dna compting

7/31/2019 DNA Compting

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TABLE OF CONTENT

1. ABSTRACT2. INTRODUCTION3. WHAT IS DNA?4. BASICS & ORIGIN OF DNA COMPUTING5. DNA COMPUTING6. HISTORY OF DNA COMPUTING7. DNA FUNDAMENTALS8. DNA VS. SILICON9. THE ADLEMANS EXPERIMENT10.DNA CRYPTOGRAPHY11.ORIGINS OF STEGANOGRAPHY12.DNA STEGANOGRAPHY13.DNA AUTHENTICATION14.ADVANTAGES & DISADVANTAGES15.MODALS & FORMATS OF DNA COMPUTATION16.PITFALL OF DNA COMPUTING17.FUTURE OF DNA COMPUTING18.CONCLUSION19.REFERENCES


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ABSTRACT

Silicon microprocessors have been the heart of computing world for more than forty years.Computer chip manufacturers are furiously racing to make the next microprocessor that willtopple speed records and in the process are cramming more and more electronic devices ontothe microprocessor. Sooner or later the physical speed and miniaturization limits of siliconmicroprocessors is bound to hit a wall.Chipmakers need a new material to produce faster computing speed with fewer complexities.You wont believe where scientists have found this new material. DNA, the material ourgenes are made of, is being used to build the next generation of microprocessors. Scientistsare using this genetic material to create nano-computers that might take the place of siliconcomputers in the next decade.A nascent technology that uses DNA molecules to build computers that are faster than theworlds most powerful human-built computers is called DNA computing. Molecular biolo-gists are beginning to unravel the information processing tools such as enzymes, copyingtools, proofreading mechanisms and so on, that evolution has spent millions of years refining.Now we are taking those tools in large numbers molecules and using them as biological com-puter processors.DNA computing has a great deal of advantage over conventional silicon-based computing.DNA computers can store billions of times more data than your personal computer. DNAcomputers have the ability to work in a massively parallel fashion, performing many calcula-tions simultaneously. DNA molecules that provide the input can also provide all the neces-sary operational energy.DNA computing has made a remarkable progress in almost every field. It has found applica-tion in fields like biomedical, pharmaceutical, information security, cracking secret codes,etc.Scentists and researchers believe that in the foreseeable future DNA computing could

scale up to great heights

Molecular biologists are beginning to unravel the information-processing tools such asenzymes that evolution has spent billions of years refining. These tools are now beentaken in large numbers of DNA molecules and using them as biological computerprocessors.Dr. Leonard Adleman, a well-known scientist, found a way to exploit the speed andefficiency of the biological reactions to solve the Hamiltonian path problem, also

known as the traveling salesman problem.

Based on Dr. Adlemans experiment, we will explain DNA computing, its algorithms, how to manage DNA based computing and the advantages and disadvantages of DNA

computing.
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INTRODUCTION

DNA (Deoxyribose Nucleic Acid) computing, also known as molecular computing is a newapproach to massively parallel computation based on groundbreaking work by Adleman.DNA computing was proposed as a means of solving a class of intractablecomputational problems in which the computing time can grow exponentially with problemsize (the 'NP-complete' or non-deterministic polynomial time complete problems).A DNAcomputer is basically a collection of specially selected DNA strands whose combinations willresult in the solution to some problem, depending on the problem at hand. Technology is cur-rently available both to select the initial strands and to filter the final solution. DNA compu-ting is a new computational paradigm that employs (bio)molecular manipulation to solvecomputational problems, at the same time exploring natural processes as computational mod-els

In 1994, Leonard Adleman at the Laboratory of Molecular Science, Department of ComputerScience, University of Southern California surprised the scientific community by using thetools of molecular biology to solve a different computational problem. The main idea was theencoding of data in DNA strands and the use of tools from molecular biology to executecomputational operations. Besides the novelty of this approach, molecular computing has thepotential to outperform electronic computers. For example, DNA computations may use abillion times less energy than an electronic computer while storing data in a trillion times lessspace. Moreover, computing with DNA is highly parallel: In principle there could be billionsupon trillions of DNA molecules undergoing chemical reactions, that is, performing compu-tations, simultaneously.

The current Silicon technology has following limitations: Circuit integration dimensions Clock frequency Power consumption Heat dissipation.

The problem's complexity that can be afforded by modern processors grows up, but greatchallenges require computational capabilities that neither most powerful and distributed sys-tems could reach.

The idea that living cells and molecular complexes can be viewed as potential machinic com-ponents dates back to the late 1950s, when Richard Feynman delivered his famous paper de-scribing "sub-microscopic" computers. More recently, several people have advocated the re-alization of massively parallel computation using the techniques and chemistry of molecularbiology. DNA computing was grounded in reality at the end of 1994, when LeonardAdleman, announced that he had solved a small instance of a computationally intractableproblem using a small vial of DNA. By representing information as sequences of bases in

DNA molecules, Adleman showed how to use existing DNA-manipulation techniques to im-plement a simple, massively parallel random search. He solved the traveling salesman prob-


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lem also known as the Hamiltonian path" problem.

There are two reasons for using molecular biology to solve computational problems.(i) The information density of DNA is much greater than that of silicon : 1 bit can be storedin approximately one cubic nanometer. Others storage media, such as videotapes, can store 1bit in 1,000,000,000,000 cubic nanometer.

(ii) Operations on DNA are massively parallel: a test tube of DNA can contain trillions ofstrands. Each operation on a test tube of DNA is carried out on all strands in the tube in paral-lel.


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WHAT IS DNA?

