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An Oldie But a Goodie

© Adam Hughes

In this fast-paced technological world we live in, it doesn't take long for a product or idea to achieve "outdated" status. For illustration, just think about the last time you bought a computer. You were probably pretty excited because you got something very fast, with lots of memory, great graphics, and plenty of nifty gadgets. Then, a few months later, you saw an add for a new machine, about the same price, with features that made your pride and joy look like a dinosaur. This is a common scenario these days, not just with PC's, but with cell phones, televisions, software, and just about any technological artifact you could name. Luckily, though, scientific principles remain constant against the tides of change, and it is possible to reach back into our scientific archive and find material from the past with vibrant relevance to our present and future. Such is the case with a book chapter from Biocomputing : Informatics and Genome Projects, edited by D.W. Smith and published by Academic Press in 1994. The chapter, entitled "Computational Simulations of Biological Systems", and written by A.R. Galper and D.L. Brutlag (Department of Biochemistry, and the Section on Medical Informatics, Stanford University School of Medicine, Stanford California) is at least seven years old, but the ideas it presents still represent the bases for theoretical biological studies today.

The main thrust of the work presented by Galper and Brutlag is that, as more biological data becomes available and computing power and algorithmic sophistication increases, a theory-bases biology becomes more of a possibility than ever before. The authors contend that, while simulation has always been utilized in the biological field in some form or another, the process has always been somewhat backward from what is traditionally seen as the role of simulation. That is, while calculations are generally performed in physics or chemistry based on some theory or model, biological simulations analyze data and build a theory along the way. In general, this seems to be due to the large amounts of data generated in biology and also to the difficulty in recognizing and understanding the structures of biological theories which may form the basis for rigorous models to be developed. The bulk of the paper is dedicated to laying out proposals for how these structures might be categorized and developing potential simulation models. In addition, biological simulation methods are reviewed and a survey of some of the then-recent biological simulation research is presented.

Overall, this work is very thorough and provides a good background on where biological simulation has been and where it may be heading. To view the entire book chapter,

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