According to 2003

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DNA Computing

Seminar Report

Submitted in partial fulfillment of degree of

Bachelor of Technology

In

Computer Science & Engineering

By

Mr. Hari Prakash Tiwari

Under the guidance of

Prof. Harender Singh

HOD(Computer Sc. & Engg.)

R. D. ENGINEERING COLLEGE

DUHAI GHAZIABAD

(Affiliated to Uttar Pradesh Technical University Lucknow)

2009-2010


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CERTIFICATE

This is to certify that the Seminar Report titled DNA Computing submitted by Hari Prakash

Tiwari student of B.Tech (CSE) Degree course from Uttar Pradesh Technical University, has

undergone study seminar work in R. D. Engineering College in partial fulfilment of the

requirements degree is a bona-fide record of the work done by his under my supervision.

His attendance, discipline and overall conduct are found good.

______________________

Signature of the Co-ordinator Signature of the

HOD

Place: R. D. Engineering College

Date:


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ACKNOWLEDGEMENT

It is my duty to acknowledge with gratitude the generous help that I have received from

Harender Singh,HOD CSE and Dr. B. Kumar Director of R. D. Engineering College,

Ghaziabad. They had provided us all the resources that are required for our project to be

operational.

We are greatful to Er. Harendra Singh,HOD ofR. D. Engineering College, who has given me

methodology for my seminar report named DNA Computing. His infinite suggestions, ideas

and specific tasks given by him steered the study to the totality.

Also,I thank to my all faculty members those who have encorporated in this work.

Hari Prakash Tiwari

0623110020


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ABSTRACT

Inroduction:

DNA computing, also known as molecular computing, is a new approach to massively parallelcomputation based on groundbreaking work by Adleman . In November of 1994, Dr. Leonard

Adleman wrote the first paper on DNA computing. In this paper, he found a way to solve the"Hamiltonian path problem," which involves finding all the possible paths between a certain

number of vertices. It is also known as the "traveling salesman problem." This name comes fromviewing each vertex as a city, with the problem to find all possible routes for a salesman passing

through each of these cities .

Computers today all use binary codes - 1's and 0's or on's and off's. These codes are the basis forall possible calculations a computer is able to perform. Because the DNA molecule is also a

code, Adleman saw the possibility of employing DNA as a molecular computer. However, ratherthan relying in the position of electronic switches in a microchip, Adleman relied on the much

faster reactions of DNA nucleotides binding with their complements, a brute force method thatwould indeed work

A DNA computer is a collection of DNA strands that have been specially selected to aid in the

search of solutions for some problems. DNA computing results in parallelism, which means thatwhen enough DNA information is given, huge problems can be solved by invoking a parallel

search .

A nanocomputerthat uses DNA (deoxyribonucleic acids) to store information and perform

complex calculations.

In 1994, University of Southern California computer scientist Leonard Adelman suggested thatDNA could be used to solve complex mathematical problems. Adelman found a way to harness

the power of DNA to solve the Hamiltonian path problem (the traveling salesman problem),whose solution required finding a path from start to end going through all the points (cities) only

once.

Each city was encoded as its own DNA sequence (DNA sequence consists of a series ofnucleotides represented by the letters A, T, G, C).

The DNA sequences were set to replicate and create trillions of new sequences based on the

initial input sequences in a matter of seconds (called DNA hybridization). The theory holds thatthe solution to the problem was one of the new sequence strands. By process of elimination, the

correct solution would be obtained.


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Adelman's experiment is regarded as the first example of true nanotechnology.

The main benefit of using DNA computers to solve complex problems is that different possiblesolutions are created all at once. This is known asparallel processing. Humans and most

electronic computers must attempt to solve the problem one process at a time (linear processing).

DNA itself provides the added benefits of being a cheap, energy-efficient resource.

In a different perspective, more than 10 trillion DNA molecules can fit into an area no larger than

1 cubic centimeter. With this, a DNA computer could hold 10 terabytes of data and perform 10trillion calculations at a time.


