MY RESEARCH LIES AT THE INTERFACE OF COMPUTER AND BIOMEDICAL SCIENCES AND FOCUSES ON THE STOCHASTIC PROCESSES UNDERLYING EVOLUTION. I DEVELOP COMPUTATIONAL METHODS TO LEARN FROM LARGE-SCALE BIOLOGICAL DATA SETS AND BUILD MATHEMATICAL MODELS TO EXPLAIN OBSERVATIONS ON A MECHANISTIC FASHION.
MOST RECENTLY, I FOCUSED ON INFERRING THE EVOLUTION AND THE SEEDING OF METASTASES IN PANCREATIC CANCERS.
Johannes Reiter returned to campus in Fall 2018 to give a special Think and Drink and to share his research with the current campus community. He also found time to do a video interview with us.
I don’t really have a business card but if I had one it would say Johannes Reiter instructor at the Canary Center for Cancer Early Detection at Stanford University School of Medicine.
The coolest thing is that I can work on a globally really important disease. I work on tumors and specifically tumor evolution. I try to approach this problem from a mathematical perspective I try to come up with a quantitative framework that can describe the evolution of cancer.
In the future, I still want to be at the interface of multiple fields like computer science, biology and medicine. I also want to do research in academia perhaps also together at a medical school or a hospital and work together with various researchers from these various disciplines.
My background is in computer science, originally I actually did computer engineering. Then I did computer science at the Technical University in Vienna. When I came to IST I wanted to stay in computer science but I kind of got into theoretical biology and towards the end, I got more into cancer evolution and combining data analysis and mathematical modeling.
IST played a really important role in my career because before I started here I saw myself as an engineer and not as much as a scientist. But then when I started my PhD here I got introduced into so many different areas and I got interested in areas outside of computer science and outside of computer engineering. And that is also the most interesting thing about my job currently. That I can work with experts from various different fields and that keeps my job interesting.
My advice would be: think critically and deeply, try to become independent and ask your own questions, very early on in your career. And perhaps do an internship in industry so you have a good comparison between research that is happening in academia and research that is happening outside of academia.
My favorite memory of IST are definitely all the table soccer games which we played for a lot of time basically every day after lunch.
Johannes Reiter was a PhD student in the Chatterjee group at IST Austria, and is currently doing a postdoc in Prof. Martin Nowak’s group at the Program for Evolutionary Dynamics at Harvard University.
Hania Koever, Head of the Graduate School Office, interviewed Johannes in Harvard to find out about the journey that took him there.
I followed it in the news when IST Austria was opened, the first time was when they announced an IST Lecture with Martin Nowak. I found out more about IST Austria and at the end of 2009 I decided to apply for IST and do science there.
In his IST Lecture, Martin Nowak talked about game theory and in particular the prisoner’s dilemma; it’s a simple game where you can either cooperate or defect, and he could explain so many things in evolution just with that easy game.
I studied Computer Science and just started with my master’s degree, and so I thought, that’s an advantage, you don’t even need a master’s degree—you can directly apply in the first year—this is my big chance. I came for interviews and was already very happy that I was invited.
A bit more than 10.
My project started a bit earlier, but I officially started in September 2010.
I knew it from before, because I visited for the interviews, and I went to the Open Campus Day, so I knew some people there already. I knew my likely supervisor and also some other professors from the interviews.
I still remember my first day: I didn’t really know what to expect from being in this new professional science environment. So I wasn’t sure how I should dress, for example. Then I met Krish on the first day, and I think he was running around in flip-flops as usual, so I thought, ok, maybe I shouldn’t have worried about that.[Laughs]
All these things were there already, from the beginning, so I did rotations with Krishnendu Chatterjee, Tom Henzinger and Nick Barton. There were not many professors there yet. There were maybe 7 professors, and some of them had brought their own students, so there were 11 students. Very good faculty-to-student ratio.[Laughs]
When I joined I thought I would also work on Game Theory, inspired by this lecture given by Martin Nowak. Then I worked a little bit with Tom’s wife, Monika Henzinger, at the University of Vienna. She was working on algorithmic game theory and I was very fascinated by that topic. Krish had a collaboration, so I went a little bit into that direction.
But before I started at IST Austria, Krish proposed some projects to me and they were in the evolutionary game theory area, and I started working on that and I liked it and, then had some first results and it turned out that this was a collaboration with Martin—I didn’t know that! It made me kind of happy because that was how I got interested in IST Austria in the first place, and I could even work on this project. That was the coolest. We were in touch with Martin more often, and then he[Martin Nowak]suggested to me at some point, “I need someone in computer science to work on this particular project in cancer”. He then introduced me to another area that led me on this route.
At the beginning of the PhD, not that much, because the problem is typically formalized by the supervisor already. So this is what Krish and Martin did for me. Then, the more senior you get, the more you identify problems, and you read more, and of course, you have to learn a lot of biology so that you can identify relevant problems to have an impact.
I very much liked—from the beginning—the interdisciplinary system at IST Austria. I also enjoyed the fact that in my first and second year I could take courses from different areas. That helped me a lot. Of the 7 people starting in that year, they were all in different areas. There were no two students in the same research group. And that was already in itself very cool, because when I was at TU Wien it was very different—I started with 700 students, not 7, and all of them were in computer science. Just a very different environment.
