Trained as a chemist, mentored in theoretical molecular biology by Peter Schuster (Vienna), educated in evolutionary biology by Leo Buss (Yale), self-taught in computer science and charmed by the social sciences through John Padgett (Chicago), I have straddled many divides that are now coming together naturally. I have taken risks in pursuing a professional trajectory shaped by the desire for a broadly engaging cross-disciplinary environment more than by career safety. This led to my decision of resigning tenure at the University of Vienna (1994-1998) to join the Santa Fe Institute on a term-limited six year position (1998-2004).
I moved to Harvard Medical School in September 2004, attracted by a vision of systems biology that emphasized evolution and molecular physiology. A theoretician for 16 years, I was transformed by living for a while among molecular biologists and seeing the opportunities that quantitative thinking and technology bring to experimental biology. I started a group with a theoretical and an experimental component.
The experimental effort is focused on aging in C.elegans. The phenomenon of aging raises questions about the limits of biological processes. What type of damage, or "garbage", and how much of it, is generated as a by-product of which molecular processes? Is this damage inescapable? What can be repaired and at what cost? Aging is also a life history trait that has been shaped by evolution. This raises the question about the plasticity of aging. Aging research seems to be lagging other areas of molecular biology in adopting a more quantitative and theoretically founded approach.
The challenge of systems biology is not only experimental in kind. It also is the challenge of reasoning about facts that are rapidly evolving while remaining highly fragmented across research communities. I see a fundamental role for models as reasoning instruments in biology. Models, not databases as we know them today, will become the main vehicles for the computer-assisted storage, communication, and retrieval of biological knowledge. Computer scientists Vincent Danos, Jean Krivine, Jerome Feret, and I have joined forces with several other researchers to design a computational environment that represents biological knowledge, as it pertains to signaling, in an editable and executable fashion. This instrument will lend itself to the collaborative construction and critique of models.