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TABLE OF CONTENTS

CHAPTER NO. TITLE PAGE NO.

Cover Page i

Certificate ii

Acknowledgement iii

Abstract iv

1. Introductionvii

2. Why we need DNA computer.....xv

3. Different form of computing...xvi

3.1.Peptide Computing ..xvi

3.2.Parellel Computing...xvi

3.3.Quantum Computer....xvi

4. How does DNA computer worksxviii

5. Molecular Models of DNA ....xxi

6. Databases for DNA molecular modelsand sequences ..xxvii

7. Applications of DNA computing.xxix

8. Drawbacks..xxxi

9.Conclusionxxxii

10.References.xxxiii


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Introduction

Contents

y Components ofDNA

y Purine Bases

y Pyrimidine Bases

y Deoxyribose Sugar

y Nucleosides

y Nucleotides

y BasePairs

y DNA Backbone

y DNADouble Helix

y

DNA Helix Axis

Components of DNA

DNA is a polymer. The monomer units of DNA are nucleotides, and the polymer is known as a"polynucleotide." Each nucleotide consists of a 5-carbon sugar (deoxyribose), a nitrogen

containing base attached to the sugar, and a phosphate group. There are four different types ofnucleotides found in DNA, differing only in the nitrogenous base. The four nucleotides are given

one letter abbreviations as shorthand for the four bases.

y A is for adenine

y G is for guanine

y C is for cytosine

y T is for thymine


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Purine Bases

Adenine and guanine are purines. Purines are the larger of the two types of bases found in DNA.

Structures are shown below:

Structure of A and G

The 9 atoms that make up the fused rings (5 carbon, 4 nitrogen) are numbered 1-9. All ringatoms lie in the same plane.

Pyrimidine Bases

Cytosine and thymine are pyrimidines. The 6 stoms (4 carbon, 2 nitrogen) are numbered 1-6.

Like purines, all pyrimidine ring atoms lie in the same plane.

Structure of C and T


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Deoxyribose Sugar

The deoxyribose sugar of the DNA backbone has 5 carbons and 3 oxygens. The carbon atoms

are numbered 1', 2', 3', 4', and 5' to distinguish from the numbering of the atoms of the purine andpyrmidine rings. The hydroxyl groups on the 5'- and 3'- carbons link to the phosphate groups to

form the DNA backbone. Deoxyribose lacks an hydroxyl group at the 2'-position whencompared to ribose, the sugar component of RNA.

Structure of deoxyribose

Nucleosides

A nucleoside is one of the four DNA bases covalently attached to the C1' position of a sugar. The

sugar in deoxynucleosides is 2'-deoxyribose. The sugar in ribonucleosides is ribose. Nucleosidesdiffer from nucleotides in that they lack phosphate groups. The four different nucleosides of

DNA are deoxyadenosine (dA), deoxyguanosine (dG), deoxycytosine (dC), and(deoxy)thymidine (dT, or T).


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Example of DNA Backbone: 5'-d(CGAAT):

Features of the 5'-d(CGAAT) structure:

y Alternatingbackbone of deoxyriboseand phosphodiester groups

y Chainhasa direction (knownas polarity), 5'- to 3'- from top to bottom

y Oxygens (red atoms) of phosphatesare polar and negativelycharged

y

A,G,C,and Tbasescanextend away from chain,and stackatop each other

y Basesarehydrophobic

DNA Double Helix

DNA is a normally double stranded macromolecule. Two polynucleotide chains, held together by

weak thermodynamic forces, form a DNA molecule.


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Structure of DNA Double Helix

Features of the DNA Double Helix

y Two DNAstrands form ahelical spiral, windingaround ahelix axis ina right-handed

spiral

y Thetwo polynucleotidechains run in opposite directions

yThesugar-phosphatebackbones ofthetwo DNAstrands wind around thehelix axis like

the railing ofasprial staircase

y Thebases ofthe individual nucleotidesare onthe inside ofthehelix,stacked ontop of

each other likethesteps ofaspiral staircase.