I think most of the time it was an advantage, because not many things were established, so you establish your own rules, and you just needed to convince some people that it was a good idea.
As for challenges? Of course if you want to be interdisciplinary then you have to go more outside of your comfort zone. It’s more time-consuming than just staying in your area and continuing what you have done for the last 5 years. But it’s also much more interesting.
I mean in the early days I just knew every face and, if someone joined, then I’d be “Hmm, I’ve never seen that face”, and then we’d talk. Then we grew to 30 people, 50, 100, and you lose track of all the people. Now if I go back it’s more like, “huh, I’ve never seen most people before”. But that is how it is now. I think the culture is still very similar in many ways. On the bridge[common room area linking the Central Building and the Bertalanffy Building]people from different disciplines meet—they succeeded in keeping that kind of culture.
It’s about 30km by car from IST Austria. In my hometown, almost everybody knows about IST, which is probably very different from the rest of Lower Austria where many people still don’t know about it. Also, many Austrian students here in Boston, you would think, should know about universities in Austria, but that’s not the case.
In my hometown, I know many people and many people know me. They know I was at IST Austria for some time, so they got to know that there is some great science going on there.
You actually hear about the institute very frequently, on TV, or if you read the newspaper. People don’t realize however, that IST Austria is in Klosterneuburg, or in Gugging. But slowly they are realizing, this is that outstanding institution and there’s a lot of amazing science going on.
The most challenging project we only finished basically now[two years later], and I started it in my third year of PhD, so that took a long time.
Yes, for that I had to learn a lot of new stuff, because typically I just do the theoretical analysis, mathematical modelling etc., and for this particular project we had to analyze a lot of data and in particular DNA sequencing data. So, I had to read up a lot on the literature and made a lot of mistakes along the way, starting with very naïve approaches in the beginning. But I gained more experience over the course of multiple years, and it has essentially become my main topic of research by now.
That was a rather easy decision for me, because I visited Martin frequently during my PhD so I already knew the environment here very well. And I knew it was kind of similar to IST Austria in that you have a lot of freedom to work on the problems that you’re interested in. I also knew many good people working in different areas, and of course it’s an advantage if you already started in that area and have interesting ongoing projects.
Essentially, I work on the evolution of cancer, and there are many different paths it can take—from the initiation of cancer, how it progresses, and then metastasis. So there’s a very long process which can take multiple decades from initiation to metastasis, and we basically want to understand which genetic events take place in that time to understand the evolutionary dynamics.
In particular, in the last couple of years I focused on the evolution of metastasis itself, so how it spreads from the primary tumor, which genetic precursors are needed for metastasis, where it travels to, and how long it takes.
Now that sequencing of the genome has become so cheap, a lot of science groups generate a big amount of data. It is very hard for someone who has training in biology to analyze all this data in a sophisticated way—a computer scientist or people who have training in that area can be very useful for that. What we do here in addition to analyzing these data is we try to find patterns and then make mathematical models to try to understand the underlying mechanisms—what would lead to metastasis, for example.
We work with many physician scientists who got their degree as a medical doctor and then transitioned into science and did another PhD. We collaborate with many people at Johns Hopkins for example. We also work with leading cancer physicians at an oncology lab in New York. They have access to cancer patients and can perform autopsies to obtain cancerous tissue samples found around the body and sequence all these different tissue samples. You can learn a lot about the genetic mutations present in these different metastases, and reconstruct the evolutionary history and build a picture of how the metastases might have evolved over multiple decades.
Certainly. Sometimes when you discuss a specific problem, probably our collaborator might think “oh this guy doesn’t understand even basic biology”, and then when we explain our mathematical models we think “they haven’t understood anything of what we did in the last year”. It takes a long time, but over the course of collaborations you get to learn more and more about the methods that other people use, and I find that extremely interesting.
I think the basics of doing mathematical models, understanding evolutionary dynamics, and the basic principles are similar across many field. If you are interested in the evolution of organisms and evolution of different diseases, how you would study them in a theoretical fashion is in fact very similar. One of the papers we produced during my PhD was combination therapy for cancer—how you combine different drugs. Then Martin said, that’s exactly what we did 10 or 15 years ago, in viruses, in particular, for HIV. They couldn’t treat it for a long time successfully, and then they started combining different drugs and that also came out of these mathematical models and it suddenly worked out. For cancer we could basically use these insights that he got from viruses and translated those to cancer.
That’s a very difficult question. Of course I very much enjoy what I’m doing, so I would like to stay in academia. I also benefited very much from the environment to which I was exposed at IST Austria with so many excellent people and now also at Harvard—there is this whole network of researchers. Essentially, you get to know many good people and they again know many good people, so I hope to end up at some institute where I can continue to benefit from an excellent interdisciplinary environment and I hope it will bring me to the next big thing.
But as you know the job market in academia is extremely tough, so it’s very hard to predict where you will actually land. If my job will be in Europe or central Europe, that would be amazing; if it’s somewhere else, we’ll see.
Work on the problems that you are most passionate about. I think it’s very important to not just solve a problem given to you—of course, it helps to read up on a particular area to understand what the science is about—but very early on start working on your own ideas. And learn from your mistakes—and often, setbacks. Don’t be afraid to ask other people for advice. I think from these you learn the most.