Ajith Bio Molecular Computer

INTRODUCTION Molecular computing is an emerging field to which chemistry, biophysics, molecular biology, electronic engineering, solid state physics and computer science contribute to a large extent. It involves the encoding, manipulation and retrieval of information at a macromolecular level in contrast to the current techniques, which accomplish the above functions via IC miniaturization of bulk devices. The biological systems have unique abilities such as pattern recognition, learning, self-assembly and selfreproduction as well as high speed and parallel information processing. The aim of this article is to exploit these characteristics to build computing systems, which have many advantages over their inorganic (Si,Ge) counterparts.

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Who thought of this? DNA computing began in 1994 when Leonard Adleman proved that DNA computing was possible by finding a solution to a real- problem, a Hamiltonian Path Problem, known to us as the Traveling Salesman Problem, with a molecular computer. In theoretical terms, some scientists say the actual beginnings of DNA computation should be attributed to Charles Bennett's work. Adleman, now considered the father of DNA computing, is a professor at the University of Southern California and spawned the field with his paper, "Molecular Computation of Solutions of Combinatorial Problems." Since then, Adleman has demonstrated how the massive parallelism of a trillion DNA strands can simultaneously attack different aspects of a computation to crack even the toughest combinatorial problems. Adleman's Traveling Salesman Problem: The objective is to find a path from start to end going through all the points only once. This problem is difficult for conventional computers to solve because it is a "non-deterministic polynomial time problem" . These problems, when they involve large numbers, are intractable with conventional computers, but can be solved using massively parallel computers like DNA computers. The Hamiltonian Path problem was chosen by Adleman because it is known problem.

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The following algorithm solves the Hamiltonian Path problem: 1.Generate random paths through the graph. 2.Keep only those paths that begin with the start city (A) and conclude with the end city (G). 3.If the graph has n cities, keep only those paths with n cities. (n=7) 4.Keep only those paths that enter all cities at least once. 5.Any remaining paths are solutions.

The key was using DNA to perform the five steps in the above algorithm. Adleman's first step was to synthesize DNA strands of known sequences, each strand 20 nucleotides long. He represented each of the six vertices of the path by a separate strand, and further represented each edge between two consecutive vertices, such as 1 to 2, by a DNA strand which consisted of the last ten nucleotides of the strand representing vertex 1 plus the first 10 nucleotides of the vertex 2 strand. Then, through the sheer amount of DNA molecules (3x1013 copies for each edge in this experiment!) joining together in all possible combinations, many random paths were generated. Adleman used well-established techniques of molecular biology to weed out the Hamiltonian path, the one that entered all vertices, starting at one and ending at six. After generating the numerous random paths in the first step, he used polymerase chain reaction (PCR) to amplify and keep only the paths that began on vertex 1 and ended at vertex 6. The next two steps kept only those strands that passed through six vertices, entering each vertex at least once. At this point, any paths that remained would code for a Hamiltonian path, thus solving the problem. (Adleman)

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How do they work? DNA is the major information storage molecule in living cells, and billions of years of evolution have tested and refined both this wonderful informational molecule and highly specific enzymes that can either duplicate the information in DNA molecules or transmit this information to other DNA molecules. Instead of using electrical impulses to represent bits of information, the DNA computer uses the chemical properties of these molecules by examining the patterns of combination or growth of the molecules or strings. DNA can do this through the manufacture of enzymes, which are biological catalysts that could be called the 'software' used to execute the desired calculation. DNA computers use deoxyribonucleic acids--A (adenine), C (cytosine), G (guanine) and T (thymine)--as the memory units, and recombinant DNA techniques already in existence carry out the fundamental operations. In a DNA computer, computation takes place in test tubes or on a glass slide coated in 24K gold. The input and output are both strands of DNA, whose genetic sequences encode certain information. A program on a DNA computer is executed as a series of biochemical operations, which have the effect of synthesizing, extracting, modifying and cloning the DNA strands. Their potential power underscores how nature could be capable of crunching number better and faster than the most advanced silicon chips.

