Dna Based Computing
A New Generation of Computers...
Seminar Report On
DNA BASED COMPUTING
Department of Electrical And Electronics Engineering
College Of Engineering Roorkee 7th K.M. Roorkee Haridwar Road (NH-58) Roorkee (India) – 247667
By Aditya Chaudhary
Acknowledgement We greatly thankful to my seminar guide Mr. Sanjay Kumar Sinha, Electrical and Electronics Engineering Department, who inspired us to present are seminar on “DNA COMPUTING”. He helped and encouraged us in every possible way. The knowledge acquired during the preparation of the seminar report would definitely help us in my future ventures. We would like to express are sincere gratitude to Mr Sanjay Kumar Sinha, Lecturer, Department of Electrical and Electronics Engineering, for finding out time and helping in this seminar. We would also thank all the teachers of our Department for there help in various aspects during the seminar.
Table of Contents
A Brief information of DNA
History of Computers
A brief description to DNA Based Computing
DNA Computer Vs Silicon Computer
Path Hamilton Problem
Programming Methods with DNA
Architecture of DNAC
Working of DNAC
Self Assembled DNA Scaffolding Used to Built Tiny Circuit Boards
Challenges to Implementation
ABSTRACT DNA computing is a form of computing which uses DNA, 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. According to the Moore‘s Law, ―silicon microprocessors double in complexity roughly every two years and the size reduces to half.‖But, one day this will no longer hold true when miniaturisation limits are reached. Therefore, we require a successor to the traditional silicon based microchip. Initially developed by Leonard Adleman the area of DNA computing has come very far and is becoming an issue of vital importance.
KEYWORDS: Extremely dense information storage, enormous parallelism, extraordinary energy efficiency.
Introduction: This Introductory segment of the seminar report on DNA-based Computing (DNAC), will provides the basic tools necessary to understand current research in DNAC. Computers based on the sub-microscopic technique, was the visionary idea of Richard Feynman, 1959. In 1994, experiments conducted by Leonard Adleman, proving the feasibility of the DNAC. He 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 codiscovered the structure of DNA in 1953. In 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 ―travelling salesman problem‖. The goal of the problem is to find the shortest route between number of cities, going through each city only once. As more cities are added to the problem, the problem becomes more difficult. Adleman chose to find the shortest route between seven cities. His work was followed by the development of the theory of neural networks based DNAC.
A brief information to DNA: Deoxyribonucleic acid is a nucleic acid that contains the genetic instructions used in the
development and functioning of all known living organisms and some virus. The main role of DNA molecule is the long-term storage of information. DNA is often compared to a set of blueprints or a recipe, or a code, since it contains the instructions needed to construct other information are called gene, but other DNA sequences have structural purposes, or are involved in regulating the use of this genetic information. Chemically, DNA consists of two long polymers of simple units called nucleotides, with backbones made of sugars and phosphate groups joined by ester bonds. These two strands run in opposite directions to each other and are therefore anti-parallel. Attached to each sugar is one of four types of molecules called bases. It is the sequence of these four bases along the backbone that encodes information. This information is read using the genetic code, which specifies the sequence of the amino acids with proteins. The code is read by copying stretches of DNA into the related nucleic acid RNA, in a process called transcription. Within cells, DNA is organized into long structures called chromosomes. These chromosomes are duplicated before cells divide, in a process called DNA replication. Eukaryotic organisms (animals, plants, fungi and protists) store most of the DNA inside the cell nucleus and some of their DNA in organelles, such as mitochondria or chloroplasts. In contrast, proteins such as histones compact and organize DNA. These compact structures guide the instructions between DNA and other proteins, helping control which parts of the DNA are transcribed.`
History of Computers: The first use of the word ―computer‖ was recorded in 1613, referring to a person who carried
