. These changes can be benign, like a random freckle on your skin; detrimental, like cancer cells; or beneficial, like the color of a moth’s wing that makes it harder for predators to find it. Hypermutators remain rare in nature, despite lab studies that show they tend to take over in controlled experiments. Moreno’s research focuses on why this phenomenon isn’t more common. 

“The question that’s posed by [the earlier] work that I think is really interesting is, we have all kinds of large populations in biology where there aren’t hypermutators that have won out,” said Moreno. “So, what’s the missing piece from the puzzle?”

Moreno used PSC’s Bridges-2 supercomputer for his initial simulations, but he then utilized the Center’s Neocortex system, a relatively new compute resource allocated through ACCESS. According to PSC, the Neocortex system “takes a completely different approach to its hardware compared with traditional computers. Instead of connecting many small computing chips, the WSE (wafer-scale engines) incorporates a massive number of computing cores — 850,000 apiece, in the two WSE2s featured in Neocortex — on a single, dinner-plate-sized wafer with multiple connections between the cores. This design speeds artificial intelligence (AI) learning greatly.”

For these hypermutator experiments, because that population size is a critical parameter, having that scale on the wafer scale engine allowed us to run experiments up to 1.5 billion agents in a population.

–Matthew Andres Moreno, University of Michigan

To read more about how Moreno used the Neocortex system for his research, you can find the original article here: Large-Scale Evolution Simulations on PSC’s Neocortex Tackle Questions about Hypermutator Evolution.

Resource Provider Institution(s): Pittsburgh Supercomputing Center (PSC)
Resources Used: Neocortex, Bridges-2
Affiliations: University of Michigan
Funding Agency: NSF
Grant or Allocation Number(s): BIO240102

The science story featured here was enabled by the U.S. National Science Foundation’s ACCESS program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.