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Biomolecular Computers

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The only fundamental difference between conventional computers and DNA computers is the capacity of memory units: electronic computers have two positions (on or off), whereas DNA has four (C, G, A or T). The study of bacteria has shown that restriction enzymes can be employed to cut DNA at a specific word(W). Many restriction enzymes cut the two strands of double-stranded DNA at different positions leaving overhangs of single-stranded DNA. Two pieces of DNA may be rejoined if their terminal overhangs are complementary. Complements are referred to as 'sticky ends'. Using these operations, fragments of DNA may be inserted or deleted from the DNA. As stated earlier DNA represents information as a pattern of molecules on a strand. Each strand represents one possible answer. In each experiment, the DNA is tailored so that all conceivable answers to a particular problem are included. Researchers then subject all the molecules to precise chemical reactions that imitate the computational abilities of a traditional computer. Because molecules that make up DNA bind together in predictable ways, it gives a powerful "search" function. If the experiment works, the DNA computer weeds out all the wrong answers, leaving one molecule or more with the right answer. All these molecules can work together at once, so you could theoretically have 10 trillion calculations going on at the same time in very little space. DNA computing is a field that holds the promise of ultradense systems that pack megabytes of information into devices the size of a silicon transistor. Each molecule of DNA is roughly equivalent to a little computer chip. Conventional computers represent information in terms of 0's and 1's, physically expressed in terms of the flow of electrons through logical Govt. Engg. College, Thrissur

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circuits, whereas DNA computers represent information in terms of the chemical units of DNA. Computing with an ordinary computer is done with a program that instructs electrons to travel on particular paths; with a DNA computer, computation requires synthesizing particular sequences of DNA and letting them react in a test tube or on a glass plate. In a scheme devised by Richard Lipton, the logical command "and" is performed by separating DNA strands according to their sequences, and the command "or" is done by pouring together DNA solutions containing specific sequences, merging. By forcing DNA molecules to generate different chemical states, which can then be examined to determine an answer to a problem by combination of molecules into strands or the separation of strands, the answer is obtained. Most of the possible answers are incorrect, but one or a few may be correct, and the computer's task is to check each of them and remove the incorrect ones using restrictive enzymes. The DNA computer does that by subjecting all of the strands simultaneously to a series of chemical reactions that mimic the mathematical computations an electronic computer would perform on each possible answer. When the chemical reactions are complete, researchers analyze the strands to find the answer -- for instance, by locating the longest or the shortest strand and decoding it to determine what answer it represents. Computers based on molecules like DNA will not have a vonNeumann architecture, but instead function best in parallel processing applications. They are considered promising for problems that can have multiple computations going on at the same time. Say for instance, all branches of a search tree could be searched at once in a molecular system Govt. Engg. College, Thrissur

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while vonNeumann systems must explore each possible path in some sequence. Information is stored in DNA as CG or AT base pairs with maximum information density of 2bits per DNA base location. Information on a solid surface is stored in a NON-ADDRESSED array of DNA words(W) of a fixed length (16 mers). DNA Words are linked together to

form

large

combinatorial

sets

of

molecules.

DNA computers are massively parallel, while electronic computers would require additional hardware, DNA computers just need more DNA. This could make the DNA computer more efficient, as well as more easily programmable.

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PROCESSING AND STORING OF INFORMATION Nucleic Acids are used because of density, efficiency and speed. DNA molecules can store far more information than any existing computer memory chip. This means that DNA computing is a far denser packing of molecular information compared with silicon-based computers. A single bacterium cell measures just a micron square - about the same size as a single silicon transistor - but holds more than a megabyte of DNA memory and has all the computational structures to sense and respond to its environment. To try to put this in some understandable perspective, it has been estimated that a gram of DNA can hold as much information as a trillion CDs. So DNA molecules would be like mega-memory. In a biochemical reaction hundreds of trillions of DNA molecules can operate in parallel. DNA computers could store a bit, 0 or 1, of data in one cubic nano meter, one trillionth the size of the conventional computer's electronic storage. Thus a DNA computer could store massive quantities of information in the space a standard computer would use to store much less. A pound of DNA could contain more computer memory than all the electronic computers ever made. It would be about twice as fast as the fastest supercomputer, performing more than 2,000 instructions per second. DNA computers also require miniscule amounts of energy to perform. "We're interested in scale up. We believe that ... we can see scaling up within a few years by a factor of a trillion or more." (Lloyd Smith)

