First, an explanation:
This page is the product of a Biology project for Harvey Mudd College, a small science and engineering school in Southern California. Our interest in DNA computing was sparked by the numerous articles we uncovered on the subject as well as our innate interest in the future of computing and biology. We hope you find the following information useful and accessible. Enjoy!
DNA computing, in the literal sense, is the use of DNA (Deoxyribose Nucleic Acid) molecules, the molecules which encode genetic information for all living things, in computers. This is accomplished in a suspended solution of DNA, where certain combinations of DNA molecules are interpreted as a particular result to a problem encoded in the original molecules present. DNA computing is currently one of the fastest growing fields in both Computer Science and Biology, and its future looks extremely promising.
First and foremost, DNA computing is useful because it has a capacity lacked by all current electronics-based computers: its massively parallel nature. What does this mean, you ask? Well, essentially while DNA can only carry out computations slowly, DNA computers can perform a staggering number of calculations simultaneously; specifically, on the order of 10^9 calculations per mL of DNA per second! This capability of multiple cotemporal calculations immediately lends itself to several classes of problems which a modern electronic computer could never even approach solving. To give you an idea of the difference in time, a calculation that would take 10^22 modern computers working in parallel to complete in the span of one human's life would take one DNA computer only 1 year to polish off!
The scientists at the forefront of the DNA computer revolution are a brilliant breed indeed. It was all started by a professor of Computer Science at USC by the name of Leonard M. Adleman, who utilized recombinant DNA to solve a simple Hamiltonian path problem which is classified as NP-complete. His article in the Nov. '94 issue of Science magazine, Molecular Computations of Solutions to Combinatorial Problems touched off the current wave of interest in molecular computation. The Hamiltonian path problem, on a large scale, is effectively unsolvable by conventional computer systems (it's theoretically possible, but would take an extremely long time).
His work was picked up by Dr. Donald Beaver, among others, who analyzed the approach and organized it into a highly accessible web page which includes a concise annotated bibliography. One major contributor to this page is the research group of Dr. Richard Lipton, Dan Boneh, and Christopher Dunworth- a professor of Computer Science and two graduate students at Princeton University. They are currently using a DNA Computer to break the government's data encryption standard (DES), as described in the article Breaking DES Using a Molecular Computer.
Currently, molecular computing is a field with a great deal of potential, but few results of practical value. In the wake of Adleman's solution of the Hamiltonian path problem, there came a host of other articles on computation with DNA; however, most of them were purely theoretical. Currently, a functional DNA "computer" of the type most people are familiar with lies many years in the future. But work continues: in his article Speeding Up Computation via Molecular Biology Lipton shows how DNA can be used to construct a Turing machine, a universal computer capable of performing any calculation. While it currently exists only in theory, it's possible that in the years to come computers based on the work of Adleman, Lipton, and others will come to replace traditional silicon-based machines.
We (actually, Ben Elgin) used Hypercard to create a simulation of the procedure used to manipulate DNA to solve Hamiltonian path problems, much like Adleman's original experiment. Download a copy of the HyperCard stack DNAcomputer here. (System Requirements: Any Macintosh with a 640x400 or larger screen, Hypercard 2.x or Hypercard Player 2.x)
Last Modified 2/28/96 by the authors.
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