Director, CCD
Professor of Biology
WE DON'T RENT PIGS!1
It's only a BB gun. The one I had at age 4 triggered my lifelong fascination with Newton's laws. The only gun we use now at this CCD is a Bio-Rad Blaster System that shoots gold particles coated with nucleic acids into worms. Some survive to lay steady supplies of golden eggs. Living animals were fascinating, but when I studied biology as a freshman at The Johns Hopkins University, no one then, in 1962, seemed to know with any rigor what principles explained how molecules animated living cells. I switched to theoretical mechanics and mathematics, in which I got B.E.S. and PhD degrees. I was among the first students given access to digital computers, learning to program in Fortran and various assembly languages a year before Johns Hopkins took possession of its first IBM 7090 computer; Electrical Engineering Professor William Huggins, rather than any computer, compiled the code students wrote on paper forms. He flagged errors with a red pencil, but could not of course execute the code. When the computer arrived, excitement overflowed because, for the first time in history, it was possible to solve the complex, often non-linear, systems of differential equations that Newton, Leibnitz, Euler, Cauchy, LaGrange, Maxwell etc. conceived to represent mathematically how each inanimate speck of the world 'knows what to do next'. Until then, we simply stood in awe of those monumental equations or solved them analytically for cases so special they seemed silly. Then, abruptly, anyone with patience to punch programs into card decks could compute how the present state of a system would evolve into the future given one's mechanistic assumptions. This was all intellectually beautiful, for its rigor and elegance, yet boring because it made little contact with the mystery of how lifelike cellular actions, i.e. life itself, springs from interactions among the myriad inanimate chemicals constituting a cell. Mechanics was only part of the story, though a central part. Only by exerting coherent forces can it move by changing its shape or the arrangement of its parts, regardless of what 'it' is - regardless, in particular, of whether 'it' is alive.
Gradually it became apparent that mathematical models, solved by computers, could amplify a detailed knowledge of how molecular parts interacted into explanations of how biological mechanism works its wonders. It was equally apparent that in the 60's and 70's such facts were scarce, and often wrong. But, when the digital computer arrived, excitement overflowed so copiously that no mere ignorance of the underlying facts could deter a generation of theoretical biologists from building beautiful and provocative explanations of how embryos develop, how cells move and divide, etc. Absent facts, factoids sprung up to under gird mathematical/computer models impressive for their new power to simulate complex systems, predicting emergent consequences. Those early proofs of concept suffer little from resting on foundations of faulty facts, although biologists may have taken early parables too literally.
I took one of the two biology courses I have ever taken as a kind of a fling after graduate school, prior to buckling down to teach pure and applied mathematics and computer science at Rensselaer Polytechnic Institute for the next 13 years. This was the "physiology" course in 1973 at the Woods Hole MBL, comprising mostly biochemistry. I was the token mathematician in the course that year. It changed my life even though it was all I could do to learn the countless new biology nouns and a few concepts. Brian McCarthy taught us how to extract DNA. It was so stable even mathematicians could gather it like cotton candy out of a toxic vat that smelled as if it could stabilize anything from any planet. It was not clear what one ought do with DNA, but we certainly got great gobs of it. Gel electrophoresis could separate proteins. With the TEM you could see almost everything, dead, in baffling detail. Perfection of video-enhanced-contrast microscopy was on the horizon for resolving detail in living cells. Excited guest lecturers told of the recent discovery of 'microfilaments', defined functionally as filaments capable of causing contraction of non-muscle cells. Other excited lecturers told us about the new mystery mushroom drug called cytochalasin, defined by its ability to ruin microfilaments. They brought precious aliquots. It stopped contractions! Actual experiments fortified circular reasoning, already the most perfect reasoning. No one knew if cytoplasmic actin lurked, but many people already believed in microtubules.
Here's what this meant to me in 1973. The avalanche of biology nouns, new and strange to me, implied knowledge by biologists of an equally numerous heap of precisely identified molecular parts that actually existed and whose interaction properties would someday be known. If facts could replace factoids, the same math/computer modeling techniques that generated, rigorously, fantasies from factoids, could equally well produce, from true molecular facts, real explanations of how molecules animate cells, and thence how leaderless swarms of autonomous cells self-organize to form embryos that grow up to squirt the genes responsible for their behavior into the future.
What I do scientifically to try to connect math/computer models ever more tightly to experimentally discovered and characterized molecular details has always been so much fun that I would do it with all my energy even if I had to pay, rather than be paid, to do it. Yet anyone could find many different enterprises equally fun and equally challenging. The fun plus pay feature cannot be the real motive for spending one's life on one quest rather than another.
While it is fun here, and the scenery is peerless, that is not what makes this CCD throb. Instead, it's this. The real biological facts are arriving in overwhelming abundance. We can change the genomes of study organisms easily and quickly and observe the emergent consequences. Computers are now astonishingly fast and cheap, with vast storage capacity. Computer languages such as Java are up to the task of translating mathematical formalities into object-oriented code that works. New computer-controlled optics and new fluorescent probes let us see in live cells what cell-level actions actually emerge from the self-organizing molecules that genes encode - let us see what actions should therefore emerge from formal models based on classic dynamical systems theory. We have in hand, somewhere in the reductionist fact avalanche, solid foundation blocks for those models. We have the computer power to connect one to the other. Just possibly, combining these tools and empirical discoveries, the generation of scientists now in their 18's - 30's will be the first to conceive and popularize a creation myth as sublimely beautiful as the ancient ones, but differing from them by being true. If you could participate in that, what beckoning calls from other choices of career or amusement would even be audible?
It's still early days on a very long quest. It will take the life's work of thousands of scientists working across the world to do it. We aspire here at the CCD only to contribute. If it succeeds, the side effect NIH hopes for will be that we will come to understand complex biological systems so deeply that we can fix them when they break. Another consequence will be that this generation's longest-remembered achievement will be that, collectively, we figured it out.