Before delving into the principles of DNA computing, we must have a basic understanding of

what DNA actually is. All organisms on this planet are made of the same type of genetic

blueprint which bind us together. The way in which that blueprint is coded is the deciding

factor as to whether you will be bald, have a bulbous nose, male, female or even whether you

will be a human or an oak tree.

Within the cells of any organism is a substance called Deoxyribonucleic Acid (DNA) which

is a double-stranded helix of nucleotides which carries the genetic information of a cell. This

information is the code used within cells to form proteins and is the building block uponwhich life is formed.

Strands of DNA are long polymers of millions of linked nucleotides. These nucleotides con-

sist of one of four nitrogen bases, a five carbon sugar and a phosphate group. The nucleo-

tides that make up these polymers are named after the nitrogen base that it consists of; Ade-

nine (A), Cytosine (C), Guanine (G) and Thymine (T). These nucleotides will only combinein such a way that C always pairs with G and T always pairs with A.

The two strands of a DNA molecule are antiparallel where each strand runs in an opposite

direction. Figure 1 illustrates two strands of DNA and the bonding priciples of of the 4 types

of nucleotides and the Figure 2 illustrates the double helix shape of DNA.


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Fig 1Graphical representation of inherent

bonding properties of DNA [11]

Fig 2Illustration of double helix shape of

DNA. [11]

The combination of these 4 nucleotides in the estimated million long polymer strands can re-

sult in billions of combinations within a single DNA double-helix. These massive amount of

combinations allows for the multitude of differences between every living thing on the planet

from the large scale (mammal vs. plant), to the small (blue eyes vs. green eyes).

With the advances in DNA research in projects such as the Human Genome project (a re-

search effort to characterize the genomes of human and selected model organisms through

complete mapping and sequencing of their DNA ) and a host of others, the mystery of DNA

and its construction is slowly being unraveled through mathematical means. Distinct formu-

lae and patterns have emerged that may have implications well beyond those found in the

fields of genetics.

What does all this chemistry and biology have to do with security you might ask? To answer

that question we must first look at how biological science can be applied to mathematical

computation in a field known as DNA computing.


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BASICS AND ORIGIN OF DNA COMPUTING

DNA computing or molecular computing are terms used to describe utilizing the inherent

combinational properties of DNA for massively parallel computation. The idea is that with

an appropriate setup and enough DNA, one can potentially solve huge mathematical prob-

lems by parallel search. Basically this means that you can attempt every solution to a given

problem until you came across the right one through random calculation. Utilizing DNA for

this type of computation can be much faster than utilizing a conventional computer.

Leonard Adleman, a computer scientist at the University of Southern California was the first

to pose the theory that the makeup of DNA and its multitude of possible combining nucleo-

tides could have application in brute force computational search techniques.


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DNA COMPUTING (DNAC)

DNA computing, in the literal sense, is the use of DNA (Deoxyribose Nucleic Acid) mole-

cules, the molecules which encode genetic information for all living things, in computers.

This is accomplished in a suspended solution of DNA, where certain combinations of DNA

molecules are interpreted as a particular result to a problem encoded in the original molecules

present. DNA computing is currently one of the fastest growing fields in both Computer Sci-

ence and Biology, and its future looks extremely promising.

A DNA computer is basically a collection of specially selected DNA strands whose combina-

tions will result in the solution to some problem. Technology is currently available both to

select the initial strands and to filter the final solution. The promise of DNA computing is

massive parallelism: with a given setup and enough DNA, one can potentially solve huge

problems by parallel search. This can be much faster than a conventional computer, for which

massive parallelism would require large amounts of hardware, not simply more DNA.

A highly interdisciplinary study incorporating the research results of computer scientists and

biologists. In the literal sense, DNA computing is the use of DNA molecules, which encode

genetic information for all living things, in computers. It is accomplished in a suspended so-

lution of DNA, where certain combinations of DNA molecules are interpreted as a particular

result to a problem encoded in the original molecules present. Dr. Leonard Aldeman is a pio-

neer of DNA computing for his solution to solve Hamiltonian Path Problem using DNA

strands.

First and foremost, DNA computing is useful because it has a capacity lacked by all current

electronics-based computers: its massively parallel nature. What does this mean, you ask?

Well, essentially while DNA can only carry out computations slowly, DNA computers can

perform a staggering number of calculations simultaneously; specifically, on the order of

10^9 calculations per mL of DNA per second! This capability of multiple co temporal calcu-

lations immediately lends itself to several classes of problems which a modern electronic

computer could never even approach solving. To give you an idea of the difference in time, a


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calculation that would take 10^22 modern computers working in parallel to complete in the

span of one human's life would take one DNA computer only 1 year to polish off!

In 1994, Leonard M. Adleman solved an unremarkable computational problem with a re-

markable technique. It was a problem that a person could solve it in a few moments or an av-

erage desktop machine could solve in the blink of an eye. It took Adleman, however, seven

days to find a solution. Why then was this work exceptional? Because he solved the problem

with DNA. It was a landmark demonstration of computing on the molecular level.

The type of problem that Adleman solved is a famous one. It's formally known as a directed

Hamiltonian Path (HP) problem, but is more popularly recognized as a variant of the so-

called "traveling salesman problem." In Adleman's version of the traveling salesman prob-lem, or "TSP" for short, a hypothetical salesman tries to find a route through a set of cities so

that he visits each city only once. As the number of cities increases, the problem becomes

more difficult until its solution is beyond analytical analysis altogether, at which point it re-

quires brute force search methods. TSPs with a large number of cities quickly become com-

putationally expensive, making them impractical to solve on even the latest super-computer.