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Biomolecular Computers

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Because the biochemical operations involved are subject to errors and are often slow, rigorous tests of the accuracy and further technological development are needed. What about efficiency? In both the solid-surface glass-plate approach and the test tube approach, each DNA strand represents one possible answer to the problem that the computer is trying to solve. The strands have been synthesized by combining the building blocks of DNA, called nucleotides, with one another, using techniques developed for biotechnology. The set of DNA strands is manufactured so that all conceivable answers are included. Because a set of strands is tailored to a specific problem, a new set would have to be made for each new problem. Most electronic computers operate linearly--they manipulate one block of data after another--biochemical reactions are highly in parallel: a single step of biochemical operations can be set up so that it affects trillions of DNA strands. While a DNA computer takes much longer than a normal computer to perform each individual calculation, it performs an enormous number of operations at a time and requires less energy and space than normal computers. 1000 litres of water could contain DNA with more memory than all the computers ever made, and a pound of DNA would have more computing power than all the computers ever made. Obviously if you want to perform one calculation at a time, DNA computers are not a viable option. When Adleman derived an optimal solution to a seven-city traveling-salesman problem, it took approximately one week.

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Unfortunately, you can solve the same problem on a piece of paper in about an hour - or by a digital computer in a few seconds. But when the number of cities is increased to just 70, the problem becomes intractable for even a 1000Mips supercomputer. What are the particular problems of error correction? In real operations that molecular biologists do every day in the lab, they don't perform many operations on the scale or amount of DNA that is necessary for practical DNA computing. They seldom handle the same number of steps that these computers will require, perhaps thousands, and they are not called upon to operate with the accuracy that is required of a computer. The analysis of the errors that occur in these computers is often very difficult because molecular biologist don't quantify their errors. For example, when creating a recombinant DNA molecule that they would like to put into a bacterium, molecular biologists don't require that a recombinant DNA operation work for 100% of the molecules in a reaction, or even 1%. A molecular biologist only wishes to get 'enough' correct recombinant molecules to be able to transfer one bacterium that will yield one colony of bacteria that have the desired characteristic. The Restricted Model: Since

Adleman's

original

experiment,

several

methods to reduce error and improve efficiency have been developed. The problems with implementing a DNA computer can be separated into two types:

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Physical obstructions: difficulties with large scale systems and coping with errors Logical obstructions: concerning the versatility of molecular computers and their capacity to efficiently accommodate a wide variety of computational problems. The Restricted model of DNA computing solves several physical problems scientists had with the Unrestricted model. The Restricted model simplifies the physical obstructions in exchange for some additional logical considerations. The purpose of this restructuring is to simplify

biochemical

operations

and

reduce

the

errors.

The Restricted model of DNA computing in test tubes is simplified to: Separate: isolate a subset of DNA from a sample Merge: pour two test tubes into one to perform union Detect: Confirm presence/absence of DNA in a given test tube Despite these restrictions, this model can still solve Hamiltonian Path problems. (Adleman) Error control can also be achieved mainly through logical operations, such as running all DNA samples showing positive results a second time to reduce false positives. Some molecular proposals, such as using DNA with a peptide backbone for stability, have also been recommended.

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DNA Computing on a chip The DNA computing was proposed as a means of solving a class of computational problems where in the computing time grows exponentially with the problem size. ‘Traveling sales man’ or ‘Hamilton path problem’ is an example. One technique for solving such problems involves the immobilization and manipulation of combinatorial mixtures of DNA on a support. These problems include the search for a solution that simultaneously satisfies a number of clauses, each composed of a number of variables connected by OR statements. These can be solved by a reasonable amount of time by using brute force search made possible by the parallel nature of DNA computing techniques. Here, space (amount of DNA needed) is exchanged for time (number of biochemical steps to be used) to achieve a small computational time. The whole process consists of the following steps

1)

A set of DNA molecules (Watson strands) encoding all candidate solutions to the computational problems of interest is synthesized and attached to a surface via a reactive functional group.