out calculations, or computations, and the word continued to be used in that sense until the middle of the 20th century. From the end of the 19th century onwards though, the word began to take on its more familiar meaning, describing a machine that carries out computations. The history of the modern computer begins with two separate technologies—automated calculation and programmability—but no single device can be identified as the earliest computer, partly because of the inconsistent application of that term. Examples of early mechanical calculating devices include the abacus, the slide rule and arguably the astrolabe and the Antikythera mechanism (which dates from about 150–100 BC). The ―castle clock‖, an astronomical clock invented by Al-Jazari in 1206, is considered to be the earliest programmable analog computer. It displayed the zodiac, the solar and lunar orbits, a crescent moon-shaped pointer travelling across a gateway causing automatic doors to open every hour, and five robotic musicians who played music when struck by levers operated by a camshaft attached to a water wheel. The length of day and night could be re-programmed to compensate for the changing lengths of day and night throughout the year. The Renaissance saw a re-invigoration of European mathematics and engineering. Wilhelm Schickard‗s 1623 device was the first of a number of mechanical calculators constructed by European engineers, but none fit the modern definition of a computer, because they could not be programmed. In 1801, Joseph Marie Jacquard made an improvement to the textile loom by introducing a series of punched paper cards as a template which allowed his loom to weave intricate patterns automatically. The resulting Jacquard loom was an important step in the development of computers because the use of punched cards to define woven patterns can be viewed as an early, albeit limited, form of programmability. It was the fusion of automatic calculation with programmability that produced the first recognizable computers. In 1837, Charles Babbage was the first to conceptualize and design a fully programmable mechanical computer, his analytical engine.Limited finances and Babbage‘s inability to resist tinkering with the design meant that the device was never completed.
In the late 1880s, Herman Hollerith invented the recording of data on a machine readable medium. Prior uses of machine readable media, above, had been for control, not data. ―After some initial trials with paper tape, he settled on punched cards ...‖ To process these punched cards he invented the tabulator, and the keypunch machines. These three inventions were the foundation of the modern information processing industry. Large-scale automated data processing of punched cards was performed for the 1890 United States Census by Hollerith‘s company, which later became the core of IBM. By the end of the 19th century a number of technologies that would later prove useful in the realization of practical computers had begun to appear: the punched card, Boolean algebra, the vacuum tube (thermionic valve) and the teleprinter. During the first half of the 20th century, many scientific computing needs were met by increasingly sophisticated analog computers, which used a direct mechanical or electrical model of the problem as a basis for computation. However, these were not programmable and generally lacked the versatility and accuracy of modern digital computers. Alan Turing is widely regarded to be the father of modern computer science. In 1936 Turing provided an influential formalisation of the concept of the algorithm and computation with the Turing machine. Of his role in the modern computer, Time magazine in naming Turing one of the 100 most influential people of the 20th century, states: ―The fact remains that everyone who taps at a keyboard, opening a spreadsheet or a word-processing program, is working on an incarnation of a Turing machine‖. The inventor of the program-controlled computer was Konrad Zuse, who built the first working computer in 1941 and later in 1955 the first computer based on magnetic storage. George Stibitz is internationally recognized as a father of the modern digital computer. While working at Bell Labs in November 1937, Stibitz invented and built a relay-based calculator he dubbed the ―Model K‖ (for ―kitchen table‖, on which he had assembled it), which was the first to use binary circuits to perform an arithmetic operation. Later models added greater sophistication including complex arithmetic and programmability. A succession of steadily more powerful and flexible computing devices were constructed in the 1930s and 1940s, gradually adding the key features that are seen in modern computers. The use of digital electronics (largely invented by Claude Shannon in 1937) and more flexible programmability were vitally important steps, but defining one point along this road as ―the first digital electronic computer‖ is difficult. Shannon 1940 Notable achievements include:
Konrad Zuse‗s electromechanical ―Z machines‖. The Z3 (1941) was the first working
machine featuring binary arithmetic, including floating point arithmetic and a measure of programmability. In 1998 the Z3 was proved to be Turing complete, therefore being the world‘s first operational computer.
