Atul Butte (photo by Susan Merrell)

Atul Butte (photo by Susan Merrell)

As founder of three different data-driven companies, Atul Butte, Director of the UCSF Institute for Computation Health Science knows a thing or two about biotech entrepreneurship.

Genomics abounds with commercial opportunities, new ideas and technologies just waiting to be licensed and developed into the next world-beating research or healthcare product. More and more, a solid entrepreneurial skill set will come in just as useful as a dab hand in the laboratory. In fact, according to Atul Butte, Director of the UCSF Institute for Computation Health Sciences and founder of no less than three different biotech companies, there’s very little difference between writing a research grant, and writing a business plan, and the latter might just make the benefits of all that data available to the wider public.

FLG: You’re a rare breed in that you have remarkable experience in computational and medical fields at some very impressive organisations. I was wondering if you could talk a little about what got you interested in computer science in the first place, and how your career has taken you up to where you are now?

AB: Sure, I’d love to. When I was growing up, the personal computer revolution took off. It just exploded in the United States. So my first computer that I started to learn how to program with was an Apple II+ that my parents got me. Prior to that, I was writing programs, so I’d take that to the department stores to plug in to the computer there. So they obviously figured out that having a regular computer for the home, which was ridiculously expensive, but obviously now, in hind sight, was really worth it.

I knew I wanted to do computers and medicine, so I was volunteering at hospitals and things like that, that high school kids do. At the time National Geographic magazine had these covers of ‘The New Radiology’ – MRI and CAT scan. So I thought for the longest time if I wanted to do computers and medicine, I was going to be a radiologist. I went into a program at Brown University in Providence, Rhode Island. They have an eight year program, where you major in anything that you want and you’re guaranteed to go into the medical school if you keep your grades up. So that’s how I was able to do computer science, and still go to medical school there. So I got the full computer science experience; I spent one winter at Apple, writing software; I spent some time at Microsoft on the Excel team; so I learned how to write code. I went on to medical school and took a year off, and during that year, the Human Genome Project started up. To me, that was the ultimate in digitising biology and medicine, and I never looked back at radiology from there! I really got enamoured by molecular biology and genomics, and after medical school I went straight into residency and training in paediatrics.

My mentors convinced me I should go back to MIT and get my PhD, so I did that while I was seeing patients in Boston. That’s probably the only place you can see patients and get a PhD at the same time. Then in 2005, I moved to Stanford and worked there for 10 years. Last year, I moved to UCSF where I’m the new Institute Director.

FLG: When people hear your name, one of the first words that come to mind is ‘data’. How is data changing the way we approach scientific research?

AB: We are able to measure so many things now, right? In genomes, we have gene expression microarrays, that’s how I got my start, those gene chips from Affymetrix; we have proteomics, cell counters, cell sorters, all of these technologies that can measure so much now. I think that has really changed a lot. In the beginning, there was a lot of resistance to this kind of technology. I mean, there were derogatory terms like ‘fishing expedition’, things like that where, in the old days, people thought you should just have one idea and just chase it down, and if it doesn’t work it doesn’t work. And now you really survey, you scan, you screen, the entire genome, the entire proteome. The other revolution that happened is not just that we can make these measurements, but the measurements are now increasingly open to the public. Because of transparency, and increasingly now because of reproducibility, we’ve got all these pushes to get the data open to the public. So many people make these measurements, and so many people make them public, but so few people use that data. To me, that’s really the ultimate treasure, it’s all this raw scientific data from these top scientists, who get a lot of funding to do this. Yet so, few people are actually using that data, so it seemed like a natural kind of fit to my background.

Check out the full interview in the latest issue of Front Line Genomics magazine.


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