Raphael Gottardo, computational biologist

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Raphael Gottardo, computational biologist

Data-driven approaches to bring scientific discoveries from research labs to the bedside

Dec. 12, 2018

By Sabin Russell / Fred Hutch News Service

When Dr. Raphael Gottardo was a young boy growing up in Lyon, France, his knack for mathematics caught the attention of his teachers early on.

He started elementary school a year ahead of his peers, and when — by his own admission — he grew bored and was annoying his classmates, he skipped another grade. He eventually entered college at the age of 16.

Yet Gottardo never saw himself as some sort of wunderkind. “I was good in math, so that is what I did. It was something I could easily do, without having to work too much.”

Somewhere along the line, Gottardo shed his youthful laziness.

When he went to Portland, Oregon, on an undergraduate exchange program, he settled on statistics as a more practical use of his talents. Then he caught the biology bug during a fellowship at Los Alamos National Laboratory, in New Mexico. It was shortly after 9/11, and the federal lab was focused on efforts to analyze potential threats from anthrax and other biological agents.

Nearly half a lifetime later, Gottardo now finds himself at the center of the busy intersection of biology, data science and technology at Fred Hutchinson Cancer Research Center, where his skills in mathematics and biostatistics are in constant demand.

In 2018, he was tapped to run the Hutch’s new Translational Data Science Integrated Research Center, a cross-disciplinary effort that draws from scientists throughout the center. The aim is to bring scientific discoveries from research labs to the bedside sooner using data-driven approaches. To do so, the Translational Data Science IRC is pulling bench scientists and clinical researchers from many corners of the Hutch into collaborations with experts in data science.

“To bring people together who may have never worked in this area is very transformative,” he said. “This is what we need. It is obviously very important to anything we do now, because everybody is generating very large and complex data sets.”

Gottardo, who holds the J. Orin Edson Foundation Endowed Chair, arrived at the Hutch from a teaching post at the University of British Columbia in 2010, a time when the sheer volume of information gathered by scientists on the genes, proteins and molecular signals involved in cancer was skyrocketing.

“When I came to Seattle, we were analyzing clinical trials where we might have 1,000 samples, and we couldn’t load all the data onto a computer’s memory. So, we had to be clever about how to access the data, how to store it, how to slice it.”

He likened this early challenge in big data to searching for a song on a CD. “You don’t want to read the entire album at once, you just want to read that one song.”

The source of this data explosion is rapidly evolving technology that analyzes the components of cells — such as the full inventory of genes and proteins — in greater depth and at higher speeds. When Gottardo was working at Los Alamos, he probed bacterial cells for their gene expression — charting which genes are turned on or off within samples containing millions of cells.

Today, high-speed gene-expression studies are performed on samples where each individual cell is separated and analyzed, each cell yielding more data than could be gleaned from the entire pooled sample 18 years earlier. “I still do gene expression, but we’ve more or less multiplied the size of the data by 1 million,” Gottardo said.

Much of his work is focused on profiling the cellular components of the human immune system. “It’s when you get into the details that it really becomes interesting,” he said. “The immune system is very complex, and it turns out we don’t know a whole lot about it yet. Looking at these single-cell technologies generating massive amounts of data has brought me to really cool statistical and computational challenges.”

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Dr. Gottardo
More resources:

    •  Gottardo Lab
    •  Scientific profile