2)

A ‘mark’ operation is carried out in which supplementary strands for all possible Watson strands satisfying the first clause are added. These supplementary strands are called Crick Strands. These Crick strands stick to corresponding Watson strand creating double stranded DNA.

3)

After this, an enzyme is added which destroys all surface bond oligonucleotides present in single strand form – destroy operation.

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4)

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The surface is then regenerated for the next cycle of operation by removing all hybridized compliments in an ‘unmark’ operation.This cycle of mark, destroy and unmark, will be repeated as many times as the number of clauses (say, N). At the end of N cycles, only those strands, which are solutions to the problem, remain. The identities of these solutions are determined in a ‘read out’ operation by polymerase chain reaction (PCR) followed by amplification and hybridization to an addressed array.

The various steps for performing DNA computing is shown below.

Consider for example, a four variable, four clause 3-SAT problem given as

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(x OR y’ OR z) AND (w OR x’ OR z) AND (x’ OR y,) AND (w OR z)

The above mentioned problem can be solved in four cycles of the ‘mark’, ‘destroy’, and ‘unmark’ operation as shown in figure below. The four binary variables w, x, y and z and the possible solution set consists of the decoded values of all combinations of these variables. Thus the universal set consists of strands of numbered 0 to 15. For example: 1010(w=1, x=0, y=1 and z=0) will represent the number 10 strand. The same convention is followed for all possible answers. Using the procedure mentioned above, the solutions are isolated one by one. For example: after step one, two strands are eliminated as 2(0010) and 10(1010) do not satisfy the first clause. Proceeding in similar fashion, one gets the final solution set as 1(0001), 3(0011), 8(1000), 9(1001),11(1011), 12(1100), 13(1101).

Not counting the number of steps required to produce the DNA molecules in the first place, the algorithm takes only (3k+1) steps (where k is the number of clauses for a brute force evaluation of all possible answers). This is a remarkable improvement over the best conventional computer algorithm. For example, a 3-SAT problem with 30 clauses and 50 variables could be solved in approximately 1.6 million steps by an ordinary algorithm, but in only 91 steps by the DNA computer.

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Enzyme Based Logic Gates (ENLOGS) These molecular switches use enzymatic activity to perform operations that correspond to simple logic operations such as AND, OR, etc. The classical analogy to explain the switching is the lock and key model. Since the enzymes are the facilitating components, it is convenient to think of each type of enzyme as a specific key and the substrates as locks. Fitting the key into the lock is a simple switching event. The main crux of the problem is to exploit the micro scale mechanisms to solve macro scale problems.

figure 1 : basic module of enzymatic switch

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The transduction and integration step, the input signals impinging on the module are transduced to physiochemical presentations with shape features, which enzyme can recognize. This is followed by the recognition effect association step in which the read out enzymes recognize the transduced features and take an action ,thus linking these features to a molecular scale output. In the amplification step, the molecular level output is amplified to a macroscopic output.

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Biomolecular Computers

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PROTEIN BASED OPTICAL COMPUTING MEMORIES Much research has gone in developing high-speed optics random access memory based on bacteriorhodopsin. Bacteriorhodopsin is a purple coloured pigment occurring in the cell membrane of Halobacterium halobium. It utilizes solar energy to move protons across the membrane, resulting in difference in the proton levels. Now it is known that under a high proton concentration, the formation of ATP takes place and this ATP is used to catalyse a reaction. By measuring the rate of reaction, one can create a logic gate. On being cooled to sufficiently low temperatures, a nanometer-sized section of the bR molecule will kink out of shape when struck by a green laser. But, most importantly, the altered bR molecules can be made to snap back to their original form, if hit by a red laser. Hence, bR can act as the basis for a molecular binary switch. This can be used to make large optical memories with access time below two nano seconds. Currently, access times of 20ns have been achieved, the major limitation being the speed at which optical beams can be positioned to read or write single bits. Such bR based molecular storage devices could potentially store as much as 480 gigabytes of data in just five cubic centimeters, that can be read, written, or erased in as little as five pico seconds using present laser diode technology.

