While an undergraduate at the University of Berlin, I was intensely engaged in the study of theoretical physics and mathematics. Yet I felt that analyzing life, the living system on this planet, was a more fascinating and challenging scientific problem than studying the world of elementary particles. To explore this potential change in career direction, I sought the advice of Max Delbrück at the California Institute of Technology, a physicist originally from Berlin who was one of the founders of molecular genetics.
After visiting friends in Texas, I hopped on a Greyhound bus to Los Angeles, made my way to Caltech, found Dr. Delbrück’s office, knocked on the door, and with a dash of chutzpah, asked the Nobel laureate if he had a moment to chat with an aspiring graduate student. Dr. Delbrück described the areas of theoretical physics that might be relevant to biology in the future — information that was in the forefront of my mind as I entered the graduate physics program at the University of California, at Berkeley, in 1967.
Wanting to move from the theoretical physics of my PhD thesis to the theoretical biology I had dreamt of, I made my second pilgrimage, this time to see Manfred Eigen, a Nobel Prize-winning chemist who was studying biological evolution in Göttingen, Germany. Dr. Eigen surprised me by explaining that the field barely existed, but he did point me to three mathematical biology research problems: a theory of the immune response, neuronal mapping in the brain, and protein folding.
So I packed my bags and I moved from the University of Heidelberg to the Weizmann Institute of Science, in Israel, where I began work with Shneior Lifson on the prediction of three-dimensional protein structures.
Enter the third motivator of my career: the first completely sequenced genome — no, not in 2000, but in 1977! In that year, I saw an amazing paper in the journal Nature from Fred Sanger’s group in Cambridge, United Kingdom, which included two entire pages filled with 5,375 letters, all As, Ts, Gs and Cs, representing the genetic blueprint of a small virus.
I walked down the hall to ask my friend Georg Schulz, “With this kind of cryptic information coming from genomes, won’t biology need computational science to decipher it?” His answer was yes, and I spent the next 23 years of my professional life preparing for the day, in the year 2001, when the 3.5 billion letters of the human genome finally became available. In the process, I helped to develop the field now known as computational biology.
The real value of computational science, when applied to any system, is to predict what’s going to happen next. Weather forecasting is an example. There’s an enormous amount of data collected about the weather, but the data, by themselves, are unintelligible. What’s required is the application of the appropriate mathematical equations embodied in a software system, which, using the data, allows one to compute tomorrow’s weather.
Applied to cancer biology, we want to be able to predict, for example, if a cancer will go from a nonaggressive to an aggressive form, or more importantly, to predict accurately the consequences of possible therapeutic interventions. The goal is to have an impact on human disease, and to do this you have to work in collaboration with physicians. In 2002, Harold Varmus presented his vision of Memorial Sloan Kettering as the perfect environment for this — a place with open doors between basic and clinical research, where close collaboration is encouraged.
My first action at Memorial Sloan Kettering Cancer Center was to start the Computational Biology Center (CBC) and its Bioinformatics Core Facility. The CBC’s researchers and engineers are devoted both to basic science and to the goal of developing diagnostic and therapeutic tools that help improve the lives of people affected by cancer.
We often collaborate with researchers in the lab and in the clinic to translate data — data such as the molecular profiles of cells and tissues, the billions of letters of genome sequences, and the functions and structures of key genes — into biological insights and prediction tools. And the Bioinformatics Core, ably led by Alex E. Lash, provides internal bioinformatics training, collaboration, and infrastructure support.
One concrete example of the practical uses of computational biology is the work we have been doing with Howard I. Scher, Chief of Memorial Sloan Kettering Cancer Center’s Genitourinary Oncology Service, and Francis M. Sirotnak, Member Emeritus and Head of Sloan Kettering Institute’s Laboratory of Molecular Therapeutics. The idea is that cancer cells, like any system that recovers from a round of major damage, might be especially sensitive after a first round of therapy.
With this concept in mind, we are aiming to prevent the development of aggressive prostate cancer by looking at the molecular profile of prostate cancer cells after androgen removal in mice, using DNA chips provided by our Genomics Core Laboratory. We use computer software to find needles in a haystack — the perhaps tens of genes, out of tens of thousands, that may be a characteristic signature of how prostate cancer reacts to such therapy. We hope this will lead us to an Achilles’ heel to target to avoid recurrence. It’s a long-term effort but the idea is to arrive computationally at the best therapeutic intervention.
Overall, what’s been most rewarding for me during my short time here is the opportunity not just to predict the behavior of biological systems, but hopefully to help improve the quality of people’s lives. The dream that started with Max Delbrück’s advice is now within reach.