Adlemans demonstration only involves seven cities, making it in some sense a trivial prob-

lem that can easily be solved by inspection. Nevertheless, his work is significant for a number

of reasons.

It illustrates the possibilities of using DNA to solve a class of problems that is diffi-cult or impossible to solve using traditional computing methods.

It's an example of computation at a molecular level, potentially a size limit that maynever be reached by the semiconductor industry.

It demonstrates unique aspects of DNA as a data structure It demonstrates that computing with DNA can work in a massively parallel fashion


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HISTORY OF DNA COMPUTING

"Computers in the future may weigh no more than 1.5 tons." So said Popular Mechanics in

1949. Most of us today, in the age of smart cards and wearable PCs would find that statement

laughable. We have made huge advances in miniaturisation since the days of room-sized

computers, yet the underlying computational framework has remained the same. Today's su-

percomputers still employ the kind of sequential logic used by the mechanical dinosaurs of

the 1930s. Some researchers are now looking beyond these boundaries and are investigating

entirely new media and computational models. These include quantum, optical and DNA-based computers. It is the last of these developments that this paper concentrates on.

The idea that living cells and molecular complexes can be viewed as potential machinic

components dates back to the late 1950s, when Richard Feynman delivered his famous paper

describing "sub-microscopic" computers. More recently, several people have advocated the

realisation of massively parallel computation using the techniques and chemistry of molecu-

lar biology. DNA computing was grounded in reality at the end of 1994, when Len Adleman

of USC announced that he had solved a small instance of a computationally intractable prob-

lem using a small vial of DNA. By representing information as sequences of bases in DNA

molecules, Adleman showed how to use existing DNA-manipulation techniques to imple-

ment a simple, massively parallel random search.

Scientists at the Universities of Liverpool and Warwick (UK) are currently building a proto-

type DNA computer to solve a different problem . The problem of "colouring" has a long his-

tory. Given a map of mainland Europe, we know that we can colour each country one of fourcolours such that no two countries sharing a border are coloured the same. However, what

happens if we lose a crayon? Can we still legally colour the map using only three colours?

This problem is a member of the large class of notoriously intractable NP-complete problems,

as are the Travelling Salesman and Bin Packing problems. These problems are characterised

by an exponential- size search space; a problem of size 10 may take a fraction of a second to

solve on a PC, but one of size 30 may take year.


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DNA FUNDAMENTALS

DNA, Deoxyribonucleic Acid, is the molecular basis of heredity and localized especially inmost cell nucleus. DNA molecules consist of two long chains held together by complemen-

tary base pairs.

A DNA chain is a long, unbranched polymer composed of only four type subunits. These are

the deoxyribonucleotides containing the bases adenine (A), cytosine(C), guanine (G), and

thymine (T). The nucleotides are linked together by covalent phosphodiester bonds that join

the 5 carbon of one deoxyribose group to the 3 carbon of the next. The four kinds of bases

are attached to this repetitive sugar-phosphate chain.

The two long chains of a DNA molecule are held together by complementary base pairs.

Three hydrogen bonds form between G and C, and two hydrogen bonds exist between A and

T. The base-pairing mechanism is the basis for DNA replication.

Figure 1:- DNA Structure


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DNA VS. SILICON

DNA, with its unique data structure and ability to perform many parallel operations, allowsyou to look at a computational problem from a different point of view. Transistor-based com-puters typically handle operations in a sequential manner. Of course there are multi-processorcomputers, and modern CPUs incorporate some parallel processing, but in general, in thebasic von Neumann architecture computer, instructions are handled sequentially. A vonNeumann machine, which is what all modern CPUs are, basically repeats the same "fetch andexecute cycle" over and over again; it fetches an instruction and the appropriate data frommain memory, and it executes the instruction. It does this many, many times in a row, really,really fast.

The great Richard Feynman, in his Lectures on Computation, summed up von Neumanncomputers by saying, "the inside of a computer is as dumb as hell, but it goes like mad!"DNA computers, however, are non-von Neuman, stochastic machines that approach compu-tation in a different way from ordinary computers for the purpose of solving a different classof problems.

Typically, increasing performance of silicon computing means faster clock cycles (and largerdata paths), where the emphasis is on the speed of the CPU and not on the size of thememory. For example, will doubling the clock speed or doubling your RAM give you betterperformance? For DNA computing, though, the power comes from the memory capacity and

parallel processing. If forced to behave sequentially, DNA loses its appeal. For example, let'slook at the read and write rate of DNA. In bacteria, DNA can be replicated at a rate of about500 base pairs a second.

Biologically this is quite fast (10 times faster than human cells) and considering the low errorrates, an impressive achievement. But this is only 1000 bits/sec, which is a snail's pace whencompared to the data throughput of an average hard drive. But look what happens if you al-low many copies of the replication enzymes to work on DNA in parallel. First of all, the rep-lication enzymes can start on the second replicated strand of DNA even before they're fin-ished copying the first one. So already the data rate jumps to 2000 bits/sec. But look whathappens after each replication is finished - the number of DNA strands increases exponential-

ly (2^n after n iterations). With each additional strand, the data rate increases by 1000bits/sec. So after 10 iterations, the DNA is being replicated at a rate of about 1Mbit/sec; after30 iterations it increases to 1000 Gbits/sec. This is beyond the sustained data rates of the fast-est hard drives.