In contrast to such a bR based system, today’s semiconductor systems are thousand times slower, and would require enclosures the size of a home refrigerator to hold an equivalent amount of data. Also, unlike 2-D semiconductors, bR devices are naturally 3-D in

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geometry. As such, bR’s unique architecture might herald a new era of multidimensional holographical computing.

Another use of bR can be in the field of developing electronic ink for computer displays. It can be made to change its optical properties, especially its colour, when acted on by electric field-a property called

as

electrochromism.

Certain

mutant

halabacteria

produce

bacteriorhodopsin, that potentially exhibits very strong electrochromism, in which the colour changes from blue to pale yellow. These can thus be used as electrically addressable pigments for use in computer displays. A bR is sandwiched between glass plates that contain arrays of large number of electrodes. A page of text or a colour image is written electrically on the protein film by applying the corresponding array of voltages on the electrodes, similar to the technology used in liquid crystal displays for laptop computers. The difference is that electronic ink get its colour from reflecting ambient light , whre as laptop displays use internal light sources which are a drain on the batteries. Thus, the battery life time problem in portable computers can be overcome.

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Biomolecular Computers

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MOLECULAR AGAINST CONVENTIONAL COMPUTING The molecular-based systems have several advantages over the silicon systems

Design and Fabrication The costs to design and build a 64 mega-bit memory chip run into billions of dollars and these costs would raise higher for larger memory-sized chips. In contrast, some bio molecular systems like bR offer the promise of being economically grown in a vat and can quickly be harvested in a normal environment which is controlled via ordinary chemistry or use of shelf laser diodes.

Quantum Effects They are introduced due to very small size of solid-state devices. This is important when the feature size reduces to a point where one is dealing with individual atoms. The quantum effects like unwanted tunneling of electrons pose a great difficulty. These effects can be nullified using an average output through redundant circuits making the fabrication costlier. In contrast, in the molecular-based systems , billions of atoms can be stuffed into smallest patches of material, which can carry or encode identical information with reasonable accuracy despite quantum effects due to the natural redundancy inherent in these systems.

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Thermal Build-up Semiconductor designers are always trying to shrink circuit line widths in order to increase overall processor speed. But this causes massive thermal dissipation problems. The tightly spaced electronic switches generate huge amounts of heat, which has to be dissipated at a high speed. Such problems will not arise in bio molecular devices.

Holographic associative Memories At present, serial memories dominate computer architectures. Associative memories, which are used by molecular-based systems, take an input and independent of the central processor, scan the entire memory for the data block that matches exactly or with some tolerance and finally return the data block or it’s memory address, which satisfies the matching criteria. Such memories have significant potential in optical computer architectures, optically coupled neural network computers, robotic vision hardware and generic pattern systems.

3D Optical Memories A major disadvantage of the present computer chips is the 2-D memory storage capacity .The 3-D addressing capability derives from the ability to adjust the location of the irradiated volume in the three dimensions.

The other advantages of the molecular computing systems are : a higher degree of integration, considerable lower switching energies, enhanced stability of the circuits in presence of radiation, inherent precision and high speed signal processing.

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Dept.of Electronics & Communication