The non-programmable Atanasoff–Berry Computer (1941) which used vacuum tube
based computation, binary numbers, and regenerative capacitor memory. The use of regenerative memory allowed it to be much more compact than its peers (being approximately the size of a large desk or workbench), since intermediate results could be stored and then fed back into the same set of computation elements.
The secret British Colossus computers (1943), which had limited programmability but
demonstrated that a device using thousands of tubes could be reasonably reliable and electronically reprogrammable. It was used for breaking German wartime codes.
The Harvard Mark I (1944), a large-scale electromechanical computer with limited
The U.S. Army‘s Ballistic Research Laboratory ENIAC (1946), which used decimal
arithmetic and is sometimes called the first general purpose electronic computer (since Konrad Zuse‗s Z3 of 1941 used electromagnets instead of electronics). Initially, however, ENIAC had an inflexible architecture which essentially required rewiring to change its programming. Several developers of ENIAC, recognizing its flaws, came up with a far more flexible and elegant design, which came to be known as the ―stored program architecture‖ or von Neumann architecture. This design was first formally described by John von Neumann in the paper First Draft of a Report on the EDVAC, distributed in 1945. A number of projects to develop computers based on the stored-program architecture commenced around this time, the first of these being completed in Great Britain. The first to be demonstrated working was the Manchester Small-Scale Experimental Machine (SSEM or ―Baby‖), while the EDSAC, completed a year after SSEM, was the first practical implementation of the stored program design. Shortly thereafter, the machine originally described by von Neumann‘s paper—EDVAC—was completed but did not see full-time use for an additional two years. Nearly all modern computers implement some form of the stored-program architecture, making it the single trait by which the word ―computer‖ is now defined. While the technologies used in computers have changed dramatically since the first electronic, general-purpose computers of the 1940s, most still use the von Neumann architecture.
Beginning in the 1950s, Soviet scientists Sergei Sobolev and Nikolay Brusentsov conducted research on ternary computers, devices that operated on a base three numbering system of -1, 0, and 1 rather than the conventional binary numbering system upon which most computers are based. They designed the Setun, a functional ternary computer, at Moscow State University. The device was put into limited production in the Soviet Union, but supplanted by the more common binary architecture. Computers using vacuum tubes as their electronic elements were in use throughout the 1950s, but by the 1960s had been largely replaced by transistor-based machines, which were smaller, faster, cheaper to produce, required less power, and were more reliable. The first transistorised computer was demonstrated at the University of Manchester in 1953. In the 1970s, integrated circuit technology and the subsequent creation of microprocessors, such as the Intel 4004, further decreased size and cost and further increased speed and reliability of computers. By the late 1970s, many products such as video recorders contained dedicated computers called microcontrollers, and they started to appear as a replacement to mechanical controls in domestic appliances such as washing machines. The 1980s witnessed home computers and the now ubiquitous personal computer. With the evolution of the Internet, personal computers are becoming as common as the television and the telephone in the household. Modern smartphones are fully programmable computers in their own right, and as of 2009 may well be the most common form of such computers in existence
A brief description to DNA Based Computers: DNA computing is a form of computing which uses DNA, 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. Research and development in this area concerns theory, experiments and applications of DNA computing. DNA computing is fundamentally similar to parallel computing in that it takes advantage of the many different molecules of DNA to try many different possibilities at once. DNA computing also offers much lower power consumption than traditional silicon computers. DNA uses adenosine triphosphate (ATP) as fuel to allow ligation or as a means to heat the strand to cause disassociation. Both strand hybridization and the hydrolysis of the DNA backbone can occur spontaneously, powered by the potential energy stored in DNA. Consumption of two ATP molecules releases 1.5 x 10−19 J. Even with a large number of transitions per second using two ATP molecules, power output is still low. For instance, Kahan reports 109 transitions per second with an energy consumption of 10−10 W, and similarly Shapiro reports a system producing 7.5 x 1011 outputs in 4000 sec resulting in an energy consumption rate of ~ 10−10 W. For certain specialized problems, DNA computers are faster and smaller than any other computer built so far. But DNA computing does not provide any new capabilities from the standpoint of computability theory, the study of which problems are computationally solvable using different models of computation. For example, if the space required for the solution of a problem grows exponentially with the size of the problem (EXPSPACE problems) on von Neumann machines, it still grows exponentially with the size of the problem on DNA machines. For very large EXPSPACE problems, the amount of DNA required is too large to be practical. (Quantum computing, on the other hand, does provide some interesting new capabilities). DNA computing overlaps with, but is distinct from, DNA nanotechnology. The latter uses the specificity of Watson-Crick base-pairing and other DNA properties to make novel structures out of DNA. These structures can be used for DNA computing, but they do not have to be necessarily. Additionally, DNA computing can be done without using the types of molecules made possible by DNA nanotechnology.