Now let's consider how you would solve a nontrivial example of the traveling salesman prob-lem (# of cities > 10) with silicon vs. DNA. With a von Neumann computer, one naive meth-od would be to set up a search tree, measure each complete branch sequentially, and keep theshortest one. Improvements could be made with better search algorithms, such as pruning thesearch tree when one of the branches you are measuring is already longer than the best candi-

date. A method you certainly would not use would be to first generate all possible paths andthen search the entire list. Why? Well, consider that the entire list of routes for a 20 city prob-


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lem could theoretically take 45 million GBytes of memory (18! routes with 7 byte words)!Also for a 100 MIPS computer, it would take two years just to generate all paths (assumingone instruction cycle to generate each city in every path). However, using DNA computing,this method becomes feasible! 10^15 is just a nano mole of material, a relatively small num-

ber for biochemistry. Also, routes no longer have to be searched through sequentially. Opera-tions can be done all in parallel.


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THE ADLEMAN EXPERIMENT

There is no better way to understand how something works than by going through an exam-ple step by step. So lets solve our own directed Hamiltonian Path problem, using the DNA

methods demonstrated by Adleman. The concepts are the same but the example has beensimplified to make it easier to follow and present.

Suppose that I live in LA, and need to visit four cities: Dallas, Chicago, Miami, and NY, withNY being my final destination. The airline Im taking has a specific set of connecting flights

that restrict which routes I can take (i.e. there is a flight from L.A. to Chicago, but no flightfrom Miami to Chicago). What should my route be if I want to visit each city only once?

It should take you only a moment to see that there is only one route. Starting from L.A. youneed to fly to Chicago, Dallas, Miami and then to N.Y. Any other choice of cities will forceyou to miss a destination, visit a city twice, or not make it to N.Y. For this example you obvi-ously dont need the help of a computer to find a solution. For six, seven, or even eight cities,

the problem is still manageable. However, as the number of cities increases, the problemquickly gets out of hand. Assuming a random distribution of connecting routes, the number ofitineraries you need to check increases exponentially. Pretty soon you will run out of pen andpaper listing all the possible routes, and it becomes a problem for a computer...or perhapsDNA. The method Adleman used to solve this problem is basically the shotgun approachmentioned previously. He first generated all the possible itineraries and then selected the cor-rect itinerary. This is the advantage of DNA. Its small and there are combinatorial techniques

that can quickly generate many different data strings. Since the enzymes work on many DNAmolecules at once, the selection process is massively parallel.


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Specifically, the method based on Adlemans experiment would be as follows:

1. Generate all possible routes.2. Select routes that start with the proper city and end with the final city.3. Select routes with the correct number of cities.4. Select routes that contain each city only once.

All of the above steps can be accomplished with standard molecular biology techniques.

The key to solving the problem was using DNA to perform the five steps in the above algo-

rithm. The following operations can be performed with DNA:

o Synthesis of a desired strando Separation of strands by lengtho Merging: pour two test tubes into one to perform uniono Extraction: extract those strands containing a given patterno Melting/Annealing: break/bond two DNA molecules with complementary se-

quenceso Amplification: use PCR to make copies of DNA strandso Cutting: cut DNA with restriction enzymeso Ligation: Ligate DNA strands with complementary sticky ends using ligaseo Detection: Confirm presence/absence of DNA in a given test tube[4]

Part I: Generate all possible routes

Strategy: Encode city names in short DNA sequences. Encode routes by connecting the citysequences for which routes exist.


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DNA can simply be treated as a string of data. For example, each city can be represented by a"word" of six bases:

Los Angeles GCTACG

Chicago CTAGTA

Dallas TCGTAC

Miami CTACGG

New York ATGCCG

The entire route can be encoded by simply stringing together these DNA sequences that rep-resent specific cities. For example, the route from L.A -> Chicago -> Dallas -> Miami ->New York would simply be GCTACGCTAGTATCGTACCTACGGATGCCG, or equiva-lently it could be represented in double stranded form with its complement sequence.

So how do we generate this? Synthesizing short single stranded DNA is now a routine pro-cess, so encoding the city names is straightforward. The molecules can be made by a machine

called a DNA synthesizer or even custom ordered from a third party. Itineraries can then beproduced from the city encodings by linking them together in proper order. To accomplishthis you can take advantage of the fact that DNA hybridizes with its complimentary se-quence.

For example, you can encode the routes between cities by encoding the compliment of thesecond half (last three letters) of the departure city and the first half (first three letters) of thearrival city. For example the route between Miami (CTACGG) and NY (ATGCCG) can bemade by taking the second half of the coding for Miami (CGG) and the first half of the cod-ing for NY (ATG). This gives CGGATG. By taking the complement of this you get, GCC-TAC, which not only uniquely represents the route from Miami to NY, but will connect the

DNA representing Miami and NY by hybridizing itself to the second half of the code repre-senting Miami (...CGG) and the first half of the code representing NY (ATG...).


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For example:

Random itineraries can be made by mixing city encodings with the route encodings. Finally,

the DNA strands can be connected together by an enzyme called ligase. What we are left withare strands of DNA representing itineraries with a random number of cities and random set ofroutes. For example:

We can be confident that we have all possible combinations including the correct one by us-ing an excess of DNA encodings, say 10^13 copies of each city and each route between cit-ies. Remember DNA is a highly compact data format, so numbers are on our side.

Part II: Select itineraries that start and end with the correct cities

Strategy: Selectively copy and amplify only the section of the DNA that starts with LA andends with NY by using the Polymerase Chain Reaction.