Biomolecular Computers

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Where is the work being done? What is the future? The year 2000 has brought a renewed interest in biomolecular computing, and money from the National Science Foundation and U.S. Military has followed. The most logical applications will be in Biology, Chemistry, Medicine, and the Military where scientists deal with enormous amounts of data. There has been an amazing growth of knowledge about how to compute with molecules and a wealth of theoretical models with steadily accumulating laboratory experience, all of which serve to present more challenges than solutions. Researchers from Stanford and Princeton universities, Richard J. Lipton, a computer-science professor at Princeton University, Daniel Boneh, an assistant professor of computer science at Stanford University, and Christopher T. Dunworth, a computer-science doctoral student at Princeton, have outlined a way for a DNA computer to crack messages coded with the U.S. government's own Data Encryption Standard, which is used to protect a wide range of data, including telephone conversations on classified topics and data transmissions between banks and the Federal Reserve. When a message is encrypted according to the standard, the coding relies on one of 72 quadrillion "keys," or encoding instructions. A message coded in this way is hard to crack, because there is no way to know which specific key was used. Testing all possible keys on an electronic computer would take an enormous amount of time, but a DNA computer could test all of the keys at the same time, find the right one, and pass it to a human code-breaker for use in translating the message. A highly

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automated version of a DNA computer might be able to produce the answer in as little as two hours. "About 15 groups are actively doing DNA computer research worldwide, and most of these groups are looking for the right architectural features for doing such molecular computations. DNA is just one molecule with which these computers could eventually be made," (Adleman). A massive study is due soon from Duke University's John H. Reif, Professor of Computer Science and involves Princeton, NYU, U of Penn, U of Delaware, Mt. Sinai, U of Wisconsin, U of Southern Calif, Binghamton U, U of Rochester. The key tasks are experimental demonstrations, nano-construction of new 3-D structures, applications, and software tools. The most important task of the project is small to moderate scale prototype demonstrations and implementations. This involves word(W) design and register design for storage and retrieval of logical and numeric information in massively parallel memories, where the registers encode large amounts of binary data within distinct DNA strands. They are using word designs and other methods to improve error resiliency. The experimental demonstrations of BMC include: massively parallel execution of basic operations such as logical and arithmetic operations, and the sequential chaining of these operations. Future research is aimed at developing a device that can read out answers more easily, perhaps on a traditional computer screen, especially if there is more than a single answer. Answers now must be gleaned from the results by the scientist.

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One-pot reactions is another dream of molecular computation. Scientists would like to be able to throw a bunch of reactants together and watch them self-assemble without any more purification, separation, or poking and prodding other than something simple like heat cycling. The problem is that setting up the kinds of chemical reactions that are rich enough to support computation is hard to do without cross-talk between them. Cells manage to decrease cross-talk by compartmentalizing chemical reactions physically with membranes or virtually by segregating them in the nanoenvironments of enzymes.(Wisz) Biomolecular computation, may have its biggest impact in completely different ways -- for example, enabling a computing system to read and decode natural DNA directly. Such a computer also might be able to perform DNA fingerprinting -- matching a sample of DNA, such as that in blood found at a crime scene, with the person from whom it came. 'The DNA computer might be a cost-effective way to decode the genetic material of humans and other living things, and it might be able to create wetdata-bases of DNA for research purposes that would eliminate the timeconsuming task of translating DNA into a form that can be stored in an electronic computer. That could be the killer application for biomolecular computation.' (Reif) While

most

research

is

taking

place

at

universities, some companies are probing the potential of DNA computers. NEC Corp.'s Research Institute in Princeton, N.J., for instance, has several scientists working on DNA computing. Hewlett-Packard Co., in Palo Alto,

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Calif., is keeping tabs on six to 10 major projects, including Smith's work at the U of Wisconsin. Current biomolecular computing technology is still far from overtaking the silicon chip. DNA computing is an infant discipline looking to find a way into real-world applications and a dream for scientists... a dream to harness the enormous data-storing capacity of DNA. It does seem to be the first example of true nanotechnology, forging a link between computational science and life science. Solutions take multidisciplinary teams employing molecular biologists, mathematicians, computer scientists, biochemists, and material engineers. Interest in these computers nearly ebbed in late 1999, but has been renewed in 2000. In my lifetime computers filled entire rooms and had to be programmed by manually rewiring. Since that time, computers have become much smaller and easier to use. It is possible that DNA computers will become more common for solving very complex problems, and those of us alive now will remember when many could not imagine that they would ever be practical. "Practical uses of the technology will come later. The business history of computation is that the capability comes before significant applications. UNIVAC, the first commercially produced electronic computer in 1951, was not a success in the marketplace. It took businesses such as IBM to start inventing their own computers and finding new uses for them. "(Wood)