DNA Computer Vs Silicon Computer: Silicon microprocessors have been the heart of the computing world for more than 40 years.
In that time, manufacturers have crammed more and more electronic devices onto their microprocessors. In accordance with Moore’s Law, the number of electronic devices put on a microprocessor has doubled every 18 months. Moore‘s Law is named after Intel founder Gordon Moore, who predicted in 1965 that microprocessors would double in complexity every two years. Many have predicted that Moore‘s Law will soon reach its end, because of the physical speed and miniaturization limitations of silicon microprocessors. DNA computers have the potential to take computing to new levels, picking up where Moore‘s Law leaves off. There are several advantages of using DNA instead of silicon:
As long as there are cellular organisms, there will always be a supply of DNA.
The large supply of DNA makes it a cheap resource.
Unlike the toxic materials used to make traditional microprocessors, DNA biochips
can be made cleanly.
DNA computers are many times smaller than today‘s computers.
DNA‘s key advantage is that it will make computers smaller than any computer that has come before them, while at the same time holding more data. One pound of DNA has the capacity to store more information than all the electronic computers ever built; and the computing power of a teardrop-sized DNA computer, using the DNA logic gates, will be more powerful than the world‘s most powerful supercomputer. More than 10 trillion DNA molecules can fit into an area no larger than 1 cubic centimetre (0.06 cubic inches). With this small amount of DNA, a computer would be able to hold 10 terabytes of data, and perform 10 trillion calculations at a time. By adding more DNA, more calculations could be performed. Unlike conventional computers, DNA computers perform calculations parallel to other calculations. Conventional computers operate linearly, taking on tasks one at a time. It is parallel computing that allows DNA to solve complex mathematical problems in hours, whereas it might take electrical computers hundreds of years to complete them. The first DNA computers are unlikely to feature word processing, e-mailing and solitaire programs. Instead, their powerful computing power will be used by national governments for cracking secret codes, or by airlines wanting to map more efficient routes. Studying DNA computers may also lead us to a better understanding of a more complex computer -- the human brain.
HAMILTON PATH PROBLEM: Adelman is often called the inventor of the DNA computers. His article in a 1994 issue of
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 numbers of cities, going through each city only once. As you add more cities the problem becomes more difficult. The objective is to find a path from start to end going through all the points only once. This problem is difficult for the conventional (serial logic) computers because they try must try each path one at a time. It is like having a whole bunch of keys and trying to see which fits into the lock. Conventional computers are very good at math, but poor at ―key into lock‖ problems. DNA based computers can try all the keys at the same time (massively parallel) and thus are very good at key into lock problems, but much slower at simple mathematical problems like multiplication. The Hamilton path problem was chosen because every key-into-lock problem can be solved as a Hamilton Path Problem.
Figure showing the possible flight routes between the seven cities. The following algorithm solves the Hamilton Path Problem, regardless of the type computers used. 1.
Generate random paths through the graph.
Keep only those paths that begin with the start city (A) and conclude with the end city
Because the graph has 7 cities, keep only those paths with 7 cities.
Keep only those paths that enter all cities at least once.
Any remaining paths are solutions.