After Part I, we now have a test tube full of various lengths of DNA that encode possibleroutes between cities. What we want are routes that start with LA and end with NY. To ac-complish this we can use a technique called Polymerase Chain Reaction (PCR), which allowsyou to produce many copies of a specific sequence of DNA. PCR is an iterative process thatcycles through a series of copying events using an enzyme called polymerase. Polymerasewill copy a section of single stranded DNA starting at the position of a primer, a short pieceof DNA complimentary to one end of a section of the DNA that you're interested in. By se-lecting primers that flank the section of DNA you want to amplify, the polymerase preferen-tially amplifies the DNA between these primers, doubling the amount of DNA containing thissequence. After many iterations of PCR, the DNA you're working on is amplified exponen-tially. So to selectively amplify the itineraries that start and stop with our cities of interest, weuse primers that are complimentary to LA and NY. What we end up with after PCR is a test


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tube full of double stranded DNA of various lengths, encoding itineraries that start with LAand end with NY.

Part III: Select itineraries that contain the correct number of cities.

Strategy: Sort the DNA by length and select the DNA whose length corresponds to 5 cities.

Our test tube is now filled with DNA encoded itineraries that start with LA and end with NY,where the number of cities in between LA and NY varies. We now want to select those itin-eraries that are five cities long. To accomplish this we can use a technique called Gel Elec-trophoresis, which is a common procedure used to resolve the size of DNA. The basic princi-ple behind Gel Electrophoresis is to force DNA through a gel matrix by using an electricfield. DNA is a negatively charged molecule under most conditions, so if placed in an electricfield it will be attracted to the positive potential. However since the charge density of DNA isconstant (charge per length) long pieces of DNA move as fast as short pieces when suspend-ed in a fluid. This is why you use a gel matrix. The gel is made up of a polymer that forms ameshwork of linked strands. The DNA now is forced to thread its way through the tiny spac-es between these strands, which slows down the DNA at different rates depending on itslength. What we typically end up with after running a gel is a series of DNA bands, with eachband corresponding to a certain length. We can then simply cut out the band of interest to iso-late DNA of a specific length. Since we known that each city is encoded with 6 base pairs ofDNA, knowing the length of the itinerary gives us the number of cities. In this case we wouldisolate the DNA that was 30 base pairs long (5 cities times 6 base pairs).


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Part IV: Select itineraries that have a complete set of cities

Strategy: Successively filter the DNA molecules by city, one city at a time. Since the DNAwe start with contains five cities, we will be left with strands that encode each city once.

DNA containing a specific sequence can be purified from a sample of mixed DNA by a tech-nique called affinity purification. This is accomplished by attaching the compliment of thesequence in question to a substrate like a magnetic bead. The beads are then mixed with theDNA. DNA, which contains the sequence you're after then hybridizes with the complementsequence on the beads. These beads can then be retrieved and the DNA isolated.

So we now affinity purify fives times, using a different city complement for eachrun. For example, for the first run we use L.A.'- beads (where the ' indicates compli-ment strand) to fish out DNA sequences which contain the encoding for L.A. (whichshould be all the DNA because of step 3), the next run we use Dallas'- beads, andthen Chicago'- beads, Miami'- beads, and finally NY'- beads. The order isnt important.If an itinerary is missing a city, then it will not be "fished out" during one of theruns and will be removed from the candidate pool. What we are left with are the areitineraries that start in LA, visit each city once, and end in NY. This is exactly whatwe are looking for. If the answer exists we would retrieve it at this step.


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DNA CRYPTOGRAPHY

DNA-based Cryptography which puts an argument forward that the high level computation-

al ability and incredibly compact information storage media of DNA computing has the pos-

sibility of DNA based cryptography based on one time pads. They argue that current practi-

cal applications of cryptographic systems based on one-time pads is limited to the confines of

conventional electronic media whereas as small amount of DNA can suffice for a huge one

time pad for use in public key infrastructure (PKI). [1]

To put this into terms of the common Alice and Bob description of secure data transmission

and reception, they are basing their argument of DNA cryptography on Bob providing Alice

his public key, and Alice will use it to send an encrypted message to him. The potential

eavesdropper, Eve, will have an incredible amount of work to perform to attempt decryption

of their transmission than either Alice or Bob.

Public key encryption splits the key up into a public key for encryption and a secret key for

decryption. It's not possible to determine the secret key from the public key. Bob generates apair of keys and tells everyone his public key, while only he knows his secret key. Anyone

can use Bob's public key to send him an encrypted message, but only Bob knows the secret

key to decrypt it. This scheme allows Alice and Bob to communicate in secret without hav-

ing to physically meet as in symmetric encryption methods. [15]


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Fig 5. Public Key Encryption illustrated.

Injecting DNA cryptography into the common PKI scenario, the researchers from Duke argue

that we have the ability to follow the same inherent pattern of PKI but using the inherent

massively parallel computing properties of DNA bonding to perform the encryption and de-

cryption of the public and private keys.

It can easily be argued that DNA computing is just classical computing, albeit highly parallel-

ized; thus with a large enough key, one should be able to thwart any DNA computer that can

be built. This puts the idea of this form of DNA computing at great risk in the field of cryp-

tography. As well, the obstacles of utilizing this kind of technology outside of a lab are ex-

tremely high.


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ORIGINS OF STEGANOGRAPHY

Steganography is a variety of encryption that completely hides text or graphics, usually unen-

crypted, within other text or graphics that are electronically transmitted.

The term steganography derives from the Greek words steganos meaning hidden and

graphein meaning to write. One of the early Grecian methods of steganography was to shave

the head of a messenger, tattoo the message to be hidden .

Throughout our history there have been many other forms of steganography used to hidemessages such as the use of null ciphers, invisible inks and others. In World War II for ex-

ample, German cryptographers devised a method of using microdots to conceal messages

within messages themselves.

More recently, computer technology and the Internet have provided a medium for steganog-

raphy that has been unseen in the past. The ability to transfer text and images is now instan-

taneous and accessible by individuals virtually everywhere on the planet. It has been report-

ed that the Al Queda network of terrorists may have used steganographic means to hide their

communications in organizing the September 11th attacks on the United States of America.

