| Biomolecular computing |
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| Biomolecular computing is an emerging area in computer technology and is expected to supplement or even replace the current silicon computers in a decade or two if not in near future. The technology involves use of biological molecules to solve computational problems. Chemical and Biochemical Computers Defined In general terms, a chemical computer is one that processes information by making and breaking chemical bonds, and it stores logic states or information in the resulting chemical (i.e., molecular) structures. A chemical nanocomputer would perform such operations selectively among molecules taken just a few at a time in volumes only a few nanometers on a side. Proponents of a variant of chemical nanocomputers, biochemically based computers, can point to an existence for them in the common place activities of humans and other animals with multicellular nervous systems. Nonetheless, artificial fabrication or implementation of this category of natural, or biomimetic biochemically based computers seems far off because the mechanisms for animal brains and nervous systems still are poorly understood. In the absence of this deeper understanding, research on biochemically-based computers has proceeded in alternative directions. One alternative direction has been to adapt naturally occurring biochemicals for use in computing processes that do not occur in nature. The most important of these is the DNA Computers , though approaches like Bacteriorhodopsin based computer memmories and computers based on live neuronal cells are being tried. The information presented in these pages are basically related to DNA based computing and is a compilation of information available on the web. This is specifically for the purpose of introducing biologists into this amazing concept and the viewers are advised to visit the links given in the links to resources page for comprehensive information on DNA computing. |
| DNA computers DNA computing, also known as molecular computing, is a new approach to massively parallel computation based on ground-breaking work by Adleman. He used DNA to solve a seven-node Hamiltonian path problem, a special case of an NP-complete problem that attempts to visit every node in a graph exactly once. (This special case is trivial to solve with a conventional computer, or even by hand, but illustrates the potential of DNA computing. A DNA computer is basically a collection of specially selected DNA strands whose combinations will result in the solution to some problem. Technology is currently available both to select the initial strands and to filter the final solution. The promise of DNA computing is massive parallelism: with a given setup and enough DNA one can potentially solve huge problems by parallel search. This can be much faster than the conventional computer, for which massive parallelism would require large amounts of hardware, not simply more DNA. |
| Birge's Bacteriorhodopsin-Based Computer Memories There are other proposals, as well, to use biochemicals in computing. For example, Robert Birge of Syracuse University has suggested the use of the light-sensitive protein dye bacteriorhodopsin, that is produced by some bacteria. He and his collaborators have shown that it could provide a very high density optical memory that could be integrated into an electronic computer to yield a hybrid device of much greater power than a conventional, purely electronic computer. |
| The US-NRL-SAIC Prototype Bioelectronic computer In still a different approach to taking advantage of small-scale biochemical processes for computing, Dr. David Stenger of the U.S. Naval Research Laboratory in Washington, DC, and Dr. James Hickman of the SAIC Corporation in McLean, VA, are culturing and linking living neuronal cells to build a bioelectronic computer. They envision that such a computer would be well-adapted for pattern-recognition tasks. |
| Why DNA ? Genetic information in DNA is encoded and stored in form of specific sequences of the four bases adenine (A), cytidine (C), guanine (G), and thymine (T). Each DNA strand can hybridize specifically with another DNA strand of complementary sequence, following the rules that A pairs with T, and C pairs with G. Each DNA sequence can further be represented by its constituent DNA oligonucleotides; and in principle any unknown DNA sequence can be identified by hybridization to a DNA oligonucleotide of known sequence. These features make DNA a useful tool for computation, especially because of its massive parallelism when using a large pool of different DNA molecules. This parallelism would enable DNA to solve NP complete problems, e.g. hard SAT problems, in linear time. A crucial factor in DNA computation is the fidelity of the hybridization (the correct pairing of the four bases A, C, G, and T) of two DNA strands forming a double-helical strand. One of the major objectives of researchers working on DNA computing is to enhance the utility of DNA hybridization by encoding a DNA sequence ('target sequence') as another DNA sequence such that the target sequence is translated by a chosen algorithm into an encoded DNA sequence. For example, each base (A, C, G, or T) can be encoded by a base-triplet (e.g. A by CGT, C by ATG, ...) in either the standard or a non-standard DNA alphabet by using synthetic, non-traditional bases (e.g. X and k). This allows the recoding of DNA into a form which a) cannot hybridize to the original DNA strand or any other natural DNA molecule because of the presence of non-natural bases. Such an encoding even even allows us to map any DNA sequence into a novel binary code (e.g. A by XX, C by Xk, ...). Recoding enhances the reliability of hybridization in the following ways: - It can increase the redundancy of the target sequence, and also the Hamming distance. Thus a single hybridization error has a smaller effect on an encoded DNA sequence. - The fidelity of hybridization increases with the length of the DNA sequence, because longer DNA duplexes have higher melting temperatures . Thus the use of encoded DNA sequences enhances the fidelity of hybridization by employing DNA molecules longer than the original target sequences. - The melting temperature (the practical hybridization temperature) depends on the base composition of a DNA sequence, i.e. the higher the G/C content of a sequence, the higher is its melting temperature. Thus higher temperatures show a bias towards G/C rich DNA sequences. The use of encoded DNA allows one to circumvent this problem if the recoding algorithm is designed in such a way to minimize differences in melting temperature. This would be especially true for any binary code consisting of only X and k. - Expansion of the genetic code by introducing the nonstandard bases X and k also enhances DNA's ability to store large quantities of information. Since X and k show high specificity exclusivly for each other, the recoded DNA is isolated from interactions with natural DNA. - There is No competition in these experiments from electronic computers, since the computations are not digital. Applications of recoding DNA sequences are not limited to the field of DNA computers but also have direct consequences for DNA sequencing by hybridization, DNA fingerprinting, and mutation detection analysis |
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