seminar orginal

8/7/2019 seminar orginal

http://slidepdf.com/reader/full/seminar-orginal 1/30

DNA Computing 1

COLLEGE OF APPLIED SCIENCE

PEERUMADE

( Affiliated by Mahatma Gandhi University Kottayam )

SEMINAR REPORT

On

DNA COMPUTING

Submitted in partial fulfillment with the

requirement for the award of the degree

of Bachelor of Science in Electronics of

Mahatma Gandhi University, Kottayam.

Submitted By

NAME : JOM JOY KURIAN

REG NO : 8310

CAS Peerrmade BSc. Electronics.



DNA Computing 2

COLLEGE OF APPLIED SCIENCEPEERUMADE

( Affiliated by Mahatma Gandhi University )

CERTIFICATE

This is to certify that seminar report entitled as “ DNA COMPUTING ’’

being submitted by JOM JOY KURIAN ( 6 TH SEMESTER ) BSC

electronics with hard ware , CAS Peerumade, during the year 2011

in partial fulfillment of the requirements for the completion of BSC

degree of Mahatma Gandhi University is a bona fide record of the

work done by him under by guidance & supervision.

PRINCIPAL H O D SEMINAR GUIDE

Date :

Place :

ACKNOWLEDGEMENT




DNA Computing 3

Many people have contributed to the success of this. Although a single sentence

hardly suffices, I would like to thank almighty God for blessing us with His grace. I

extend my sincere and heart felt thanks to

Mr. .Jothys sir Head of the electronics for providing us the right ambience for

carrying out this work. …

………………

I am profoundly indebted to my seminar Guide Elezabath mis for innumerable

acts of timely advice encouragement and I sincerely express my gratitude to her.

I express my immense pleasure and thankfulness to all the teachers and staff of

the Department of electronics , for their cooperation and support.

Last but not the least , I thank all others, and especially my classmates who in

one way or another helped me in the successful completion of this.

JOM JOY KURIAN

ABSTRACT




DNA Computing 4

Molecular biologists are beginning to unravel the information-processing tools such as

enzymes that evolution has spent billions of years refining. These tools are now been

taken in large numbers of DNA molecules and using them as biological computer

processors.

Dr. Leonard Adleman, a well-known scientist, found a way to exploit the speed and

efficiency of the biological reactions to solve the “Hamiltonian path problem”, also

known as the “traveling salesman problem”.

Based on Dr. Adleman’s experiment, we will explain DNA computing, its algorithms,

how to manage DNA based computing and the advantages and disadvantages of DNA

computing.




DNA Computing 5

CONTENT


Introduction 6

History 7

DNA fundamentals 8

Principles of DNA Computing 11

Synthesizing 12

Algorithm 19

Hamiltanion path problem 20

DNA Computing technology 23

How DNA Computer will work? 24

Silicon v/s DNA Microprocessor 25

Present and future DNA Computers 26

Advantages 27

Disadvantages 28

Conclusion 29

Reference 30



DNA Computing 6

INTRODUCTION

DNA (Deoxyribose Nucleic Acid) computing, also known as

molecular computing is a new approach to massively parallel

computation based on groundbreaking work by Adleman. DNA

computing was proposed as a means of solving a class of intractable

computational problems in which the computing time can grow

exponentially with problem size (the 'NP-complete' or non-

deterministic polynomial time complete problems).A DNA computer

is basically a collection of specially selected DNA strands whosecombinations will result in the solution to some problem, depending

on the problem at hand. Technology is currently available both to

select the initial strands and to filter the final solution. DNA

computing is a new computational paradigm that employs

(bio)molecular manipulation to solve computational problems, at the

same time exploring natural processes as computational models. In

1994, Leonard Adleman at the Laboratory of Molecular Science,

Department of Computer Science, University of Southern California

surprised the scientific community by using the tools of molecular

biology to solve a different computational problem. The main idea was the encoding of data in

DNA strands and the use of tools from molecular biology to execute computational operations.

