Unlocking Computer and Animal Evolution

Dr. Elise Lauterbur’s journey from biologist to evolutionary ecologist highlights the transformative role of High Performance Computing (HPC) in scientific discovery. With a dual background in biology and music from Oberlin College, Elise’s fascination with behavioral ecology led her to study the genetics of animal behavior, starting with goldfinches and mate selection. This spark ignited a path of research that took her to her PhD program, where she leveraged a blend of fieldwork and computational analysis to explore the unique biology of bamboo lemurs. These lemurs consume cyanide-rich bamboo without harm, as they had adapted with a sulfur-based amino acids to detoxify the cyanide.

In her postdoctoral research, Elise embarked on two major projects leveraging HPC resources. The first turned her focus to investigating virus resilience in Myotis bats. She developed reference genomes, which involved sequencing over 100 bats and analyzing genetic variations to uncover virus resistance patterns. "This is something where the use of HPC is really important," she explained, "because it lets me run things in parallel and at a speed that's basically impossible on a local machine—and if it were possible, it would be increasing the temperature of the room I'm running it in noticeably."

Her second project expanded the scope, examining viral adaptations across various mammals. Elise analyzed how factors like temperature and rainfall influenced viral exposure across millions of years using Bayesian models (models that incorporate prior knowledge). For Elise, the power of HPC made these insights possible, saying, “This is something that I think people probably dreamed of before HPCs existed but isn't possible without the kind of computing power that HPCs provide.”

 Elise went on to use the university HPC to develop a convolutional neural network (CNN), which specializes in image classification, to classify genome regions under natural selection. She generated vast amounts of training data through simulations for the CNN’s machine learning. Thanks to the flexibility and power of the University of Arizona’s HPC resources, her method has gained traction, with collaborators from various institutions adopting it for their own genomic studies.

This fall, Elise begins her first faculty role at the University of Vermont, where HPC will continue to be a central tool in her research. Reflecting on her journey, she credits HPC as a cornerstone of her work, driving her forward in fields that would otherwise be constrained by computational limits. Elise’s work exemplifies how HPC opens new frontiers in genomics and evolutionary biology, unlocking new understanding of how mammals adapt to environmental challenges across millennia.