The key to solving the problem was using DNA to perform the five steps in solving the above algorithm. These interconnecting blocks can be used to model DNA:
DNA likes to form long double helices:
The two helices are joined by ―bases‖, which will be represented by coloured blocks. Each base binds only to one other specific base. In our example, we will say that each coloured block will bind only with the block of same colour. For example, if we only had red coloured blocks, they would form a long chain like this:
Any other colour will not bind with red:
Programming Method with DNA: STEP 1: Create a unique DNA sequence for each city A through G. For each path, for
example, from A to B, creates a linking pieces of DNA that matches the last half of A and first half of B:
Here the red block represents the city a, while the orange block represents the city B. the halfred half-orange block connecting the two other blocks represents the path from A to B. In a test tube, all different pieces of DNA will randomly link with each other, forming paths through the graph. STEP 2: Because it is difficult to ―remove‖ DNA from solution, the target DNA, the DNA which started from A and ended at G was copied over and over again until the test tube contained a lot of it relative to other random sequences. This is essentially the same as removing all the other pieces. Imagine a sock drawer which initially contains one or two coloured socks. If you put in a hundred black socks, the chances are that all you will get if you reach in is black socks. STEP 3: Going by weight, the DNA sequences which were 7 ―cities‖ long were separated from the rest. A ―sieve‖ was used which would allow smaller pieces of DNA to pass quickly, while larger segments are slowed down. the procedure used actually allows you to isolate the pieces which are precisely 7 cities long from any shorter or longer paths. STEP 4: To ensure that the remaining sequences went through each of cities, ―sticky‖ pieces of DNA attached to magnets were used to separate the DNA. The magnets were used to ensure that the target DNA remained in the test tube, while the unwanted DNA was washed away. First, the magnets kept all the DNA which went through city A in the test tube, then B, then C, and D, and so on. In the end, the only DNA which remained in the tube was that which went through all seven cities.
STEP 5: all that was left to sequences the DNA, revealing the path from A to B to C to D to E to F to G.
Architecture of DNA Computers: 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.
Working of DNAC: 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.
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. The hardware key to biological life is a very stable chemicals called nucleic acid. It is the equivalent of silicon computer system. It can exist in the form of chains of molecules known as nucleotides. There are only four different nucleotides but each of them have a pair of complementary coupling points due to an element of oxygen on one side and a phosphate site on the other. The oxygen atom of any of the four nucleotides can bind to the phosphate site of any other.
Nucleotides have an affinity to stick together due to a chemical formation at each side which provides a physical coupling supplemented by electromagnetic attraction This ability of the nucleotides to bind side by side allows them to form long chains without it mattering which type of nucleotide is binding to which other type.
DNA can form digital strings of information because the four nucleotides cannot be linked to each other in any order
A long string of these four bases can thus contain a massive amount of information. The nucleotides also have another pair of complementary coupling sites which, from a hardware point of view, give DNA other very important characteristics. They allow each nucleotide to link up to a third nucleotide. These extra binding sites are not universal coupling points like the chain building coupling sites, these binding sites allow only specific pairs of nucleotides to bond - A will bind with T and T with A; C will bind with G and G with C.
Nucleotides have additional binding sites which attract specific complementary nucleotides to bind to them This extra bonding site allows the formation of double strand, with one strand being the complement of other. This forms the famous ―double helix‖ structure which carries genetic code.
The complementary coupling sites allow strings of DNA to form into double strands with one strand being the complement of the other. The two strands form into a double helix The first advantage of the double strand structure is the increased stability it provides. Although nucleotide bonding is quite secure there is so much jostling in the environment of a cell that individual nucleotides can get displaced. A double stranded structure of complementary strands allows damaged sections of the strand to be repaired by referring to the complement nucleotides
Enzymes can split a double stranded DNA into a two chain of nucleotides
The splitting process forms two separate chains with one the complement of the other
The two halves of a DNA section which has been split down the middle can each rapidly build an additional complementary strand. These results in the splitting operation producing two copies of the original.