Besides the novelty of this approach, molecular computing has the potential to outperform

electronic computers. For example, DNA computations may use a billion times less energy

than an electronic computer while storing data in a trillion times less space. Moreover,

computing with DNA is highly parallel: In principle there could be billions upon trillions of

DNA molecules undergoing chemical reactions, tha is, performing computations,

simultaneously.


http://www.usc.edu/dept/molecular-science/fm-adleman.htm

http://www.usc.edu/dept/molecular-science/

http://www.usc.edu/dept/molecular-science/fm-adleman.htm

http://www.usc.edu/dept/molecular-science/



DNA Computing 7

History & Motivation

"Computers in the future may weigh no more than 1.5 tons." So said Popular Mechanics in1949. Most of us today, in the age of smart cards and wearable PCs would find that statement

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

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

supercomputers 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 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 great

challenges require computational capabilities that neither most powerful and distributed systems

could reach.

The idea that living cells and molecular complexes can be viewed as potential machiniccomponents dates back to the late 1950s, when Richard Feynman delivered his famous paper

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

realization of massively parallel computation using the techniques and chemistry of molecular

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

announced that he had solved a small instance of a computationally intractable problem 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 implement a simple,

massively parallel random search. He solved the traveling salesman problem 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 stored inapproximately one cubic nanometer. Others storage media, such as videotapes, can store 1 bit in

1,000,000,000,000 cubic nanometer.

(ii) Operations on DNA are massively parallel: a test tube of DNA can contain trillions of

strands. Each operation on a test tube of DNA is carried out on all strands in the tube in parallel.




DNA Computing 8

DNA Fundamentals

DNA (deoxyribonucleic acid) is a double stranded sequence of four nucleotides; the four

nucleotides that compose a strand of DNA are as follows:

1) adenine (A),

2) guanine (G),

3) cytosine(C),

4) thymine (T);

they are often called bases. DNA supports two key functions for life:

coding for the production of proteins,

self-replication.

Each deoxyribonucleotide consists of three components:

a sugar — deoxyribose

five carbon atoms: 1´ to 5´

hydroxyl group (OH) attached to 3´ carbon

a phosphate group






DNA Computing 10

The DNA monomers can link in two ways:

Phosphodiester bond Hydrogen bond

The four nucleotides adenine (A), guanine (G), cytosine (C), and thymine (T) compose a strand

of DNA. Each DNA strand has two different ends that determine its polarity: the 3’end, and the

5’end. The double helix is an anti-parallel (two strands of opposite polarity) bonding of two

complementary strands.




DNA Computing 11

The structure of DNA double helix

Principles of DNA Computing

DNA is the major information storage molecule in living cells, and billions of years of evolution

have tested and refined both this wonderful informational molecule and highly specific enzymes

that can either duplicate the information in DNA molecules or transmit this information to other

DNA molecules. Instead of using electrical impulses to represent bits of information, the DNA

computer uses the chemical properties of these molecules by examining the patterns of

combination or growth of the molecules or strings. DNA can do this through the manufacture of

enzymes, which are biological catalysts that could be called the ’software’, used to execute the

desired calculation.

A single strand of DNA is similar to a string consisting of a combination of four different

symbols A G C T. Mathematically this means we have at our disposal a letter alphabet, Σ = {A

GC T} to encode information which is more than enough considering that an electronic

computer needs only two digits and for the same purpose. In a DNA computer, computation

takes place in test tubes or on a glass slide coated in 24K gold. The input and output are both

strands of DNA, whose genetic sequences encode certain information. A program on a DNA

computer is executed as a series of biochemical operations, which have the effect of

synthesizing, extracting, modifying and cloning the DNA strands.

As concerning the operations that can be performed on DNA strands the proposed models of

DNA computation are based on various combinations of the following primitive bio-operations:




DNA Computing 12

Synthesizing

Mixing : combine the contents of two test tubes into a third one to achieve union. a

desired polynomial-length strand used in all models.

Annealing:

bond together two single-stranded complementary DNA sequences by cooling the solution.Annealing in vitro is known as hybridization




DNA Computing 13

Melting:

break apart a double-stranded DNA into its single-stranded complementary components by

heating the solution. Melting in vitro is also known under the name of denaturation.




