Eric Schadt on genomics

Eric Schadt is the Director of the Institute for Genomics and Multiscale Biology at Mount Sinai Hospital in New York City. He is also the Chief Scientific Officer at Pacific Biosciences. We sat down with him to discuss his impressive career path. Dr. Schadt went from pure mathematics and computer science to bio-mathematics. His research focuses on generating and analyzing big biological datasets to further our understanding of human disorders.

Tell us about your background before college.

I have a very odd background. I grew up in a very poor rural area, where education wasn’t really something that was promoted and then went into the military. Through that, I got into college and when I started, it was a very intellectual sort of exercise, trying to discover how smart I was and how far I could push myself. So the combination of CS and pure math was a very natural place to go to push myself.

You started out by studying Math and CS. What got you into biology?

My undergraduate degree wasn’t so challenging so I decided to do my graduate studies in pure math, which I view as one of the most conceptually difficult areas of study. I was going through that but I always had an applied bent, and in pure math, it’s doing math for math’s sake: it’s not encouraged to figure out whether what you work on would satisfy another area of study. So out of curiosity, I wanted to figure out how everything we’re doing fits together, why we’re here and so on.

At UCLA, once I got the Ph.D. candidacy in pure math, I made the jump to a bio/math dual Ph.D. program that had the right level of rigor. I didn’t want to be a mathematical biologist of the type that were very good mathematicians but didn’t have a very deep understanding of the problems in biology and how to design your own experiments. I wanted to grasp that intuition; I wanted to think like a biologist.

Are you a biologist who does computation or a mathematician doing biology?

I view myself as a biologist who is heavily computational. Mathematicians that I’ve worked with in the past would not respect at all what I’m doing now as being real math. That always stings me a little bit, because biology has classically not been a very quantitative science so the kind of math that we do (e.g. Bayesian network reconstruction) is difficult but it’s not what a mathematician would view as the hardest thing!

Did you go to industry immediately after your Ph.D.?

Yes. What I saw while finishing up my Ph.D. was this revolution around technologies like the gene chips (used to recognize DNA from samples being tested) and microarrays (used to measure gene expression levels). Many companies said they’d start generating massive scales of data, house them in big databases and mine them, and that was unheard of in biology.

So I was very interested in those technologies and looked around for how I could get access to them. Roche Biosciences was among the first to sign all these big deals with the companies who were making the technology, and they appreciated the fact that you needed someone much more mathematical to look at the data. So I joined Roche. It was perfect timing.

What attracted you to Roche?

They had access to technologies that none of the universities had because of the outrageous costs at the time. What also drove me to Roche were the big resources and the excitement of having to carry out the right experiments to show proof-of-concept.

Because I was one of the first to apply statistical analysis for gene chips, I gained a certain degree of fame doing that, which caught the attention of the heads of Roche. But all of a sudden I was spending 50% of my time in meetings and fighting for why doing this is important instead of actually doing the science.

What did you do next?

I started talking to Rosetta, a startup that focused on building the technologies behind gene chips. After a year and a half at Roche, I went to Rosetta because they were more focused on the science. Also, since it was a startup, there was no bureaucracy or politics.

About a year and half later, Rosetta was bought by Merck. They loved what I was doing and they invested very heavily in that arm for 5-6 years. We did lots of good science and published a lot of papers. By the time I left Merck, we were responsible for about half of all the new drug discovery programs, so we were also delivering on the business side.

Why did you leave Merck?

Merck also got limiting because, as we were learning more about building these Bayesian networks, we wanted to go to the next level. We told them what we thought the next step should be but the price tag was about a billion dollars. That was too expensive for any one company to fund, so they were thinking more along the lines of turning this area into a pre-competitive space, where companies would be able to share all the data between each other.

After a lot of discussion, the co-Founder of Rosetta and I left Merck to found SAGE Bionetworks, a non-for-profit research center in Seattle. We focused on open-access biology: how to facilitate sharing of big data, how to build models and validate them, and enabling others to interact with those models.