Readily available software applications such as the freeware application JPHide and JPSeek

will encrypt messages with the common JPG format of graphic files. Other applications give

the user the ability to hide data within other graphic formats such as GIF or BMP and audio

formats such as MP3. Messages can now be hidden in the inconspicuous advertising bannersof web pages and the music files we listen to.


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DNA STEGANOGRAPHY

Experiments in DNA Steganography have been conducted by Carter Bancroft and his teamat the Mt. Sinai School of Medicine to encrypt hidden messages within microdots.

The principles used in this experiment used a simple code to convert the letters of the alpha-

bet into combinations of the four bases which make up DNA and create a strand of DNA

based on that code. A piece of DNA spelling out the message to be hidden is synthetically

created which contains the secret encrypted message in the middle plus short marker se-quences at the ends of the message. The encoded piece of DNA is then placed into a normal

piece of human DNA which is then mixed with DNA strands of similar length. The mixture

is then dried on to paper that can be cutup into microdots with each dot containing billions of

strands of DNA. Not only is the microdot difficult to detect on the plain message medium

but only one strand of those billions within the microdot contains the message.

The key to decrypting the message lies in knowing which markers on each end of the DNA

are the correct ones which mean there must be some sort of shared secret that is transmitted

previously for this type of transmission to work successfully. Once the strand is determined

via identifying the markers, the recipient uses polymerase chain reaction to multiply only the

DNA which contains the message and applies the simple code to finally decode the true mes-

sage. [2] Utilizing these methods, Bancroft and his team were successfully able to encode

and decode the famous message June 6 Invasion: Normandy within a microdot placed in the

full stops on a posted typed letter.


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Fig 6. DNA Steganography. a, Structure of secret message DNA strand illustrating marker

sequences. b, key used to encode message in DNA. c, Gel analysis of DNA strand. d, Se-

quence of cloned product of PCR amplification and resulting encoded message. [8]

The DNA microdot team does see this technology having applications in another field

howeverthat of authentication. With the amount of plant and animal genetic engineering

that is taking place today and will continue to do so in the future, this methodology would

allow engineers to place DNA authentication stamps within organisms they are working with

to easily detect counterfeits or copyright infringements.


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DNA AUTHENTICATION

It is worth mentioning that DNA authentication is currently at work in the marketplace todayalbeit not in the genetic engineering form envisioned by Bancroft and his team. Forms of

DNA authentication have already been used for such items as the official clothing from the

Sydney Olympic Games, sports collectibles and limited edition art markets such as original

animation cells distributed by the Hanna Barbara group of artists.

In the case of the clothing used in the Sydney Olympic Games, a Canadian company namedDNA Technologies was able to showcase its DNA-tagging abilities on the world stage in the

summer of 2000. All Olympic merchandise from shirts and hats to pins and coffee mugs

were tagged with special ink that contained DNA taken from an unnamed Australian athlete.

DNA was taken via saliva samples from the athlete and mixed into existing ink compounds

which was in turn used in the regular merchandise manufacturing process. A hand held scan-

ner is then used to scan the inked area of the clothing to determine if a piece of merchandise

is authentic or not. As it is estimated that the human genome is roughly 3 billion base pairs in

size, and the samples taken were from a random athlete from a Olympic team of hundreds,

the possibility of counterfeiting this merchandise is difficult to say the least. For the Sydney

games, DNA inks were applied too nearly 50 million items at a cost of about five cents each,

including licensing, databasing , and back-end support.

There are possibilities of this type of technology to be used in the arenas of currency and oth-

er such brandable items where existing authentication methods such as holograms are proving

ineffective and costly. DNA-tagging is much cheaper in comparison and ultimately more dif-

ficult to thwart.


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ADVANTAGES AND DISADVANTAGES

The advantages presented by a DNA computer are amazing. Their capacity for memory stor-age is tremendous. Also, they are inexpensive to build, being made of common biological

materials. Many of the DNA molecules could be reused with a little splicing, so the whole

computer is really materialistically very efficient.

Then, their computational power is incomparable to anything in existence. First and foremost,

DNA computing is useful because it has a capacity lacked by all current electronics-based

computers: its massively parallel nature. What does this mean, you ask? Well, essentially

while DNA can only carry out computations slowly, DNA computers can perform a stagger-

ing number of calculations simultaneously; specifically, on the order of 10^9 calculations per

mL of DNA per second!

This capability of multiple co temporal calculations immediately lends itself to several clas-

ses of problems which a modern electronic computer could never even approach solving. To

give you an idea of the difference in time, a calculation that would take 10^22 modern com-

puters working in parallel to complete in the span of one human's life would take one DNAcomputer only 1 year to polish off!

However, DNA computers do have their disadvantages. Although Adleman's first applica-

tion of the computer took only milliseconds to produce a solution, it took about a week to fish

the solution molecules out from the rest of the possible path molecules that had formed. To

make these computers more realistically viable, the DNA splicing and selection equipment

needs to be refined for this purpose and better methods for fishing developed.

There is also no guarantee that the solution produced will necessarily be the absolute best

solution, though it will certainly be a very good one, arrived at in a much shorter time than

with a conventional computer. DNA computers could not (at this point) replace traditional

computers. They are not programmable and the average dunce can not sit down at a familiar

keyboard and get to work. Some think that in the future, computers will be a combination of

the current models and DNA, using the most attractive features of both to create a vastly im-

proved total product. However, research is ongoing in doing Boolean logic with DNA anddesigning universal (programmable) DNA computers.