DNA Computing 14

Amplifying (copying):

ake copies of DNA strands by using the Polymerase Chain Reaction PCRm. The DNA

polymerase enzymes perform several functions including replication of DNA. The

replication reaction requires a guiding DNA single-strand called template, and a shorter

oligonucleotide called a primer, that is annealed to it.




DNA Computing 15

Separating:-

the strands by length using a technique called gel electrophoresis that makes possible theseparation of strands by length.




DNA Computing 16

Extracting

those strands that contain a given pattern as a substring by using affinity purification.




DNA Computing 17

Cutting

DNA double-strands at specific sites by using commercially available restriction enzymes.

One class of enzymes, called restriction endonucleases, will recognize a specific short

sequence of DNA, known as a restriction site. Any double-stranded DNA that contains the

restriction site within its sequence is cut by the enzyme at that location.

Ligating:

paste DNA strands with compatible sticky ends by using DNA ligases. Indeed, another

enzyme called DNA ligase, will bond together, or ``ligate'', the end of a DNA strand to

another strand.




DNA Computing 18

Substituting:

substitute, insert or delete DNA sequences by using PCR site-specific oligonucleotide

mutagenesis.

Marking single strands by hybridization: complementary sequences are attached to the strands,

making them double-stranded. The reverse operation is unmarking of the double-strands

by denaturing, that is, by detaching the complementary strands. The marked sequences

will be double-stranded while the unmarked ones will be single-stranded.




DNA Computing 19

Destroying

the marked strands by using exonucleases, or by cutting all the marked strands with a

restriction enzyme and removing all the intact strands by gel electrophoresis. (By using

enzymes called exonucleases, either double-stranded or single-stranded DNA molecules

may be selectively destroyed. The exonucleases chew up DNA molecules from the endinward, and exist with specificity to either single-stranded or double-stranded form.)

Detecting and Reading:

given the contents of a tube, say ``yes'' if it contains at least one DNA strand, and ``no''

otherwise. PCR may be used to amplify the result and then a process called sequencing is used

to actually read the solution.

In Short, DNA computers work by encoding the problem to be solved in the language of DNA:

the base-four values A, T, C and G. Using this base four number system, the solution to any

conceivable problem can be encoded along a DNA strand like in a Turing machine tape. Every

possible sequence can be chemically created in a test tube on trillions of different DNA strands,

and the correct sequences can be filtered out using genetic engineering tools.

Algorithm

Turing machine:

A Turing machine is, as described in theoretical informatics, a machine which may

easily be described by a band, able to store information and a few operations on it. Each rule is

stated in the form: "In state n, if the head is reading symbol x, write symbol y, then move left or right one cell on the tape, and change the state to m." It was shown by Dr. Adleman that the

Turing machine has more computing power than every other deterministic machine especially

the computer itself. A Turing machine is an all-purpose machine, which may be used for every

computation.

One example of a Turing machine.




DNA Computing 20

The goal of the computer, which works with DNA, is to develop an all-purpose

computer like the Turing machine. As a matter of fact this is rather difficult. Every command

should be possible in every state of the machine and it should be readable (extractable) at every

point of time. Great efforts in research are necessary to implement this.

Recent experiments on DNA computing have shown that it is more efficient to use the power of DNA computing in specific algorithms where the mapping in DNA is easy and the

parallel computing power of DNA’s are useable. Specifically, problems which are hard to solve

with Turing machines may work better (in terms of speed and efficiency) with DNA

computing. These problems referred to, as NP-complete problems are not solvable in

polynomial time with Turing machines. This means that the number of steps to solve the

problems increase exponentially with the number of the input data.

NP-complete problems are common in the real world. Examples are the Hamiltonian path or

the Shortest-Path in a graph.

Example of DNA Computing :

The Hamiltonian Path Problem;-

In 1994 Leonard M. Adleman showed how to solve the Hamilton

Path Problem, using DNA computation.

