8. Advantages of DNA Computers

8. Advantages of DNA Computers
1) Parallelism:-
“The speed of any computer, biological or not, is determined by two factors: (i) how many parallel processes it has; (ii) how many steps each one can perform per unit time. The exciting point about biology is that the first of these factors can be very large: recall that a small amount of water contains about 1022 molecules. Thus, biological computations could potentially have vastly more parallelism than conventional ones.”[6]
In November of 1994, Leonard Adleman published a dramatic reminder that computation is independent of any particular substrate. By using strands of DNA annealing to each other, he was able to compute a solution to an instance of the Hamiltonian path problem (HPP) (Figure 4). While working in formal language theory and artificial selection of RNA had presaged the concept of using DNA to do computation, these precursors had largely gone unnoticed in mainstream computer science. Adleman’s work sparked intense excitement and marked the birth of a new field, DNA computation [7].


The Hamiltonian Path problem.
The goal is to find a path from the start city to the end city going through every city only once.
The Hamiltonian Path problem is shown in Figure 3. To solve this problem Adleman used a non-deterministic algorithm (brute force method) to solve this problem. The main thinking of using DNA other than electronic computer to solve this problem is the parallelism of DNA operations. In fact, the real interesting thing on the DNA solution for the Hamiltonian path problems is that most input data grow just linearly with the growth of the number of edges.
That means it is almost impossible to solve this kind of problems (NP or NP-Compete) using a normal computer when the complexity of the problem grows because they must try each option one at a time. While, as for DNA based computers, just the quantity of DNA’s should grow exponentially but this is not a problem because the quantity of DNA’s for all known problems is small enough. (In reasonable concentrations, a liter of DNA solution can store up to 1022 bits of information [8]) They can try all of the options at the same time, determining possible solutions while weeding out wrong answers.
Let’s now look a little bit more deeply into the biochemical operation. 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 molecules at a time. Rather, many copies of the enzyme can work on many DNA molecules simultaneously. So this is the power of DNA computing that it can work in a massively parallel fashion.
2) Gigantic memory capacity:-
Just as we have discussed, the other implicit characteristic of DNA Computer is its gigantic memory capacity. Storing information in molecules of DNA allows for an information density of approximately 1 bit per cubic nanometer. The bases (also known as nucleotides) of DNA molecules, which represent the minimize unit of information in DNA Computers, are spaced every 0.34 nanometers along the DNA molecule (Figure 4), giving DNA a remarkable data density of nearly 18 Megabits per inch. In two dimensions, if you assume one base per square nanometer, the data density is over one million Gigabits per square inch. Compare this to the data density of a typical high performance hard driver, which is about 7 gigabits per square inch -- a factor of over 100,000 smaller [8]. Researchers from Pacific Northwest National Laboratory are tapping forces of nature to store information more permanently. The researchers used artificial DNA sequences to encode portions of the text of the children's song it's a Small World, added the sequences to bacteria DNA, allowed the bacteria to multiply, and then extracted the message part of a DNA strand and retrieved the encoded information. Because DNA is passed down through generations of living organisms, information stored this way should survive for as long as the line of organisms survives, said Pak Wong, a chief scientist at the Pacific Northwest National Laboratory.
Storing information is DNA's natural function, said Wong. "We [are] taking advantage of a time-tested, natural, nanoscale data storage technology perfected over the last 3 billion years." The encoding method could be used to store any digital information, he said. "Text, pictures, music -- anything you can send or receive over the Web could be saved in this form."
3) Low Power Dissipation:-
“The potential of DNA-based computation lies in the fact that DNA has a gigantic memory capacity and also in the fact that the biochemical operations dissipate so little energy,” says University of Rochester computer scientist Mitsunori Ogihara [10]. DNA computers can perform 2 x 1019 ligation operations per joule. This is amazing, considering that the second law of thermodynamics dictates a theoretical maximum of 34 x 1019 (irreversible) operations per joule (at 300K). Existing supercomputers aren’t very energy-efficient, executing a maximum of 109 operations per joule [11]. Just think about the energy could be very valuable in future. So, this character of DNA computers can be very important.
4) Suitable For Ambinatorial Problems:-
From the first day that DNA Computation is developed, Scientists used it to solve combinatorial problems. In 1994, Leonard Adleman used DNA to solve one of Hamiltonian Path problem -Traveling Salesman problem. After that Lipton used DNA Computer to break Data Encryption Standard (DES) [12]. And then much of the work on DNA computing has continued to focus on solving NP-complete and other hard computational problems. In fact, experiments have proved that DNA Computers are suitable for solving complex combinatorial problems, even until now, it costs still several days to solve the problems like Hamiltonian Path problems. But the key point is that Adleman's original and subsequent works demonstrated the ability of DNA Computers to obtain tractable solutions to NP-complete and other hard computational problems, while these are unimaginable using conventional computers.
5) Clean ,Cheap And Available:-
Besides above characteristics, clean, cheap and available are easily found from performance of DNA Computer. It is clean because people do not use any harmful material to produce it and also no pollution generates. It is cheap and available because you can easily find DNA from nature while it’s not necessary to exploit mines and that all the work you should do is to extract or refine the parts that you need from organism.
DNA processors are cheaper and cleaner than today's silicon-based microprocessors. DNA resources are also more readily available than traditional microprocessor's. The field is highly multidisciplinary, attracting a host of extremely bright computer scientists, molecular biologists, geneticists, mathematicians, physicists, and others. Because of DNA computer's massive parallel processing powers (about 10E20 computations a second), computations that would take years to be done on a conventioal computer could be computed in minutes. Certain operations in DNA computing are also over a billion times more energy efficient than conventional computers. DNA stores information at a density of about one bit per cubed nm—about a trillion times as efficiently as videotape. In addition to its potential applications, such as DNA computation, nanofabrication, storage devices, sensing, and healthcare, biocomputation also has implications for basic scientific research.