While looking for alternative technologies to overcome the limitations of modern computing, researchers hit on the use of biological systems that could be imitated by computer systems. This was the genesis of DNA computing, which has brought about a variety of potential applications. From security to nanotechnology , where tiny DNA-based devices are programmed to repair cells in patients suffering from pathological illness.
The past two decades have seen massive progress in the field of information and computing technology. Consequently the applications of computing , and indeed the computer itself , have become a necessary thread in the fabric of our lives. The foundation on which computing is based , namely conventional silicon–based semiconductor technology, has so far been able to sustain the rate of growth predicted by Moore’s Law.
Moore’s Law postulates that the speed of the computing chip will double roughly every eighteen months.
The most progress so far has been the result of fine-tuning testing fabrication capabilities and packing chips with smaller and smaller transistors. But it is a bottleneck in terms of bizarre quantum phenomena at atomic size –scales that have to be achieved to sustain Moore’s predictions. Researches in the field have always concentrate on alternative technologies also.
This quest for new paradigms in computing led to birth of computer systems based on biological systems. This mode of computing is significantly different from using information technology to solve complex biological problems. Rather, it involves the use of biological molecules such as DNA, DNA analogues and RNA to perform the arduous task of computing by employing complex biological process analogues to the underlying computation.
WHAT IS DNA ?
Prior to delving into the principles of DNA computing, it becomes necessary to explore fundamentals of molecular biology. All organisms on this planet are made of same genetic blueprint. Coding of this decides our physical makeup-from colour of our eyes to whether we are human. In human there are trillions of cells and within every nucleus of each cell there are tightly coiled threads of deoxyribonucleic acid (DNA) .
DNA is a double stranded helix of nucleotides that carries the genetic information of a cell. This information is the code used within cells to form proteins and is the building block upon which life is formed. The complete set of instructions for ‘building’ an organism is called the “genome”. It contains the master blueprint for all cellular structures and activities for the lifetime of the cell or organism.
Strands of DNA are long polymers of millions of linked nucleotides .These nucleotides consist of one of four nitrogen bases, a five-carbon sugar and a phosphate group. The nucleotides that make up these polymers are named after nitrogen bases that comprise it, namely ,Adenine(A), Cytosine(C) , Guanine(G) and Thymine(T).These nucleotides only combine in such a way that C always pairs with G, and T always pairs with A . The two strands of DNA molecule are anti-parallel in that each strand runs in an opposite direction.
The particular order of the bases arranged along the sugar-phosphate backbone is called the DNA sequence and the combination of the four nucleotides in the estimated million long polymer strands results in billions of combinations within a single DNA double helix. These massive amounts of combinations allow us to differentiate between every living thing on the plane- from the large scale (mammals) to the small scale (differences in human hair colour).
Each time a cell divides into two daughter cells, its full genome is duplicated. For humans and other complex organisms, this duplication occurs in the nucleus. Strict base-pairing rules are adhered to. Adenine will pair only with thymine(an A-T pair) and cytosine with guanine (a C-G pair).Each daughter cell rules ensure that the new strand is an exact copy of the old one. This the minimises the incidences of errors(mutations) .That may affect the resulting organism or its offspring.
Each DNA molecule contain many genes, the basic physical and functional hereditary units. A gene is a specific sequence of nucleotides bases whose sequences carry the information required for constructing proteins, which provides the structural components of cells and tissues as well as enzymes for essential biochemical reactions. The nucleus of most human cells contains two sets of chromosomes , one set given by each parent. Chromosomes contain roughly equal parts of protein and DNA.
The mystery of DNA and its construction is slowly being unraveled through mathematical means. These biological phenomena involving DNA have given computing researchers the much needed food for thought to apply these concepts to fashion the entirely new field of DNA computing , which finds exciting potential applications from security (encryption-decryption standards )to improving the quality of life by programming tiny DNA-based therapeutic devices to repair cells in patients who may be suffering from debilitating pathological conditions.
DNA‘S GREAT PROMISE-
MASSIVE PARALLELISM AND DATA DENSITY :-
Basically computers can deal with computational work in two ways : through serial computation and parallel computation.
Serial computation means that the computer deals with computational work in sequence , one by one , so to speak and is the process employed by most conventional silicon-based or ‘von Neumann computers’. Parallel computation means that computers deal with a lot of computational tasks simultaneously. Apart from obvious advantages that massively parallel computing provides, DNA has one more important feature that lends to being use it as computing medium. When compared to silicon, which is the material of choice in today’s computers , 1 base of DNA measures 0.35nm , lending it a data density of 18 Mega bits per square inch. This in turn implies that 10^6 Giga bits of information can be stored per square inch. Comparing this with conventional hard drives that have a data density of 7 Giga bits per square inch, we instantly see that DNA has a higher data capacity by a factor of 10^5. So one gram of DNA can hold enough information to completely fill one billion CD’s.
Nature has also protected DNA against errors by the process of ‘exactcopy‘ complementary strands-a process that is analogous to the Redundant Array of Inexpensive Disks(RAID) , which employs two disks to reduce errors in the event that either fails.
Given these two salient points in its favour-parallelism and data density-it is quite natural that researchers in the field of DNA computing have been enjoying a certain degree of optimism.