The splitting of strands and then re-growing the complementary strands results in the original strand being copied and is exactly analogous to the way in which binary data is copied within a computer program.
DNA computing is a field that holds the promise of ultra-dense 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 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 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.
DNA molecule Arrangement in Chip
DNA Scaffolding Used To Build Tiny Circuit
“Scientists are using DNA origami to build tiny circuit boards; in this image, low concentrations of triangular DNA origami are binding to wide lines on a lithographically patterned surface.” Science Daily (Aug. 20, 2009) — Scientists at the California Institute of Technology (Caltech) and IBM‘s Almaden Research Center have developed a new technique to orient and position self-assembled DNA shapes and patterns—or ―DNA origami‖—on surfaces that are compatible with today‘s semiconductor manufacturing equipment. These precisely positioned DNA nanostructures, each no more than one one-thousandth the width of a human hair, can serve as scaffolds or miniature circuit boards for the precise assembly of computer-chip components. The advance, described in the current issue of the journal Nature Nanotechnology, could allow the semiconductor industry to pack more power and speed into tiny computer chips, while making them more energy efficient and less expensive to manufacture than is possible today. DNA origami structures have been heralded as a potential breakthrough for the creation of nanoscale circuits and devices. In a process created by Caltech senior research associate Paul W. K. Rothemund and his colleagues, DNA molecules self-assemble in solution via a reaction between a long single strand of viral DNA and a mixture of different short synthetic DNA strands. These short segments act as staples that effectively fold the viral DNA into desired two-dimensional shapes through complementary base-pair binding.
In this way, DNA nanostructures such as squares, triangles, and stars can be prepared that measure 100 to 150 nanometers on an edge and are as thick as the DNA double helix is wide. One roadblock to the use of DNA origami, however, is that the structures are made in saltwater solution—whereas electronic circuits are created on surfaces, like a silicon wafer, so they can be integrated with other technologies. DNA origami structures also adhere randomly to surfaces, which means that ―if you just pour DNA origami over a surface to which they stick, they attach everywhere,‖ explains Rothemund, who jointly led the project with IBM. ―It‘s a little like taking a deck of playing cards and throwing it on the floor; they are scattered willy-nilly all over the place. Such random arrangements of DNA origami are not very useful. If they carry electronic circuits, for example, they are difficult to find and wire up into larger circuits.‖ To eliminate these problems, Rothemund and his colleagues at the Almaden Research Center developed a way to precisely position DNA origami nanostructures on a surface, ―to line them up like little ducks in a row,‖ Rothemund says. ―This knocks down one of the major roadblocks for the use of DNA origami in technology,‖ he adds. In a process developed by IBM scientists, electron-beam lithography and oxygen plasma etching, conventional semiconductor techniques, are used to make patterns on silicon wafers, creating lithographic templates of the proper size and shape to match those of individual triangular DNA origami structures created by Rothemund. The etched patches are negatively charged, as are DNA origami structures, and are therefore ―sticky.‖ To connect the origami to the templates, magnesium ions are added to the saltwater solution containing the origami. The positively charged magnesium ions can stick to both the DNA origami and the negatively charged patches on the template. Thus, when the solution is poured over the template, a negative–positive–negative ―sandwich‖ is formed, with the magnesium atoms acting as a glue to hold the origami to the sticky patches. ―The triangles bind strongly to the sticky patches, but also they can wiggle a bit, so they line up with the outline of the sticky patch. So not only can we put origami where we want them, but they can be oriented in the direction we want them,‖ Rothemund says. The positioned DNA nanostructures can then serve as scaffolds or miniature circuit boards for the precise assembly of components such as carbon nanotubes, nanowires, and nanoparticles at dimensions significantly smaller than possible with conventional semiconductor fabrication
techniques. This opens up the possibility of creating functional devices that can be integrated into larger structures as well as enabling studies of arrays of nanostructures with known coordinates. ―The spacing between the components can be 6 nanometers, so the resolution of the process is roughly 10 times higher than the process we currently use to make computer chips,‖ Rothemund says. ―Then, if you want to design a really small electronic device, say, you just design DNA strands to create the pattern you want, attach little chemical ‗fastening posts‘ to those DNA strands, assemble the pattern, and then assemble the components onto the pattern,‖ he explains. The process isn‘t limited to organizing things that are of interest to physical scientists and engineers, like electronic components, Rothemund adds. For example, he says, ―Biologists studying how proteins interact can place them in patterns on top of DNA origami. This may be useful in the case of motor proteins, the little machines that power our muscles. They work in gangs, with multiple motors pulling together. To study how different configurations of motors cooperate, scientists may use DNA origami to organize the gangs.‖ ―Rothemund and his colleagues have removed a key barrier to the improvement and advancement of computer chips. They accomplished this through the revolutionary approach of combining the building blocks for life with the building blocks for computing,‖ says Ares Rosakis, Theodore von Kármán Professor of Aeronautics and Mechanical Engineering and chair of Caltech‘s Division of Engineering and Applied Science. This work was supported by the National Science Foundation and the Focus Center Research Program.
