Dna Computing

DNA Computing

DNA computing is a form of computing which uses DNA, biochemistry and molecular biology, instead of the traditional silicon-based computer technologies. DNA computing, or, more generally, molecular computing, is a fast developing interdisciplinary area. R&D in this area concerns theory, experiments and applications of DNA computing. DNA computing is a novel and fascinating development at the interface of computer science and molecular biology. It has emerged in recent years, not simply as an exciting technology for information processing, but also as a catalyst for knowledge transfer between information processing, nanotechnology, and biology. This area of research has the potential to change our understanding of the theory and practice of computing.

Essentially, three classes of DNA computing are now apparent: (1) intramolecular, (2) intermolecular, and (3) supramolecular. The Japanese Project lead by Hagiya (Takahashi) focuses on intramolecular DNA computing, constructing programmable state machines in single DNA molecules, which operate by means of intramolecular conformational transitions. Intermolecular DNA computing, of which Adleman's experiment is an example, focusing on the hybridization between different DNA molecules as a basic step of computations. Finally, supramolecular DNA computing, as pioneered by Winfree, harnesses the process of self assembly of rigid DNA molecules with different sequences to perform computations.

Since Adleman's solution to the HPP (Adleman 1994), DNA and RNA solutions of some NP-complete problems, such as the 3-SAT problem, the maximal clique problem, and the knight problem were proposed. The power of parallel, high-density computation by molecules in solution allows DNA computers to solve hard computational problems such as NP-complete problems in polynomial increasing time, while a conventional Turing machine requires exponentially increasing time. However, all the current DNA computing strategies are based on enumerating all candidate solutions, and then using selection processes to eliminate incorrect DNA. This algorithm requires that the size of the initial data pool increases exponentially with the number of variables in the calculation. For example, to calculate a DNA solution of an NP-complete problem, the number of molecules in the solution