DNA COMPUTER-The Future of Computing
DNA computing is a form of computing which uses DNA and 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.
In 1994, Leonard Adleman introduced the idea of using DNA to solve complex mathematical problems. Adleman, a computer scientist at the University of Southern California, came to the conclusion that DNA had computational potential after reading the book “Molecular Biology of the Gene,” written by James Watson, who co-discovered the structure of DNA in 1953. Adleman is often called the inventor of DNA computers. His article in a 1994 issue of the journal Science outlined how to use DNA to solve a well-known mathematical problem, called the directed Hamilton Path problem, also known as the “traveling salesman” problem. The goal of the problem is to find the shortest route between a number of cities, going through each city only once. As you add more cities to the problem, the problem becomes more difficult. Adleman chose to find the shortest route between seven cities.
The Essay on The Problems Of Mexico City
The Problems of Mexico City The principal problems now facing Mexico City are its overcrowding and overpopulation, its dangerous environment, its dwindling water supply, and its terrible air pollution. One of Mexico City's problems is that it has an enormous population that continues to rapidly increase every day. There are many people that move to Mexico City and many people that are born there ...
Now let us see how the problem is solved by DNA Computation. The Problem is illustrated in the picture.
Thus we hear how it is being solved in Adleman’s own words.
“To simplify the discussion here, consider the map which contains just four cities—Atlanta, Boston,Chicago and Detroit—linked by six flights. The problem is to determine the existence of a Hamiltonian path starting in Atlanta and ending in Detroit.I began by assigning a random DNA sequence to each city. In our example,Atlanta becomes ACTTGCAG, Boston TCGGACTG and so on. It was convenient to think of the first half of the DNA sequence as the first name of the
city and the second half as the last name. So Atlanta’s last name is GCAG,whereas Boston’s first name is TCGG.Next, I gave each nonstop flight a DNA“flight number ,” obtained by concatenating the last name of the city of origin with the first name of the city of desti-nation. In the example, the Atlanta-to-Boston flight number becomes GCAGTCGG.Recall that each strand of DNA has its Watson-Crick complement. Thus, each city has its complementary DNA name. Atlanta’s complementary name becomes, for instance, TGAACGTC.After working out these encodings, I had the complementary DNA city names and the DNA flight numbers synthesized.
(As it turned out, the DNA city names themselves were largely unnecessary.) I took a pinch (about 1014 molecules) of each of the different sequences and put them into a common test tube. To begin the computation, I simply added water—plus ligase, salt and a few other ingredients to approximate the conditions inside a cell. Altogether only about one fiftieth of a teaspoon of solution was used. Within about one second, I held the answer to the Hamiltonian Path Problem in my hand.To see how, consider what transpires in the tube.
For example, the Atlanta-to-Boston flight number (GCAGTCGG)and the complementary name of Boston(AGCCTGAC) might meet by chance.By design, the former sequence ends with TCGG, and the latter starts with AGCC. Because these sequences are complementary, they will stick together . If the resulting complex now encounters the Boston-to-Chicago flight number (ACTGGGCT), it, too, will join the complex because the end of the former (TGAC) is complementary to the beginning of the latter (ACTG).In this manner,complexes will grow in length, with DNA flight numbers splinted together by complementary DNA city names. The ligase in the mixture will then permanently concatenate the chains of DNA flight numbers. Hence,the test tube contains molecules that encode random paths through the different cities (as required in the first step of the algorithm).Because I began with such a large number of DNA molecules and the problem contained just a handful of cities, there was a virtual certainty that at least one of the molecules formed would encode the Hamiltonian path. It was amazing to think that the solution to a mathematical problem could be stored in a single molecule! Notice also that all the paths were created at once by the simultaneous interactions of literally hundreds of trillions of molecules. This biochemical reaction represents enormous parallel processing.
The Term Paper on DNA Based Cryptography
ABSTRACT The biological research in the field of information technology paves the exploitation of storing capabilities, parallelism and also in conservative cryptography which enhances the security features for data transmission. DNA is the gene information which encodes information of all living beings. Though the DNA computing has its application in the field of huge information storage, massive ...
For the map, there is only one Hamiltonian path, and it goes through Atlanta, Boston, Chicago and Detroit, in that order.Thus,the molecule encoding the solution will have the sequence GCAGTCGGACTGGGCTATGTCCGA.Unfortunately, although I held the solution in my hand, I also held about 100 trillion molecules that encoded paths that were not Hamiltonian. These had
to be eliminated. To weed out molecules that did not both begin with the start city and terminate with the end city, I relied on the polymerase chain reaction (PCR).”
This DNA computer has lots of promising uses even though it’s in its very early research stages. Some of its very popular uses in the field of biology could be:
1) Some scientists predict a future, where our bodies are patrolled by tiny DNA computers that monitor our well-being and release the right drugs to repair damaged or unhealthy tissue.
2) DNA Computer can detect the presence of diagnostic markers for cancer and release a suitable cancer treatment molecule. So far, the molecular computer has only been trailed in test tubes, but ultimately it could find a use inside the body. Our medical computer might one day be administered as a drug and distributed throughout the body by the blood stream to detect disease markets autonomously and independently in every cell. In this way a single cancer cell could be detected and destroyed before the tumor develops. Even in a late stage cancer, this kind of treatment could reach every secondary growth, however small and effectively terminate the disease.
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DNA computers show promise because they do not have the limitations of silicon-based chips. For one, DNA based chip manufacturers will always have an ample supply of raw materials as DNA exists in all living things; this means generally lower overhead costs. Secondly, the DNA chip manufacture does not produce toxic by-products.
Last but not the least, DNA computers will be much smaller than silicon-based computers as one pound of DNA chips can hold all the information stored in all the computers in the world. With the use of DNA logic gates, a DNA computer the size of a teardrop will be more powerful than today’s most powerful supercomputer.
A DNA chip less than the size of a dime will have the capacity to perform 10 trillion parallel calculations at one time as well as hold ten terabytes of data. The capacity to perform parallel calculations, much more trillions of parallel calculations, is something silicon-based computers are not able to do. As such, a complex mathematical problem that could take silicon-based computers thousands of years to solve can be done by DNA computers in hours. For this reason, the first use of DNA computers will most probably be cracking of codes, route planning and complex simulations for the government.
But the DNA Computer has some drawbacks.Firstly,DNA computer takes much time to solve simple problems when compared to traditional silicon computers.Secondly,DNA computers take longer time to sort out the answers to a problem than it took to solve the same problem and finally sometimes there may be an error in the pairing of nucleotides present in the DNA strands.
DNA computing field is in its infancy stage, still the applications of this technology is not fully understood. This field as any other field also has got many obstacles and drawbacks.