ORIGINS OF DNA COMPUTING :-
With an appropriate setup and enough DNA we can potentially solve huge problems by parallel search. This means that we can attempt every solution to a given problem until we come across the right one through random calculations. Using DNA for those type of computation can be much faster than using a conventional computer, for which massive parallelism would require large amounts of hardware , not simply more DNA.
Dr Leonard Adleman , professor at the University of southern California is recognized as a father of DNA computing.
In early 1994,Dr Adleman put his theory of DNA computing to the test on a problem called the Hamiltonian Path problem that is popularly called as the ‘Travelling Salesman Problem’. The ‘salesman’ in this problem has a map of several cities that he must visit to sell his wares where these cities have only one-way streets between some but not all of them. The crux of the problem is that the salesman must find a route to travel that passes through each city (A through G) exactly once , with a designated beginning and end.
The salesman wants to make efficient use of his time and does not want to backtrack or double back on a path he has taken previously.
Mathematicians call this type of problem a non-deterministic polynomial time problem (NP). The idea of guessing the right answer to a problem, or checking all possible problems in parallel to determine which is correct, is called non-determinism. An algorithm that works in this manner is called a non-deterministic algorithm and any problem with such an algorithm that runs on a non0deterministic machine in polynomial time is called a non-deterministic polynomial time problem.
The NP problem was chosen for Dr Adleman’s DNA computing test, as it is a type problem that is difficult for conventional computers to solve. The parallel computing of a DNA combination suited for NP problem solving.
Dr Adleman, using a basic seven-city 13-street model for the Travelling Salesman Problem, created randomly-sequenced DNA strands 20 bases long to chemically represent each city’s strand halfway to represent each street.
Adleman’s DNA computing representation of the ‘Travelling Salesman problem’.
By placing a few grams of every DNA city and street in a testtube and allowing the natural bonding tendenccies of the DNA building blocks to occur ,the DNA bonding created over 10^6 answers answers in less than one second. Ofcourse not all of the answers that came in one second were the correct answers as Dr Adleman only needed to keep those paths that exhibited the following properties:
•The path must starts at city A and end at city G.
•Of those paths ,the correct paths must pass through all seven
cities at least once.
• The final Path(s) must contain each city in turn.
The correct answer was determined by filtering the strands of DNA according to their end-bases to determine which strands began from city A and ended with city G and discarding those that did not..
ADVANTANGES OF DNA COMPUTING :-
Speed :
Combining DNA strands as demonstrated by Dr Adleman, made computations equivalent to 10^9 or better,arguably over 100 times faster than fastest computer.
Minimal storage requirements :
DNA stores memory at a density of about one bit per cubic nanometer where conventional storage media requires 10^12 cubic nanometers to storage one bit.
Minimal power requirements :
No power is required for DNA computing while computation is taking place. The chemical bonds that are the building blocks of DNA happen without any outside power source.
DNA APPLICATIONS
DNA plays a key role in following security applications.
DNA Cryptography
Scientists of Duke University published a paper entitled ‘DNA-based Cryptography’a which argues that the high-level computational ability and incredibly compact information storage media of DNA computing has the possibility of DNA-based cryptography based on the one-time pads , limited to the confines of conventional electronic media , whereas small amounts of DNA can suffice for a huge one-time pad for use in public key infrastructure(PKI).
DNA Steganography:
Steganography is a variety of encryption that completely hides text or graphics usually unencrypted ,within other text or graphics that are electronically transmitted.
The term “steganography” derives from the Greek words steganos (hidden) and graphein (to write).
This method uses a simple code to convert the letters of alphabet into combinations of the four bases that make up DNA and create a strand of DNA based on that code. A piece of DNA spelling out the message to be hiddden is syntetically created which contains the secret encrypted message in the middle plus short marker sequences at the ends of the message. The encoded piece of DNA is then placed into a normal piece of human DNA, which is then mixed with DNA strands of similar length. The mixture is then dried onto a paper that can be cut up into microdots ,with each dot containing billions of strands of DNA. Not only is the microdot difficult to detect on the plain message medium but also only one strand of those billions within the microdot contains the message.
But the problems with DNA cryptography are evident in DNA steganography as well as. The ‘test tube’ environment used in this type of steganography is far from practical for everyday use.
DNA Limitations :-
Volume Complexity :
Dr Adleman speculated the more useful instances of the Hamiltonian path could be solved in linear time with a manageable volume of solution. Later conclude that , due to exponential growth of the number of paths with an increase in the number of vertices , the required mass of the solution for a graph with 200 vertices would exceed 3*10^25 kg! While other algorithms may be more efficient in terms of volume complexity.
Data representation :
One of the related problems with DNA computing is that there is no universal method of data representation. In today’s computer systems binary representation is universally agreed. DNA computing has no such standard because it does not have an operation to extract a strand if it has a particular value at a particular position.
No efficient Implementation :
One of the biggest problem facing the field of DNA computing is that no efficient implementation has been produced for testing, verification, and general explanation. The reason for this is that the resources required to execute these algorithms are both expensive and hard to find. DNA computing has a high incremental cost both in time of the operators and the raw materials that it uses.
Despite these difficulties, there still a number of researchers working on topics related to DNA computing.
Future of DNA computing :-
In spite of all these limitations ,we believe that DNA computing still has an important role to play and recent advances in the field of biomedical nanotechnology and the human genome project will provide an impetus to DNA computing researchers who seek to gain a better understanding of certain pathological conditions that currently afflict the human race.
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