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Seminar Report

On

DNA BASED COMPUTING 

Department of Electrical And Electronics Engineering

College Of Engineering Roorkee7th K.M. Roorkee Haridwar Road (NH-58)

Roorkee (India) – 247667

By

Aditya Chaudhary

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 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.

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Table of Contents

S. No. Topics Page No.

i Abstract 4

1 Introduction 5

2 A Brief information of DNA 6

3 DNA Structure 7

4 History of Computers 8

5 A brief description to DNA Based Computing 12

6 DNA Computer Vs Silicon Computer 13

7 Path Hamilton Problem 14

8 Programming Methods with DNA 16

9 Architecture of DNAC 18

10 Working of DNAC 19

11 DNA Chip 24

12 Self Assembled DNA Scaffolding Used to Built

Tiny Circuit Boards

25

13 Development Scale 28

14 Advantages 29

15 Challenges to Implementation 29

16 Applications 30

17 Future Outlook 31

18 Limitations 33

19 Conclusion 34

20 Referemces 35

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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. 

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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 co-

discovered 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.

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1.  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.`

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2.  DNA Structue:

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3.  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.

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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‖.[10]

 

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:

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  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

programmability.

  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.

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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

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4.  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.

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5.  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. 

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6.  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.

2. Keep only those paths that begin with the start city (A) and conclude with the end city

(G).

3. Because the graph has 7 cities, keep only those paths with 7 cities.

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4. Keep only those paths that enter all cities at least once.

5. 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:

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7.  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 half-

red 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.

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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.

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8.  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.

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9.  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

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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 

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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

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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. 

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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.

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10. DNA Chip:

Mother Board

DNA molecule Arrangement in Chip 

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11. Self-Assembled DNA Scaffolding Used To Build Tiny Circuit

Boards:

“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.

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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 thetemplate, 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

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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.

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12. Development Scale:

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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 to Implementation:

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

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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 computationalsystems, 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.

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16. Future Outlook:

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, andengineering, 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 computationalproblems 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

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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.

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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.

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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.

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 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.  http://www.bbcworld.com

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