13. Advantages: DNA computers derive their potential advantage over conventional computers from their ability to: Perform millions of operations simultaneously. The massively parallel processing capabilities of DNA computers may give them the potential to find tractable solutions to otherwise intractable problems, as well as potentially speeding up large, but otherwise solvable, polynomial time problems requiring relatively few operations. Another advantage of the DNA approach is that it works in ―parallel,‖ processing all possible answers simultaneously. Therefore it enables to conduct large parallel searches and generate a complete set of potential solutions. DNA can hold more information in a cubic centimeter than a trillion CDs, thereby enabling it to efficiently handle massive amounts of working memory. The DNA computer also has very low energy consumption, so if it is put inside the cell it would not require much energy to work and its energy-efficiency is more than a million times that of a PC. 14. Challenges
Practical protocols for input and output of data into the memory. A Representation of data in DNA sequences. An Understand the information capacity of the hybridization interactions in large collections of many different DNA sequences. Appropriate physical models to guide design and experimentation
15. APPLICATIONS: The potential applications of re-coding natural DNA into a computable form are many and include: DNA sequencing DNA fingerprinting DNA mutation detection Development and miniaturization of biosensors, which could potentially allow communication between molecular sensory computers and conventional electronic computers. The fabrication of nanoscale objects that can be placed in intracellular locations for monitoring and modifying cell function The replacement of silicon devices with nanoscale molecular-based computational systems, and The application of biopolymers in the formation of novel nanostructured materials with unique optical and selective transport properties DNA based models of computation might be useful for simulating or modeling other emerging computational paradigms, such as quantum computing, which may not be feasible until much later. Evolutionary programming for applications in design or expert systems. In theory, this technology could one day lead to the development of hybrid computer systems, in which a silicon-based PC generates the code for automated laboratory- based operations, carried out in a miniature ‗lab in a box‘ linked to the PC.