A directed graph G with designated nodes vin and vout is said to

have a Hamiltonian path if and only if there exists a sequence of compatible one-way edges e1,e2, ...en that begins at vin, ends at vout and enters every other node exactly once. A simplified

version of this problem, known as the traveling salesman problem, poses the following question:given an arbitrary collection of cities through which a salesman must travel, what is the shortest

route linking those cities? This problem is difficult for conventional computers to solve because

it is a ”non-deterministic polynomial time problem”. These problems, when the instance size is

large, are intractable with conventional computers, but can be solved using massively parallel

computers like DNA computers. NP problems are intractable with deterministic

(conventional/serial) computers, but can be solved using non-deterministic (massively parallel)

computers. A DNA computer is a type of non-deterministic computer. The Hamiltonian Path

problem was chosen by Adleman because it is known as ”NP-complete”.

Directed graph with node 0 as source (Vin)

and node 6 as destination (Vout)




DNA Computing 21

Simplified graph

Hamiltonian path : Atlanta–Boston–Chicago–Detroit

Adleman´s AlgorithmInput: A directed graph G with n vertices, and designated vertices vin and vout.

Step 1:

Generate paths in G randomly in large quantities.

Step 2:

Reject all paths that

do not begin with vin and

do not end in vout.

Step 3:

Reject all paths that do not involve exactly n vertices.

Step 4:

For each of the n vertices v: reject all paths that do not involve v.

Output:

YES, if any path remains; NO, otherwise.

To implement step 1,

each node of the graph was encoded as a random 20-base strand of DNA. Then, for each edge

of the graph, a different 20-base oligonucleotide was generated that contains the second half of

the source code plus the first half of the target node.




DNA Computing 22

City DNA Name Complement Atlanta ACTTGCAG TGAACGTC Boston TCGGACTG AGCCTGACChicago GGCTATGT CCGATACA Detroit CCGAGCAA GGCTCGTT

City DNA Flight Number

Atlanta – Boston GCAGTCGGAtlanta – Detroit GCAGCCGA

Boston – Chicago ACTGGGCTBoston – Detroit ACTGCCGABoston – Atlanta ACTGACTTChicago – Detroit ATGTCCGA

To implement step 2, The product of step 1 was amplified by PCR using oligonucleotide primers representing vinand vout and ligase enzyme. This amplified and thus retained only those molecules encoding

paths that begin with vin and end with vout. ~1014 computations are carried out in a single

second.

For implementing step 3, Agarose gel electrophoresis allowed separation and recovery of DNA strands of the correct

length. The desired path, if it exists, would pass through all seven nodes, each of which was

assigned a length of 20 bases. Thus PCR products encoding the desired path would have to be

140 bp.

Step 4, was accomplished by successive use of affinity purification for each node other than the start

and end nodes. The solution strand has to be filtered from the test tube:

GCAG TCGG ACTG GGCT ATGT CCGAAtlanta → Boston → Chicago → Detroit

Thus we see in a graph with n vertices, there are a possible (n-1)! permutations of the

vertices between beginning and ending vertex.

To explore each permutation, a traditional computer must perform O(n!) operations to explore

all possible cycles. However, the DNA computing model only requires the representative oligos.

Once placed in solution, those oligos will anneal in parallel, providing all possible paths in the

graph at roughly the same time. That is equivalent to O(1) operations, or constant time. In

addition, no more space than what was originally provided is needed to contain the constructed paths.




DNA Computing 23

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 to

a computer hard 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 cityonly 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 stickingtogether. 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.

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

behind only the flight paths that connect all seven cities.

The success of the Adleman DNA computer proves that DNA can be used to calculate complexmathematical problems. However, this early DNA computer is far from challenging silicon-

based computers in terms of speed. 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 Rochester developed

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.




DNA Computing 24

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 of genetic material as input, splice

together these fragments and form a single output. For instance, a genetic gate called the "Andgate" links two DNA inputs by chemically binding them so they're locked in an end-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.

How DNA Computers Will Work:

Even as you read this article, computer chip manufacturers are furiously racing to make the next

microprocessor that 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 nextgeneration of microprocessors. Millions of natural supercomputers exist inside living organisms,

including your body. DNA (deoxyribonucleic acid) molecules, the material our genes 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

Silicon vs. DNA Microprocessors:






DNA Computing 26

A year ago, researchers from the Weizmann Institute of Science in Rehovot, Israel, unveiled

a programmable molecular computing machine composed of enzymes and DNA molecules

instead of silicon microchips. "This re-designed device uses its DNA input as its source of

fuel," said Ehud Shapiro, who led the Israeli research team. This computer can perform 330

trillion operations per second, more than 100,000 times the speed of the fastest PC.