Base Pairs

Within the DNA double helix, A forms 2 hydrogen bonds with T on the opposite strand, and Gforms 3 hyrdorgen bonds with C on the opposite strand.


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Example of dA-dT base pair as found within DNA double helix

Example of dG-dC base pair as found within DNA double helix


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y dA-dTand dG-dCbase pairsarethesame length,and occupythesamespace withina

DNA doublehelix.ThereforetheDNA moleculehasauniform diameter.

y dA-dTand dG-dCbase pairscan occur inany order withinDNA molecules

DNA Helix Axis

The helix axis is most apparent from a view directly down the axis. The sugar-phosphatebackbone is on the outside of the helix where the polar phosphate groups (red and yellow atoms)

can interact with the polar environment. The nitrogen (blue atoms) containing bases are inside,stacking perpendicular to the helix axis.


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1.Why we need DNA computer

DNA computing is fundamentally similar toparallel computing in that it takes advantage of themany different molecules of DNA to try many different possibilities at once. [6]

DNA computing also offers much lower power consumption than traditional silicon computers.DNA uses adenine triphosphate (ATP) as fuel to allow ligation or as a means to heat the strand to

cause disassociation.[7]

Both strand hybridization and the hydrolysis of the DNA backbone canoccur 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 twoATP molecules, power output is still low. For instance, Kahan reports 109 transitions per second

with an energy consumption of 10-10 W,[8] and similarly Shapiro reports a system producing 7.5 x10

11outputs in 4000 sec resulting in an energy consumption rate of ~ 10

-10W.

[9]

For certain specialized problems, DNA computers are faster and smaller than any other computerbuilt 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 differentmodels 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, itstill 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. (Quantumcomputing, 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-Crickbasepairing and other DNA properties to make novel structures outof DNA. These structures can be used for DNA computing, but they do not have to be.

Additionally, DNA computing can be done without using the types of molecules made possibleby DNA nanotechnology


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2.Different form of computing1. Peptide Computing

Peptide computing is a form ofcomputing which usespeptides and molecularbiology, instead of traditional silicon-based computer technologies. The basis of this

computational model is the affinity ofantibodies towards peptide sequences. Similarto DNA computing, the parallel interactions of peptide sequences and antibodies have

been used by this model to solve a fewNP-complete problems. Specifically, thehamiltonian path problem (HPP) and some versions of the set cover problem are a

few NP-complete problems which have been solved using this computational modelso far. This model of computation has also been shown to be computationally

universal (or Turing complete).

This model of computation has some critical advantages overDNA computing. For

instance, while DNA is made of four building blocks,peptides are made of twentybuilding blocks. The peptide-antibody interactions are also more flexible with respect

to recognition and affinity than an interaction between a DNA strand and its reversecomplement. However, unlike DNA computing, this model is yet to be practically

realized. The main limitation is the availability of specific monoclonal antibodiesrequired by the model.

2. Parallel ComputingPeptide computing is a form ofcomputing which usespeptides and molecular biology,instead of traditional silicon-based computer technologies. The basis of this

computational model is the affinity ofantibodies towards peptide sequences. Similar toDNA computing, the parallel interactions of peptide sequences and antibodies have been

used by this model to solve a fewNP-complete problems. Specifically, the hamiltonianpath problem (HPP) and some versions of the set cover problem are a few NP-complete

problems which have been solved using this computational model so far. This model ofcomputation has also been shown to be computationally universal (or Turing complete).

This model of computation has some critical advantages overDNA computing. Forinstance, while DNA is made of four building blocks,peptides are made of twenty

building blocks. The peptide-antibody interactions are also more flexible with respectto recognition and affinity than an interaction between a DNA strand and its reverse

complement. However, unlike DNA computing, this model is yet to be practicallyrealized. The main limitation is the availability of specific monoclonal antibodies

required by the model.