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NEW DEVOLOPMENTS

The first applications were "brute force" solutions in which random DNA molecules were generated, and then the correct sequence was identified. The first problems solved by DNA computations involved finding the optimal path by which a travelling salesman could visit a fixed number of cities once each. Recent work showed how DNA can be employed to carry out a fundamental computer operation, addition of two numbers expressed in binary." (Bancroft)

In January 2000, the Lloyd Smith team at the University of Wisconcin, showed that DNA computing can be simplified by attaching the molecules to a surface and then using them to tackle real and complex problems. This 'solid surface' chemistry greatly simplifies the complex and repetitive steps previously used in rudimentary DNA computers. It takes DNA out of the test tube and puts it on a solid surface, making the technology simpler, more accessible and more amenable to the development of larger DNA computers. This one breakthrough revitalized the research community.

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The Wisconsin approach uses a gold-plated square of glass as something like to a conventional memory chip. As many as a trillion individual strands of DNA would be anchored to the glass, each strand containing information being stored in the DNA computer. The new surface chemistry provides an opportunity for harnessing DNA to make the biggest non-conventional computer yet. (Smith) To use a solid surface the scientists must immobilize a complete combinatorial set of single strand DNA oligomers onto a surface. The surface will facilitate sample handling and simplify reactantproduct separation. There will however be a loss of information density in this two-dimensional world and slower enzyme movement. This is why Adleman's research remains in test tubes. The n301ers will recognize some of the lingo here. Information is stored in a NON-ADDRESSED array of 'DNA Words' of a fixed length, 16mers (8 bits per word=256 or 16mers) These words (W) will be linked together to form large combinatorial sets of molecules. (2300 copies of each DNA or 64mer in 1 cm^2). To get a readout: perform enzymatic DNA computations to remove most of the words from the surface (Here are those restrictive enzymes.), amplify the remaining DNA words (PCR), and then identify the remaining words by detecting PCR products on single word arrays. How do they know what the mathmatical answer is? You might be interested in seeing this chart. It's somehow familiar; and yet, it isn't. We can see 16 strands of DNA and their enzyme sequence as it corresponds to the binary numbers that represent 0 through 15. (Smith)

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Biomolecular Computers

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CONCLUSION Biomolecular computers have the real potential for solving problems of high computational complexities and therefore, many problems are still associated with this field. The difficulty of devising an interface is therefore the sensitive dependence on a biological environment, susceptibility to degradation, senescence and infection, etc. Nevertheless, it offers the best approach to human cognitive equivalence. But like any radically new technology, there is a daunting learning and manufacturing curve that must first be overcome before these molecular devices can find a practical use in everyday life. They are still five to ten years away from becoming a commercial reality.

Govt. Engg. College, Thrissur

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Biomolecular Computers

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REFERENCES 1.

Michael Conrad. ‘Molecular Computing: The Lock – Key Paradigm.’ Computer, vol25, 1992, p 11.

2.

‘DNA Computing on a chip’ by Mitsunori Ogihara & Animesh Ray.

3.

Q Liu, et al . ‘DNA Computing on Surfaces’.

4.

T H Cormer, et al. ‘Introduction to Algorithms’

5.

Robert Birge. ‘Protein based Optical Computing Memories.’

6. A L Lehninger, et al. ‘Principles of Biochemistry’ 7. Kirstof Sienicky. ‘Molecular Electronics & Molecular Electronic Devices’

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ACKNOWLEDGEMENT I thank God Almighty for the successful completion of my seminar. Sincere feelings of gratitude for Prof. K.P. Indira Devi, Head of the Department, Electronics & Communication Engineering. I express my heartfelt gratitude to co-ordinator Smt. Muneera C.P. for her valuable advice and guidance. I would also like to express my gratitude to all my respected teachers.

I would like to thank my dear friends, for their kind-hearted cooperation and encouragement.

AJITH DEVADAS

Govt. Engg. College, Thrissur

Dept.of Electronics & Communication