DNA computing is a form of computing which uses DNA and molecular biology, instead of the traditional silicon-based computer technologies.
This field was initially developed by Leonard Adleman of the University of Southern California. In 1994, Adleman demonstrated a proof-of-concept use of DNA as form of computation which was used to solve the seven-point Hamiltonian path problem. Since the initial Adleman experiments, advances have been made, and various Turing machines have been proven to be constructable.
There are works over one dimensional lengths, bidimensional tiles, and even three dimensional DNA graphs processing.
On April 28, 2004, Ehud Shapiro and researchers at the Weizmann Institute announced in the journal Nature that they had constructed a DNA computer. This was coupled with an input and output module and is capable of diagnosing cancerous activity within a cell, and then releasing an anti-cancer drug upon diagnosis.
DNA computing is fundamentally similar to parallel computing in that it takes advantage of the many different molecules of DNA to try many different possibilities at once.
For certain specialized problems, DNA computers are faster and smaller than any other computer built so far. But DNA computing does not provide any new capabilities from the standpoint of computational complexity theory, the study of which computational problems are difficult. For example, problems which grow exponentially with the size of the problem (EXPSPACE problems) on von Neumann machines still grow exponentially with the size of the problem on DNA machines. For very large EXPSPACE problems, the amount of DNA required is too large to be practical. (Quantum computing, on the other hand, does provide some interesting new capabilities).