3. Quantum ComputerA quantum computer is a device forcomputation that makes direct use ofquantum

mechanicalphenomena, such as superposition and entanglement, to perform operations


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on data. The basic principle behind quantum computation is that quantum properties canbe used to represent data and perform operations on these data.

[1]

Although quantum computing is still in its infancy, experiments have been carried out

in which quantum computational operations were executed on a very small number of

qubits (quantum bit). Both practical and theoretical research continues with interest,

and many national government and military funding

agencies support quantum computing research to develop quantum computers forboth civilian and national security purposes, such as cryptanalysis.[2]

If large-scale quantum computers can be built, they will be able to solve certainproblems much faster than any of our current classical computers (for example Shor's

algorithm). Quantum computers are different from other computers such as DNAcomputers and traditional computers based on transistors. Some computing

architectures such as optical computers[3]

may use classical superposition ofelectromagnetic waves. Without some specifically quantum mechanical resources

such as entanglement, it is conjectured that an exponential advantage over classicalcomputers is not possible.


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3.How does DNA computer worksEven as you read this article, computer chip manufacturers are furiously racing to make

the next microprocessorthat will topple speed records. Sooner or later, though, this

competition is bound to hit a wall. Microprocessors made of silicon will eventually reach

their limits of speed and miniaturization. Chip makers need a new material to produce

faster computing speeds.

You won't believe where scientists have found the new material they need to build the

next generation of microprocessors. Millions of natural supercomputers exist inside

living organisms, including your body. DNA (deoxyribonucleic acid) molecules, the

material ourgenes are made of, have the potential to perform calculations many times

faster than the world's most powerful human-built computers. DNA might one day be

integrated into a computer chip to create a so-called biochip that will push computers

even faster. DNA molecules have already been harnessed to perform complex

mathematical problems.

While still in their infancy,DNA computers will be capable of storing billions of times

more data than your personal computer. In this article, you'll learn how scientists are

using genetic material to create nano-computers that might take the place of silicon-based

computers in the next decade.

DNA Computing Technology

DNA computers can't be found at your local electronics store yet. The technology is still in

development, and didn't even exist as a concept a decade ago. In 1994, Leonard Adleman

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 fact, DNA is very similar toa computerhard drive in how it stores permanent information about your genes.

Adleman is often called the inventor of DNA computers. 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 "traveling salesman" problem. The goal of

the problem is to find the shortest route between a number of cities, going through each city only

once. As you add more cities to the problem, the problem becomes more difficult. Adleman

chose to find the shortest route between seven cities.

You could probably draw this problem out on paper and come to a solution faster than Adleman

did using his DNA test-tube computer. Here are the steps taken in the Adleman DNA computer

experiment:

1. Strands of DNA represent the seven cities. In genes, genetic coding is represented by

the letters A, T, C and G. Some sequence of these four letters represented each city and

possible flight path.

2. These molecules are then mixed in a test tube, with some of these DNA strands

sticking together. A chain of these strands represents a possible answer.

3. Within a few seconds, all of the possible combinations of DNA strands, which

represent answers, are created in the test tube.


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4. Adleman eliminates the wrong molecules through chemical reactions, which leaves

behind only the flight paths that connect all seven cities.

Surpassing Silicon?Although DNA computers haven't overtakensilicon-based microprocessors, researchers

have made some progress in using geneticcode for computation. In 2003,Israeli

scientists demonstrated a limited, butfunctioning, DNA computer. You can read

more about it atNational Geographic.

The success of the Adleman DNA computer proves that DNA can be used to calculate complex

mathematical problems. However, this early DNA computer is far from challenging silicon-

based computers in terms ofspeed. The Adleman DNA computer created a group of possible

answers very quickly, but it took days for Adleman to narrow down the possibilities. Another

drawback of his DNA computer is that it requires human assistance. The goal of the DNA

computing field is to create a device that can work independent of human involvement.Three years after Adleman's experiment, researchers at the University of Rochesterdeveloped

logic gates made of DNA. Logic gates are a vital part of how your computer carries out functions

that you command it to do. These gates convert binary code moving through the computer into a

series of signals that the computer uses to perform operations. Currently, logic gates interpret

input signals from silicon transistors, and convert those signals into an output signal that allows

the computer to perform complex functions.

