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PhD grad uses AI to advance genetic engineering research
By Evelyn Jones
After an unexpected path to biochemistry, Tyler Browne is using machine learning to make gene-editing tools safer and more efficient
By Evelyn Jones
Tyler Browne’s path to biochemistry started somewhere unexpected: political science.
Browne began his academic journey at King’s University College, blocks away from the lab where he would eventually complete his PhD. His path started to shift as he followed new interests across disciplines.
Driven by his curiosity and a few summer courses in computer science and calculus, Browne decided to try bioinformatics after a lecturer mentioned the program.
“I had no plan to end up where I did,” he said.
This month, Browne is graduating with a PhD in biochemistry from Western’s Schulich School of Medicine & Dentistry, with collaborative specializations in scientific computing and machine learning in health and biomedical sciences.
His research uses machine learning to better predict how CRISPR-Cas9 gene-editing tools interact with DNA.
Browne describes CRISPR as molecular scissors designed to make cuts at specific locations in DNA. The challenge is that those scissors don’t always cut the way researchers want them to.
DNA contains many potential target sites for CRISPR. Testing each one individually takes significant time and resources, so researchers need a way to narrow the field.
“My idea was to use machine learning to figure out which DNA targets were most likely to work,” said Browne.
That idea led to crisprHAL, a collection of machine learning models trained to recognize patterns in CRISPR-Cas9 data.
While many conversations about AI focus on large language models, Browne sees machine learning as a way to make scientific research more efficient. With crisprHAL, that means helping researchers sort through possible genetic targets faster.
By helping researchers narrow the field, tools like crisprHAL could make it easier to study CRISPR-Cas9 activity and design more precise genetic tools.
Through his work, Browne also began recognizing the value of his own contributions.
In one meeting, after Browne brought forward results that raised new questions, one of his supervisors asked what experiment they should run next to validate the finding.
“That was the moment I realized I wasn’t just a student,” he said. “I was an important part of the team.”
While much of Browne’s research happens behind a computer, where the impact may take years to unfold, volunteering with St. John Ambulance gave him a more immediate way to help people.
“Research is a long process, and you often don’t get to see the direct impact,” he said. “Volunteering lets me help people directly.”
Looking ahead, Browne will continue working as a postdoctoral researcher at Schulich Medicine & Dentistry as he explores more applications of AI in biomedicine. Long-term, he hopes his work will help advance genetic disease research.
“There’s a lot of unsolved problems in biology,” he said. “We know what we have to do. We just have to figure out ways to make that safe and effective.”