With so many different methods and models emerging from the current research, DNA computing can be more accurately described as a collection of new computing paradigms rather than a single focus. Each of these different paradigms within biomolecular computing can be associated with different potential applications that may prove to place them at an advantage over conventional methods. Many of these models share certain features that lend them to categorization by these potential advantages. However, there exist enough similarities and congruencies that hybrid models will be possible, and that advances made in both ―classic‖ and ―natural‖ areas of DNA computing will be mutually beneficial to both areas of research. Advancements in DNA computing may also serve to enhance understanding of both the natural and computer sciences. For these reasons, and due to the many areas dependent on each of computer science, mathematics, natural science, and engineering, continued interdisciplinary collaboration is very important to any future progress in all areas of this new field. A ―killer app‖ is yet to be found for DNA computation, but might exist outside the bulk of current research, in the domain of DNA2DNA applications and other more natural models and applications of manipulated DNA. This direction is particularly interesting because it is an area in which DNA based solutions are not only an improvement over existing techniques, but may prove to be the only feasible way of directly solving such problems that involve the direct interaction with biological matter. On the ―classical‖ front, problem specific computers may prove to be the first practical use of DNA computation for several reasons. First, a problem specific computer will be easier to design and implement, with less need for functional complexity and flexibility. Secondly, DNA computing may prove to be entirely inefficient for a wide range of problems, and directing efforts on universal models may be diverting energy away from its true calling. Thirdly, the types of hard computational problems that DNA based computers may be able to effectively solve are of sufficient economic importance that a dedicated processor would be financially reasonable. As well, these problems will be likely to require extensive time they would preclude the need for a more versatile and interactive system that may be able to be implemented with a universal computing machine. Even if the current difficulties found in translating theoretical DNA computing models into real life are never sufficiently overcome, there is still potential for other areas of development. Future applications might make use of the error rates and instability of DNA based computation methods as a means of simulating and predicting the emergent behavior of complex systems. This could pertain
to weather forecasting, economics, and lead to more a scientific analysis of- social science and the humanities. Such a system might rely on inducing increased error rates and mutation through exposure to radiation and deliberately inefficient encoding schemes. Similarly, methods of DNA computing might serve as the most obvious medium for use of evolutionary programming for applications in design or expert systems. DNA computing might also serve as a medium to implement a true fuzzy logic system.
17. LIMITATIONS: However, there are certain shortcomings to the development of the DNA computers: A factor that places limits on his method is the error rate for each operation. Since these operations are not deterministic but stochastically driven, each step contains statistical errors, limiting the number of iterations one can do successively before the probability of producing an error becomes greater than producing the correct result. Algorithms proposed so far use relatively slow molecular-biological operations. Each primitive operation takes hours when you run them with a small test tube of DNA. Some concrete algorithms are just for solving some concrete problems. Every Generating solution sets, even for some relatively simple problems, may require impractically large amounts of memory. Also, with each DNA molecule acting as a separate processor, there are problems with transmitting information from one molecule to another that have yet to be solved.
18. Conclusion: DNA computers will become more common for solving very complex problems; Just as DNA cloning and sequencing were once manual tasks, DNA computers will also become automatedes. Studying DNA computers may also lead us to a better future enhancement. With so many possible advantages over conventional techniques, DNA computing has great potential for practical use. Future work in this field should begin to incorporate cost-benefit analysis so that comparisons can be more appropriately made with existing techniques and so that increased funding can be obtained for this research that has the potential to benefit many circles of science and Industry.
19. REFERENCES: 1. IEEE papers on DNA computing 2. COMPUTING WITH DNA,Leonard M.Adleman,Scientific American, August 1998. 3. Molecular Computation of Solutions to Combinatorial Problems‖, L.M. Adleman, Science Vol. 266 pp1021-1024, 11 Nov 1994. 4. ―Introduction to computational molecular biology‖ by Joao Setubal and Joao Meidans Sections 9.1 and 9.3 5. ―DNA computing, new computing paradigms‖ by G.Paun, G.Rozenberg, A.Salomaa-chapter 2 6. ―Self-Assembled DNA Scaffolding‖ Science Daily, August 20 2009. DNA computing (web) 1. http://www.stanford.edu/~alexli/soco/index.html 2. http://www.news.nationalgeographic.com/news/2003/02/0224_030224_DNAcomputer.html 3. http://www.cnn.com 4. http://www.sciam.com/article.cfm?articleID=000A4F2E-781B-1E5A-A98A809EC5880105 5. http://unisci.com/stories/20021/0315023.htm 6. http://www.howstuffworks.com 7.
8. http://www.wikipedia.com/wiki/DNA 9. http://www.nature.com/embor/journal/v4/n1/fig_tab/embor719_f1.html 10. http://dnacomputing.design.officelive.com/Documents/DNAStructure.html Journal Reference: 1. Kershner et al. Placement and orientation of individual DNA shapes on lithographically patterned surfaces. Nature Nanotechnology, 2009; DOI: 10.1038/nnano.2009.220