Rachel Gittelman’s deepest fascination in graduate school was human evolution, but she quickly realized she couldn’t find the answer to her central question — how did humans become human? — without a deeper knowledge of computation and statistics.
“The collection of human genomes across the globe is really a record of the kind of evolution we’ve gone through,” she said. “In order to fully understand that evolution, you need to be able to make inferences from really large numbers of genomes, really large amounts of data.”
Gittelman is now making those connections within the genetics of the immune system at Adaptive Biotechnologies, where she is a senior manager of computational biology on the company’s innovation team.
She is using huge datasets to interrogate how the immune system detects and responds to diseases. Her efforts dramatically accelerated at the outset of the pandemic, when she helped lead a team to identify which T cell receptors respond to Covid-19. The team’s findings were crucial to the launch of T-Detect, a Covid-19 diagnostic tool that was granted an emergency use authorization from the Food and Drug Administration in March.
“We’ve made a ton of progress in these areas and there’s still so much more we can do to help ultimately decode the human immune system,” Gittelman said. That work doesn’t only apply to the urgent demands of the pandemic, but also to cancer and a long list of autoimmune conditions, areas where computational tools are now promising to speed up lengthy trial-and- error processes to make accurate diagnoses.
“Many patients with autoimmune disease go through these diagnostic journeys that can be years long, rather than the doctor being able to tell them, ‘This is what you have,’ and tell them confidently,” Gittelman said. “I think we can contribute to that effort, and even beyond that really inform you about how your immune system is working.”
— Casey Ross
