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( This feature for!) In 1994, Leonard M. Adleman solved an unremarkable computational problem with a remarkable technique. It was a problem that a person could solve it in a few moments or an average desktop machine could solve in the blink of an eye. It took Adleman, however, seven days to find a solution. Why then was this work exceptional? Because he solved the problem with DNA. It was a landmark demonstration of computing on the molecular level.
The type of problem that Adleman solved is a famous one. It's formally known as a directed Hamiltonian Path (HP) problem, but is more popularly recognized as a variant of the so-called 'traveling salesman problem.' In Adleman's version of the traveling salesman problem, or 'TSP' for short, a hypothetical salesman tries to find a route through a set of cities so that he visits each city only once. As the number of cities increases, the problem becomes more difficult until its solution is beyond analytical analysis altogether, at which point it requires brute force search methods. TSPs with a large number of cities quickly become computationally expensive, making them impractical to solve on even the latest super-computer. Adleman’s demonstration only involves seven cities, making it in some sense a trivial problem that can easily be solved by inspection. Nevertheless, his work is significant for a number of reasons.
It illustrates the possibilities of using DNA to solve a class of problems that is difficult or impossible to solve using traditional computing methods. It's an example of computation at a molecular level, potentially a size limit that may never be reached by the semiconductor industry. It demonstrates unique aspects of DNA as a data structure. It demonstrates that computing with DNA can work in a massively parallel fashion. DNA: A unique data structure The amount of information gathered on the molecular biology of DNA over the last 40 years is almost overwhelming in scope.
So instead of getting bogged down in biochemical and biological details of DNA, we'll concentrate on only the information relevant to DNA computing. The data density of DNA is impressive. Just like a string of binary data is encoded with ones and zeros, a strand of DNA is encoded with four bases, represented by the letters A, T, C, and G.
The bases (also known as nucleotides) are spaced every 0.35 nanometers along the DNA molecule, giving DNA an remarkable data density of nearly 18 Mbits per inch. In two dimensions, if you assume one base per square nanometer, the data density is over one million Gbits per square inch. Compare this to the data density of a typical high performance hard drive, which is about 7 Gbits per square inch - a factor of over 100,000 smaller. Another important property of DNA is its double stranded nature.
The bases A and T, and C and G, can bind together, forming base pairs. Therefore every DNA sequence has a natural complement. For example if sequence S is ATTACGTCG, its complement, S', is TAATGCAGC. Both S and S' will come together (or hybridize) to form double stranded DNA. This complementarity makes DNA a unique data structure for computation and can be exploited in many ways. Error correction is one example.
Errors in DNA happen due to many factors. Occasionally, DNA enzymes simply make mistakes, cutting where they shouldn't, or inserting a T for a G. DNA can also be damaged by thermal energy and UV energy from the sun. If the error occurs in one of the strands of double stranded DNA, repair enzymes can restore the proper DNA sequence by using the complement strand as a reference. In this sense, double stranded DNA is similar to a RAID 1 array, where data is mirrored on two drives, allowing data to be recovered from the second drive if errors occur on the first. In biological systems, this facility for error correction means that the error rate can be quite low. For example, in DNA replication, there is one error for every 10^9 copied bases or in other words an error rate of 10^-9.
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(In comparison, hard drives have read error rates of only 10^-13 for Reed-Solomon correction). Operations in parallel In the cell, DNA is modified biochemically by a variety of enzymes, which are tiny protein machines that read and process DNA according to nature's design. There is a wide variety and number of these 'operational' proteins, which manipulate DNA on the molecular level.
For example, there are enzymes that cut DNA and enzymes that paste it back together. Other enzymes function as copiers, and others as repair units. Molecular biology, Biochemistry, and Biotechnology have developed techniques that allow us to perform many of these cellular functions in the test tube. It's this cellular machinery, along with some synthetic chemistry, that makes up the palette of operations available for computation.
Just like a CPU has a basic suite of operations like addition, bit-shifting, logical operators (AND, OR, NOT NOR), etc. That allow it to perform even the most complex calculations, DNA has cutting, copying, pasting, repairing, and many others. And note that in the test tube, enzymes do not function sequentially, working on one DNA at a time.
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Rather, many copies of the enzyme can work on many DNA molecules simultaneously. This is the power of DNA computing, that it can work in a massively parallel fashion.
DNA computing may not be fast but it is massively parallel. With the right kind of setup, it has the potential to solve huge mathematical problems. It’s hardly surprising then, that DNA computing represents a serious threat to various powerful encryption schemes such as the Data Encryption Standard (DES). But if DNA can be used to break codes then it can also be exploited to encrypt data. Various groups have suggested using the sequence of nucleotides in DNA (A for 00, C for 01, G for 10, T for 11) for just this purpose. One idea is to not even bother encrypting the information but simply burying it in the DNA so it is well hidden, a technique called DNA steganography. But that all sounds to simple for Nang King, an independent researcher who today puts forward an entirely new approach based on the way in which information from DNA is processed inside cells.
The processing works in two stages called transcription and translation. In transcription, a DNA segment that constitutes a gene is converted into messenger RNA (mRNA) which floats out of the nucleus and into the body of the cell.
This happens only after the noncoding parts of the gene have been removed and the remaining sequences spliced back together. In translation, molecular computers called ribosomes read the information that mRNA carries and uses it to assemble amino acids into protein chains. This is a one way process. Information can be transferred from DNA to a protein but it cannot be converted back. There reasons are various.
How would this process know where to reinsert the noncoding regions of DNA that were originally cut out or what these noncoding sequences would have consisted of in the first place? Nang’s idea is that Alice encodes her message in the original DNA sequence and allows this to be transcribed and translated. The resulting protein is then like a public key which can be sent to Bob through a public channel. Meanwhile, Alice sends Bob the secret key which consists of the information he needs to reassemble the DNA such as the location of the noncoding regions that need to be reinserted. Nang says that this form of cryptography is surprisingly secure to a number of powerful attacks. But he also points out various weaknesses such as that the encryption becomes increasingly difficult if more complex keys are used. But it piques the interest for sure.
And as an additional weapon in the cryptographer’s armoury, it’s surely an idea worthy of further study. Ref:: A Pseudo DNA Cryptography Method.