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And of course we are talking about DNA here, the genetic code of life itself. It certainly has

been the molecule of this century and most likely the next one. Considering all the attention

that DNA has garnered, it isnt too hard to imagine that one day we might have the tools and

talent to produce a small integrated desktop machine that uses DNA, or a DNA-like biopoly-

mer, as a computing substrate along with set of designer enzymes. Perhaps it wont be used to

play Quake IV or surf the web -- things that traditional computers are good at -- but it certain-

ly might be used in the study of logic, encryption, genetic programming and algorithms, au-

tomata, language systems, and lots of other interesting things that haven't even been invented

yet.

Advantages

SpeedConventional computers can perform approximately 100 MIPS (millions of instruc-

tion per second). Combining DNA strands as demonstrated by Adleman, made computations

equivalent to9

10 or better, arguably over 100 times faster than the fastest computer. The

inherent parallelism of DNA computing was staggering.

Minimal Storage RequirementsDNA stores memory at a density of about 1 bit per cubic

nanometer where conventional storage media requires12

10 cubic nanometers to store 1 bit.

In essence, mankinds collective knowledge could theoretically be stored in a small bucket of

DNA solution.

Minimal Power Requirements - There is no power required for DNA computing while thecomputation is taking place. The chemical bonds that are the building blocks of DNA happen

without any outside power source. There is no comparison to the power requirements of

conventional computers.


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MODELS AND FORMATS OF DNA COMPUTATION

In the two years that followed, a lot of theoretical work has been done on generalizing

Adleman's approach in order to define a general-purpose DNA-based molecular computerthat could also be implemented by an in vitro system. Lipton generalized Adleman's modeland showed how his model can encompass solutions to other NP-complete problems. Theother model is by splicing operation proposed by Head and vigrously followed by many re-searchers using formal language theory.

It is shown that the generative power of finite extended splicing systems is equal to that ofTuring Machines. Afterwords, Paun and others introduced the so-called sticker model. Unlikeprevious models, the sticker mode has a memory that can both read and written to, and em-ploys reusable DNA. Also there is a proposal about the tendency of DNA structures to self-assemble as a computational tool. They show that the self-assembly of complex branches

known as double cross-overs into two-dimensional sheets or three-dimensional solids is acomputationally powerful model.However, there are some impediments to effective computation by these models. It is a com-mon feature of all the proposed implementations that the biological operations to be used areassumed to be error-free. An operation central to and frequently employed in most models isthe extraction of DNA strands containing a certain sequence (known as removal by DNA hy-bridization).

The most important problem with this method is that extraction is not 100% efficient andmay at times inadvertently remove strands that do not contain the specified sequence. Espe-cially for a large problem, the number of extractions required may run into hundreds, or even

thousands resulting a high probability of incorrect hybridization.Thus, a novel error-resistant model of DNA computation has been proposed by Alan Gibbonsand his team that obviates the need for hybridization extraction within the main body of thecomputation.Like previous models, this model is particularly effective for algorithmic description. It issufficiently strong to solve any of the problems in the class NC and the authors have givenDNA algorithms for 3-vertex-colorability problem, Permutations Problem, Hamiltonian PathProblem, the Subgraph isomorphism problem, and the Maximum clique and maximum inde-pendent set problem.There are two general formats in which complex combinatorial sets of DNA molecules maybe manipulated. in solution ( solution-phase format) attached to a surface (solid-phase format)The solid-phase format possesses many important advantages over the solution-phase format.A Facilitated sample handling.With the DNA molecules attached to a support, the experimental manipulations are very sim-ple. They are addition of a solution to the support and removal (washing) to a solution fromthe support. These steps are readily automated. Decreased losses during sample handlingA Reduction of interference between oligonucleotidesA Solid-phase chemistry permits facile purification of the DNA molecules at every step of

the process.


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PITFALLS OF DNA COMPUTING

The idea of using DNA to solve computational problems is certainly intriguing and elegant,and DNA does provide a massive parallelism far beyond what is available on existing silicon-based computers. However, there are many technological hurdles to overcome. We give be-low one of the huge fundamental problem to be solved to attain the goal of designing univer-sally programmable molecular computer.

The fundamental problem is that, the function of 2n is exponential whether it counts time ormolecules. It has been estimated that Adleman's Hamiltonian path problem, if enhanced to 50or 100 cities, would require tons of DNA. The minimum amount of required DNA for Lip-

ton's SAT method needs a few grams of DNA molecules for 70 variables.

If this is increased to 100 variables, the minimum DNA requirement of millions of kilo-grams.Thus raw idea of brute-force enumeration is not going to work beyond modest problemsizes. Thus it is imperative to bring forth new revolutionary ideas to make this notion ofDNA-based computing to work realistically. Only time and investment will tell where theinitial ideas for DNA computing from those experts will lead. Many enhancive ideas havebeen published but all of them suffer under this fundamental problem. Hopefully the futuremolecular computation methods may bring forth new revolutionary ideas to overcome this

very fundamental as well as significant hurdle


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THE FUTURE OF DNA COMPTUING

Some centers of research in this area are at the University of Southern California at Los An-geles, with Dr. Adleman, Princeton, with Dr. Richard Lipton and his graduate students Dan

Boneh and Eric Baum, and the NEC Research Institute in Princeton, NJ. With others else-

where, they are developing new branches in this young field. Advancements are being made

in cryptography. Researchers are working on decreasing error in and damage to the DNA

during the computations/reactions. The Princeton contingent has published papers on models

for universal DNA computers, while others have described methods for doing addition and

matrix multiplication with these computers.