The Rochester team's DNA logic gates are the first step toward creating a computer that has a

structure similar to that of an electronic PC. Instead of using electrical signals to perform logical

operations, these DNA logic gates rely on DNA code. They detect fragments ofgenetic material

as input, splice together these fragments and form a single output. For instance, a genetic gate

called the "And gate" links two DNA inputs by chemically binding them so they're locked in anend-to-end structure, similar to the way two Legos might be fastened by a third Lego between

them. The researchers believe that these logic gates might be combined with DNA microchips to

create a breakthrough in DNA computing.

DNA computer components -- logic gates and biochips -- will take years to develop into a

practical, workable DNA computer. If such a computer is ever built , scientists say that it will be

more compact, accurate and efficient than conventional computers. In the next section, we'll look

at how DNA computers could surpass their silicon-based predecessors, and what tasks these

computers would perform.

Silicon vs. DNA Microprocessors

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 afterIntel 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.


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DNA computers have the potential to take computing to new levels, picking up where Moore's

Law leaves off. There are several advantages to using DNA instead of silicon:

y As long as there are cellular organisms, there will always be a supply of DNA.

y The large supply of DNA makes it a cheap resource.

y Unlike the toxic materials used to make traditional microprocessors, DNA biochips

can be made cleanly.y 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 centimeter (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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4.Molecular Models of DNAMolecular models of DNA structures are representations of the molecular geometryand topology of Deoxyribonucleic acid (DNA) molecules using one of several means,

such as: closely packed spheres (CPKmodels) made of plastic, metal wires for 'skeletalmodels', graphic computations and animations by computers, artistic rendering, and so

on, with the aim of simplifying and presenting the essential, physical and chemical,properties of DNA molecular structures eitherin vivo orin vitro. Computer molecular

models also allow animations and molecular dynamics simulations that are veryimportant for understanding how DNA functions in vivo. Thus, an old standing dynamic

problem is how DNA "self-replication" takes place in living cells that should involvetransient uncoiling of supercoiled DNA fibers. Although DNA consists of relatively rigid,

very large elongated biopolymer molecules called "fibers" or chains (that are made ofrepeating nucleotide units of four basic types, attached to deoxyribose and phosphate

groups), its molecular structure in vivo undergoes dynamic configuration changes that

involve dynamically attached water molecules and ions. Supercoiling, packing withhistones in chromosome structures, and other such supramolecular aspects also involve invivoDNA topology which is even more complex than DNA molecular geometry, thus

turning molecular modeling of DNA into an especially challenging problem for bothmolecular biologists and biotechnologists. Like other large molecules and biopolymers,

DNA often exists in multiple stable geometries (that is, it exhibits conformationalisomerism) and configurational, quantum states which are close to each other in energy

on the potential energy surface of the DNA molecule. Such geometries can also becomputed, at least in principle, by employing ab initioquantum chemistry methods that

have high accuracy for small molecules. Such quantum geometries define an importantclass ofab initio molecular models of DNA whose exploration has barely started.

DNA computing biochip:3D

In an interesting twist of roles, the DNA molecule itself was proposed to be utilized forquantum computing. Both DNA nanostructures as well as DNA 'computing' biochipshave been built (see biochip image at right).

The more advanced, computer-based molecular models of DNA involve molecular

dynamics simulations as well as quantum mechanical computations of vibro-rotations,


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delocalized molecular orbitals (MOs),electric dipole moments,hydrogen-bonding, andso on.

Examples of DNA molecular models

Animated molecular models allow one to visually explore the three-dimensional (3D) structureof DNA. The first DNA model is a space-filling, orCPK, model of the DNA double-helixwhereas the third is an animated wire, or skeletal type, molecular model of DNA. The last two

DNA molecular models in this series depict quadruplex DNA that may be involved in certaincancers

[13][14]. The last figure on this panel is a molecular model of hydrogen bonds between

water molecules in ice that are similar to those found in DNA.

