Dear friends,
I typed this out from a book. I feel that this would explain the reason for our major.

The advice to computer scientists is twofold. First, learn as much real biology as possible – read biology texts and journals (Science, Nature, and Trends in Genetics are particularly good for a broad view), attend biology talks and conferences, talk extensively to biolo­gists, and seriously consider computational problems and abstract models already defined by biologists. But, second, do not be limited by the already formalized computational problems and established models, and do not be discouraged if you cannot immediately incorporate the full biological complexity into your formalized problems.
After immersion into real biology, try to frame and explore your own questions (partic­ularly biology-driven questions) guided by your understanding of the biology, by the goal of ultimately solving the “full problem”, and by your understanding of what is computa­tionally feasible and infeasible. Computer science will make the most serious contribution to biology after the emergence of a large community of people who understand both fields and who know the strengths and needs of both. I am not suggesting that you ignore prob­lems posed by biologists, but rather that you augment or modify them with your own questions. To quote from Leroy Hood “As computer scientists come in knowing more and more biology, they will chart their own course. Once they get into it, we don’t tell them what to do or where to go. They just take off” [280].
In the long run, a community of biology-educated computer scientists will make the largest impact by proceeding the same way that molecular biology proceeds – by picking problems and research approaches that best suit available and potential techniques; by de­veloping new techniques that one believes will be valuable, even if they do not perfectly fit existing problems; by focusing on model organisms (model computational problems); by framing manageable, somewhat simplified problems where progress can be made; and by working as a community to build on each others’ results, incrementally adding in more real­istic features of the biology-driven problem. That indirect and incremental approach is well accepted in both computer science and biology, but it will generate many silly, overly ideal­ized computational problems along the way, and these may be disappointing to some peo­ple. However, this approach is realistic. It will be more likely to succeed than will premature frontal attacks on the most difficult computational problems for the same reasons that fun­damental research on model organisms in biology is often a more productive, but more indirect way to obtain practical insights to problems arising in more complex organisms. It is instructive to realize that less than one percent of all known bacteria have been successfully cultured in the lab, creating a huge bias in what laboratory biologists have focussed on. And yet, studies of that one percent have lead to profound discoveries. Looking intensely under a lamppost, while also trying to expand its beam, is often both a necessary and a successful strategy.
I offer one more piece of advice to theoretical computer scientists. Focus more on the biological quality of a computation, and not exclusively on speed and space improvements. Because the formalization of a biology-driven problem may be difficult, the biological quality of the computed results must be examined, possibly requiring successive changes in the formal problem. The skills of computer scientists can be very important in iterating ­this process (finding practical solutions to each of the succeeding formalized problems). Making each step practical may be more important than optimally speeding any given step.

In summary, learn real biology, talk extensively to biologists, and work on problems of known importance to biology. But, in addition, try to become your own consultant. Guided by real biology, frame your own manageable, technical questions. Be ­willing to take the criticism that many of these questions will be too idealize to be of ­immediate practical use, knowing that in the long run, this incremental approach (with its many failures) is how a research community best addresses difficult problems. Most of all, be curious, have fun, and appreciate the wonderful opportunities you have to work in such an exciting and important field.
Dan Gusfield

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