While a desktop PC is designed to perform one calculation very fast, DNA strands produce

billions of potential answers simultaneously. This makes the DNA computer suitable for

solving "fuzzy logic" problems that have many possible solutions rather than the either/or

logic of binary computers. In the future, some speculate, there may be hybrid machines that

use traditional silicon for normal processing tasks but have DNA co-processors that can take

over specific tasks they would be more suitable for.

ADVANTAGES




DNA Computing 27

Perform millions of operations simultaneously.

Generate a complete set of potential solutions and conduct large parallel

searches.

Efficiently handle massive amounts of working memory.

They are inexpensive to build, being made of common biological materials.

block, thus effectively doubling our capacity to The clear advantage is that we have a

distinct memory block that encodes bits.

DNA computers show promise because they do not have the limitations of silicon-based

chips. For one, DNA based chip manufacturers will always have an ample supply of raw

materials as DNA exists in all living things; this means generally lower overhead costs.

Secondly, the DNA chip manufacture does not produce toxic by-products. Last but not the least,

DNA computers will be much smaller than silicon-based computers as one pound of DNA chips

can hold all the information stored in all the computers in the world.

With the use of DNA logic gates, a DNA computer the size of a teardrop will be more

powerful than today's most powerful supercomputer. A DNA chip less than the size of

a dime will have the capacity to perform 10 trillion parallel calculations at one time aswell as hold ten terabytes of data. The capacity to perform parallel calculations, much

more trillions of parallel calculations, is something silicon-based computers are not

able to do. As such, a complex mathematical problem that could take silicon-based

computers thousands of years to solve can be done by DNA computers in hours. For

this reason, the first use of DNA computers will most probably be cracking of codes,

route planning and complex simulations for the government.

DISADVANTAGES




DNA Computing 28

Generating solution sets, even for some relatively simple problems, mayrequire impractically large amounts of memory (lots and lots of DNA strands

are required)

Many empirical uncertainties, including those involving: actual error rates, the

generation of optimal encoding techniques, and the ability to perform necessary

bio-operations conveniently in vitro (for every correct answer there are millions

of incorrect paths generated that are worthless).

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.

CONCLUSION




DNA Computing 29

DNA computers will work through the use of DNA-based logic gates. These logic gates are very

much similar to what is used in our computers today with the only difference being the

composition of the input and output signals. In the current technology of logic gates, binary

codes from the silicon transistors are converted into instructions that can be carried out by the

computer. DNA computers, on the other hand, use DNA codes in place of electrical signals as

inputs to the DNA logic gates. DNA computers are, however, still in its infancy and though it

may be very fast in providing possible answers, narrowing these answers down still takes days.

The idea of DNA Based computing is to subvert the mechanisms produced by

evolution and use them to do data processing we want to do.

References




DNA Computing 30

Paun, G., Rozenberg, G., and Salomaa, A., DNA Computing,Springer,1998.

DNA COMPUTING-GRAPH ALGORITHMS [lec-12.pdf] G. P. Raja Sekhar,Dept. of Mathematics, IIT, Kharagpur

Leonard M. Adleman, Computing with DNA, Scientific American,August 1998.

From Microsoft to Biosoft Computing with DNA, Lila Kari, Departmentof Computer Science University of Western Ontario

L.Adleman. On constructing a molecular computer. 1st DIMACSworkshop on DNA based computers, Princeton, 1995. In DIMACSseries, vol.27 (1996)

L.Adleman, P.Rothemund, S.Roweis, E.Winfree. On applying molecularcomputation to the Data Encryption Standard. 2nd DIMACS workshopon DNA based computers, Princeton, 1996

Date post:	09-Apr-2018
Category:	Documents
Upload:	ahilreji
View:	221 times
Download:	0 times

seminar orginal

Documents