Images for DNA Structure Determination from X-Ray Patterns

The following images illustrate both the principles and the main steps involved in generating

structural information from X-ray diffraction studies of oriented DNA fibers with the help ofmolecular models of DNA that are combined with crystallographic and mathematical analysis of

the X-ray patterns. From left to right the gallery of images shows:

y

o First row:

y 1. Constructive X-ray interference, or diffraction, following Bragg's Law of X-ray"reflection by thecrystalplanes";


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y 2. A comparison of A-DNA (crystalline) and highly hydrated B-DNA (paracrystalline)X-ray diffraction, and respectively, X-ray scattering patterns (courtesy of Dr. Herbert R.

Wilson, FRS- see refs. list);y 3. Purified DNA precipitated in a water jug;

y 4. The major steps involved in DNA structure determination by X-ray crystallography

showing the important role played by molecular models of DNA structure in thisiterative, structure--determination process;o Secondrow:

y 5. Photo of a modern X-ray diffractometer employed for recording X-ray patterns ofDNA with major components: X-ray source, goniometer, sample holder, X-ray detector

and/or plate holder;y 6. Illustrated animation of an X-ray goniometer;

y 7. X-ray detector at the SLAC synchrotron facility;y 8. Neutron scattering facility at ISIS in UK;

o Thirdandfourth rows: Molecular modelsofDNAstructure at variousscales;figure #11 is an actual electron micrograph of a DNA fiber bundle, presumably of

a single bacterial chromosome loop.


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Paracrystalline lattice models of B-DNA structures

Aparacrystalline lattice, or paracrystal, is a molecular or atomic lattice with significant amounts(e.g., larger than a few percent) of partial disordering of molecular arranegements. Limiting

cases of the paracrystal model are nanostructures, such as glasses,liquids, etc., that may possessonly local ordering and no global order. Liquid crystals also have paracrystalline rather than

crystalline structures.

DNA Helix controversy in 1952

Highly hydrated B-DNA occurs naturally in living cells in such a paracrystalline state, which is adynamic one in spite of the relatively rigid DNA double-helix stabilized by parallel hydrogenbonds between the nucleotide base-pairs in the two complementary, helical DNA chains (see

figures). For simplicity most DNA molecular models ommit both water and ions dynamicallybound to B-DNA, and are thus less useful for understanding the dynamic behaviors of B-DNA in

vivo. The physical and mathematical analysis of X-ray[15][16]

and spectroscopic data forparacrystalline B-DNA is therefore much more complicated than that of crystalline, A-DNA X-

ray diffraction patterns. The paracrystal model is also important for DNA technological


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applications such as DNA nanotechnology. Novel techniques that combine X-ray diffraction ofDNA with X-ray microscopy in hydrated living cells are now also being developed (see, for

example,"Application of X-ray microscopy in the analysis of living hydrated cells"). Genomicand Biotechnology Applications of DNA molecular modeling

The following gallery of images illustrates various uses of DNA molecular modeling inGenomics and Biotechnology research applications from DNA repair to PCR and DNAnanostructures; each slide contains its own explanation and/or details. The first slide presents an

overview of DNA applications, including DNA molecular models, with emphasis on Genomicsand Biotechnology.

Gallery: DNA Molecular modeling applications


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6. Databases for DNA molecular models and sequences

X-ray diffraction

y NDB ID: UD0017 Database

y

X-ray Atlas -databasey PDB files of coordinates for nucleic acid structures from X-ray diffraction by NA (incl.

DNA) crystalsy Structure factors dowloadable files in CIF format

Neutron scattering

y ISIS neutron sourcey ISIS pulsed neutron source:A world centre for science with neutrons & muons at

Harwell, near Oxford, UK.