What did you do once at SAGE?

Now that SAGE was set in motion, I joined Pacific Biosciences (PacBio). The idea was that I would setup a new institute in the Bay Area that would focus more on data generation and model building. PacBio knew that going in, and they liked the idea of me spending 75% of my time doing research outside the company because it would cost too much to have that big of a research effort going on internally. And for 25% of the time, I would be the Chief Scientific Officer at PacBio.

Although we got offers from UCSF and Stanford to set up the institute there, we needed ~$100M to really make a go at that project and we were having trouble finding enough money to make that project more than just my lab and myself. As I expanded the search for money, I locked onto Mount Sinai because we found donors inclined to give the $100M to do this effort.

What do you like best about Mount Sinai?

Compared to Stanford and UCSF, Mount Sinai had a reduced bureaucracy. Here, there’s a CEO who runs the hospital and the medical school. It’s a command-and-control architecture that I’m used to from the business side, where it’s easier to see things get done than one where every decision needs a committee. And it is smack down in the middle of a medical center, which will allow us to impact decision making directly in the clinic. That was very attractive. Moving to the East Coast is not something I thought I would ever do but all the pieces fell together!

Do you have advice about choosing between academia and industry?

I’ve always had a foot in academia and another in industry. Before joining Mount Sinai, however, the heavier foot was always in industry. This is the first time my heavy foot is in academia. I’ve seen both worlds for a long time and I think what academia offers is the ability to be your own CEO, grow out your own program and even though there are funding issues, you have much greater flexibility than in a company. You can make the kind of partnerships you need to leverage what is happening in industry and I view that as a more favorite path.

But I will say that the industry path offers, especially to young investigators, clarity of purpose and focus. Going to a startup is an experience like no other. Unlike academia, where you’re able to float and your timelines aren’t so critical, in a biotech startup, you’re living six months to six months. You have money and you see the cliff of when the money will end and if you don’t meet milestones, you’re going off that cliff and you’ll have to fire half the people in the company. That drives you to form bonds and work as a team to accomplish things far bigger than you could ever do. What you learn from that is invaluable.

The other advantage that I learned at Merck is: If what you’re working on is in the critical path of a company, the scale of resources you can get to carry out your vision is an order or two of magnitude greater than what you can ever get funded to do in academia, especially if it’s something new and risky.

What are the privacy issues with DNA sequencing becoming more popular?

We always want to protect data that can personally identify us but there’s another component of that which is the expectation of privacy. For example, your social security number can identify who you are and you should have an expectation of privacy around that. On the other hand, there are things like your face, which can be used to identify you but you have no reasonable expectation of privacy around your face.

DNA used to be in the camp of the social security number: of course you want to keep your DNA protected because it defines who you are. What’s changing now, however, is that the technology is becoming so amazing that, in 10 years, sequencing your genome will become as easy as taking a photograph? When that happens, there will still be the personal identifiable issue but the expectation of privacy will go away, because how can you have expectation of privacy around something that is as easy as taking a photograph. That is the transition we’re in. Of course you can take steps to protect the information but there are limits to what kind of privacy you can expect. Educating the population and legislators about that is critically important. The next step is to make laws that prevent discrimination based on that data.

What’s the biggest change you’d like to see in biology?

If biology wants to go to the next level in achieving an understanding of all the complex things we see, it needs to become much more quantitative and information-driven. Biology has to become a more physics-like discipline.

If biologists don’t do that, they’ll become irrelevant when Google, Amazon and other computer science powerhouses come in and do it before them. They won’t wait for the biologists to give them permission to analyze that data so if biologists aren’t there to work with them, they’ll be supplanted. I don’t think that’s extreme when you consider competitions where solving a biological problem gives you a $20,000 or $50,000 prize. If you look at who’s on the top of the leaderboard, none of them are biologists. Last time I checked, the top of the leaderboard was an accountant from Australia who knows nothing about biology.