Currently, molecular computing is a field with a great deal of potential, but few results of

practical value. In the wake of Adleman's solution of the Hamiltonian path problem, there

came a host of other articles on computation with DNA; however, most of them were purely

theoretical. Currently, a functional DNA "computer" of the type most people are familiar with

lies many years in the future. But work continues: in his article Speeding Up Computation via

Molecular Biology Lipton shows how DNA can be used to construct a Turing machine, a

universal computer capable of performing any calculation. While it currently exists only in

theory, it's possible that in the years to come computers based on the work of Adleman, Lip-

ton, and others will come to replace traditional silicon-based machines.

The field of DNA computing is truly exciting for the revolution it implies will occur within

the next few years. It also demonstrates the current trend of merging and lack of distinction

between the sciences, where a computer scientist can mess around with biology equipment

and come up with something new and valuable.
http://www.neci.nj.nec.com/ftp://ftp.cs.princeton.edu/pub/people/rjl/bio.psftp://ftp.cs.princeton.edu/pub/people/rjl/bio.psftp://ftp.cs.princeton.edu/pub/people/rjl/bio.psftp://ftp.cs.princeton.edu/pub/people/rjl/bio.pshttp://www.neci.nj.nec.com/


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CONCLUSION

So will DNA ever be used to solve a traveling salesman problem with a higher number of cit-

ies than can be done with traditional computers? Well, considering that the record is a whop-

ping 13,509 cities, it certainly will not be done with the procedure described above. It took

this group only three months, using three Digital AlphaServer 4100s (a total of 12 processors)

and a cluster of 32 Pentium-II PCs.

The solution was possible not because of brute force computing power, but because they used

some very efficient branching rules. This first demonstration of DNA computing used a ra-

ther unsophisticated algorithm, but as the formalism of DNA computing becomes refined,

new algorithms perhaps will one day allow DNA to overtake conventional computation andset a new record.

On the side of the "hardware" (or should I say "wetware"), improvements in biotechnology

are happening at a rate similar to the advances made in the semiconductor industry. For in-

stance, look at sequencing; what once took a graduate student 5 years to do for a Ph.D thesis

takes Celerajust one day. With the amount of government funded research dollars flowing

into genetic-related R&D and with the large potential payoffs from the lucrative pharmaceu-

tical and medical-related markets, this isn't surprising.

Just look at the number of advances in DNA-related technology that happened in the last five

years. Today we have not one but several companies making "DNA chips," where DNA

strands are attached to a silicon substrate in large arrays (for example Affymetrix's genechip).

Production technology of MEMS is advancing rapidly, allowing for novel integrated small

scale DNA processing devices. The Human Genome Project is producing rapid innovations

in sequencing technology. The future of DNA manipulation is speed, automation, and minia-turization.

And of course we are talking about DNA here, the genetic code of life itself. It certainly has

been the molecule of this century and most likely the next one. Considering all the attention

that DNA has garnered, it isnt too hard to imagine that one day we might have the tools and

talent to produce a small integrated desktop machine that uses DNA, or a DNA-like biopoly-

mer, as a computing substrate along with set of designer enzymes.
http://www.crpc.rice.edu/CRPC/newsletters/sum98/news_tsp.htmlhttp://www.celera.com/celerascience/index.cfmhttp://www.affymetrix.com/http://www.affymetrix.com/http://www.celera.com/celerascience/index.cfmhttp://www.crpc.rice.edu/CRPC/newsletters/sum98/news_tsp.html


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Perhaps it wont be used to play Quake IV or surf the web -- things that traditional comput-

ers are good at -- but it certainly might be used in the study of logic, encryption, genetic pro-

gramming and algorithms, automata, language systems, and lots of other interesting things

that haven't even been invented yet.

The field of DNA computing is still in its infancy and the applications for this technology are

still not fully understood. Is DNA computing viableperhaps, but the obstacles that face the

field such as the extrapolation and practical computational environments required are daunt-

ing. DNA authentication methods on the other hand have shown great promise in the

marketplace of today and it is hoped that its applications will continue to expand.

The beauty of both these DNA research trends is found in the possibility of mankinds utiliza-

tion of its very life building blocks to solve its most difficult problems. DNA computing re-

search has resulted in significant progress towards the ability to create molecules with the

desired properties . This ability could have important applications in biology ,chemistry and

medicine,a strong argument for continued research.


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REFERENCES

[1] DIMACS Proceedings: DNA Based Computers I (#27), II (#44), III (#48), IV (Special Issueof Biosystems), V(MIT, June 1999)

[2] Other: Genetic Programming 1 (Stanford, 1997), Genetic Programming 2 (Wisconsin-Madison, 1998), IEEE International Conference on Evolutionary Computation (Indianapolis,1997)

[3] Richard P. Feynman, There's Plenty of Room at the Bottom. In D. Gilbert, ed., Miniaturi-zation, 282-296. Reinhold (1961)

[4] Leonard Adleman, Molecular computation of solutions to combinatorial problems. Sci-ence 266, 1021-1024 (1994)

[5] Martyn Amos, Alan Gibbons and David Hodgson, Error-resistant Implementation ofDNA Computations. In Proceedings of the Second Annual Meeting on DNA Based Comput-ers, Princeton University. American Mathematical Society (1996) (To appear)

[6] Michael R. Garey and David S. Johnson, Computers and Intractability: A Guide to theTheory of NP-Completeness. W. H. Freeman and Company (1979)

[7] http://www.wi.leidenuniv.nl/~jdassen/dna.html

[8] http://dope.caltech.edu/winfree/DNA.html

[9] http://www.msci.memphis.edu/~garzonm/bmc.html

dna compting

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