X-ray microscopy

y Application of X-ray microscopy in the analysis of living hydrated cells

Electron microscopy

y DNA under electron microscope

Atomic Force Microscopy (AFM)

Two-dimensional DNA junction arrays have been visualized by Atomic Force Microscopy(AFM)

[17]. Other imaging resources for AFM/Scanning probe microscopy(SPM) can be freely

accessed at:

Gallery of AFM Images


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Mass spectrometry--Maldi informatics


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7. Applications of DNA computing

Computational Gene

A computational gene[1]

[2]

[3]

is a molecularautomaton consisting of a structural part and a

functional part; and its design is such that it might work in a cellular environment. The

structural part is a naturally occurring gene, which is used as a skeleton to encode the input

and the transitions of the automaton (Fig. 1A). The conserved features of a structural gene

(e.g.,DNA polymerase binding site, start and stop codons, and splicing sites) serve as

constants of the computational gene, while the coding regions, the number ofexons and

introns, the position of start and stop codon, and the automata theoretical variables (symbols,

states, and transitions) are the design parameters of the computational gene. The constants

and the design parameters are linked by several logical and biochemical constraints (e.g.,

encoded automata theoretic variables must not be recognized as splicing junctions). Theinput of the automaton are molecular markers given by single stranded DNA (ssDNA)

molecules. These markers are signalling aberrant (e.g., carcinogenic) molecularphenotype

and turn on the self-assembly of the functional gene. If the input is accepted, the output

encodes a double stranded DNA (dsDNA) molecule, a functional gene which should be

successfully integrated into the cellulartranscription and translation machinery producing a

wild typeprotein or an anti-drug (Fig. 1B). Otherwise, a rejected input will assemble into a

partially dsDNA molecule which cannot be translated.

Challenges

Although mechanistically simple and quite robust on molecular level, several issues need to be

addressed before an in vivo implementation of computational genes can be considered. First, the

DNA material must be internalised into the cell, specifically into the nucleus. In fact, the transferof DNA orRNA throughbiological membranes is a key step in the drug delivery. Some resultsshow that nuclear localisation signals can be irreversibly linked to one end of the

oligonucleotides, forming an oligonucleotide-peptide conjugate that allows effectiveinternalisation of DNA into the nucleus

[9].

In addition, the DNA complexes should have low immunogenicity to guarantee their integrity in


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the cell and their resistance to cellularnucleases. Current strategies to eliminate nucleasesensitivity include modifications of the oligonucleotide backbone such as methylphosphonate

and phosphorothioate (S-ODN) oligodeoxynucleotides, but along with their increased stability,modified oligonucleotides often have altered pharmacologic properties.

Finally, similar to any other drug, DNA complexes could cause nonspecific and toxic sideeffects. In vivo applications of antisense oligonucleotides showed that toxicity is largely due to

impurities in the oligonucleotide preparation and lack of specifity of the particular sequence used

Undoubtedly, progress on antisense biotechnology will also result in a direct benefit to the model

of computational genes


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8. Drawbacks

y For small problems, computers can solve them easily. With increasing speed and

parallelism of CPU, more and more problems belong to this category.y For larger problems, there are many issues.

1. Limited applicability of the method.

2. The mess of DNA which is needed to represent the problem can become prohibitivelylarge. The mess is not scalable.

3. Synthesizing such long base pairs and large amount of DNA becomes a very difficulttask. Such synthesis is not scalable.

4. The preparation and processing time are just too long to make it worth while even if it isautomated.

5. Experiments can fail due to many reasons such as DNA degradation, the secondary

structure of DNA, etc.


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10. Conclusion

In conclusion, technology is always changing, and computer technology will soon take a drastic

change too. DNA Computing is very new concept which can be extended to achieve a very high

level of calculation.It can be used to medical field as well as in many fields and can be invoked

to reach high research.

Many issues to be overcome to produce a useful DNA computer.

It will not replace the current computers because it is application specific, but has a

potential to replace the high-end research oriented computers in future.

Nanotechnology?


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