dna based computing final2 7

8/4/2019 DNA Based Computing Final2 7

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Sandipan Das

DNA Based Computing


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Outline

Introduction

Memory storage

Hamiltonian Path Problem(Adelman experiment)

Recent DNA technology

Evolution of DNA Computer

DNA V/s Silicon computer

Application

Benefits and limitation

Conclusion


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Need of DNA computer?

Moores Law states that silicon

microprocessors double in complexity roughly

every two years.

One day this will no longer hold true when

miniaturisation limits are reached. Intel

scientists say it will happen in about the year

2018.

Require a successor to silicon.


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What is DNA?

DNA stands for Deoxyribonucleic Acid

DNA represents the genetic blueprint of living

creatures

DNA contains instructions for assembling

cells

Every cell in human body has a complete setof DNA

DNA is unique for each individual


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Double Helix

Sides

Sugar-phosphate backbones

ladders

complementary base pairs

Adenine & Thymine

Guanine & Cytosine

Two strands are held together by

weak hydrogen bonds between the

complementary base pairs

Source: Human Physiology: From Cells to System

4thEd., L. Sherwood, Brooks/Cole, 2001, C-3


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Composed of four nucleotides(+ sugar-phosphate backbone)A AdenineT ThymineC CytosineG Guanine

Bond in pairsA TC G


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Instructions in DNA

Instructions are codedin a sequence of the DNA

bases

A segment of DNA is exposed, transcribed and

translated to carry out instructions

Sequence to indicate the

start of an instruction

Instruction that triggers

Hormone injectionInstruction for hair cells


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DNA Duplication

Source: Human Physiology: From Cells to System

4th Ed., L. Sherwood, Brooks/Cole, 2001, C-5


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Can DNA Compute?

DNA itself does not carry out any computation.

It rather acts as a massive memory.

BUT, the way complementary bases react with

each other can be used to compute things.

Proposed by Adelman in 1994


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DNA computers are the computers which using

enzymes as a program that processes on the DNAmolecules (input data)

Definition Of DNA Computing


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Dense Information Storage

This image shows 1 gram of DNA on aCD. The CD can hold 800 MB of data.

The 1 gram of DNA can hold about1x1014 MB of data.

Storage Capacity: Vol(1g ofDNA)=1cm3 , 18Mb/inch of Length(0.35nm Between Base Pairs)


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How enormous is the parallelism?

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 !

Check this out. We Typically use


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Outline

Introduction

Memory storage

Hamiltonian Path Problem

(Adelman experiment)Recent DNA technology



Application


Conclusion


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Adlemans Experiment

Hamilton Path Problem(also known as the travelling salesperson problem)

Perth

Darwin

Brisbane

Sydney

Melbourne

Alice Spring

Is there any Hamiltonian path from Darwin to Alice Spring?


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Adlemans Experiment (Contd)

Solution by inspection is:Darwin Brisbane SydneyMelbourne Perth

Alice Spring

BUT, there is no deterministic solution to thisproblem, i.e. we must check all possible

combinations.

Perth

Darwin

Brisbane

Sydney

Melbourne

Alice Spring


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1. Encode each city with complementary base -

vertex moleculesSydney - TTAAGG

Perth - AAAGGGMelbourne - GATACT

Brisbane - CGGTGC

Alice SpringCGTCCA

Darwin - CCGATG


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2. Encode all possible paths using the

complementary baseedge moleculesSydneyMelbourneAGGGAT

Melbourne SydneyACTTTAMelbourne PerthACTGGG

etc


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3. Marge vertex molecules and edge molecules.All complementary base will adhere to each other to

form a long chains of DNA molecules

Solution with

vertex DNA

molecules

Solution with

edge DNA

molecules

Marge

&

Anneal

Long chains of DNA molecules (All

possible paths exist in the graph)


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The solution is a double helix molecule:

CCGATG CGGTGC TTAAGG GATACT AAAGGG CGTCCA

TACGCC ACGAAT TCCCTA TGATTT CCCGCA

Darwin Brisbane Sydney Melbourne Perth Alice Spring

DarwinBrisbane

BrisbaneSydney

SydneyMelbourne

MelbournePerth

PerthAlice Spring


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A.Product of the ligationreaction (lane 1),

PCR amplification of the

product of the ligation

reaction ( 2 thru 5)molecular weight marker

in base pairs (lane

6).

B. Graduated PCR of the productfrom step 3( 1 thru 6)

the molecular weight marker is

in lane 7.

NOTE: These figures relate to the graph

used by Dr. Adleman.


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C. Graduated PCR of the final product of the experiment,revealing the Hamiltonian Paths ( 1 thru 6 ).

The molecular weight marker is in lane 7.


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Operations

Meltingbreaking the weak hydrogen bonds in a double helix

to form two DNA strands which are complement to

each other

Annealingreconnecting the hydrogen bonds between

complementary DNA strands


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Operations (Contd)

Mergingmixing two test tubes with many DNA molecules

AmplificationDNA replication to make many copies of the original

DNA molecules

Selection

elimination of errors (e.g. mutations) and selection ofcorrect DNA molecules


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Outline

Introduction

Memory storage





Application


Conclusion


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DNA Chip

Source: Stanford Medicine Magazine, Vol 19, 3 Nov 2002http://mednews.stanford.edu/stanmed/2002fall/translational-dna.html


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Chemical IC

Source: Tokyo Techno Forum 21, 21 June 2001http://www.techno-forum21.jp/study/st010627.htm


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Outline

Introduction

Memory storage





Application


Conclusion


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Evolution of the DNA computer (1)

Began in 1994 when Dr. Leonard Adleman

wrote the paper Molecular computation of

solutions to combinatorial problems.

He then carried out this experiment

successfully although it took him days to do

so!


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DNA computers moved from test tubes onto

gold plates.


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First practical DNA computer unveiled in 2002.

Used in gene analysis.


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Self-powered DNA computer unveiled in 2003.

First programmable autonomous computing

machine in which the input, output, software and

hardware were all made of DNA molecules.

Can perform a billion operations per second with

99.8% accuracy.


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Biological computer developed that could be

used to fight cancers.

Designer DNA identifies abnormal and is

attracted to it.

The Designer molecule then releases chemicals to

inhibit its growth or even kill the malignant cells.

Successfully tested on animals.


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Outline

Introduction

Memory storage





Application


Conclusion


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DNA COMPUTER Vs SILICON COMPUTER

Feature DNA COMPUTER SILICON COMPUTER

Miniaturization Unlimited Limited

Processing Parallel Sequential

Speed Very fast Slower

Cost Cheaper Costly

Materials used Non-toxic Toxic

Size Very small Large

Data capacity Very large Smaller


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Outline

Introduction

Memory storage





Application


Conclusion


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APPLICATIONS

DNA chips

Genetic programming

Pharmaceutical applications

Cracking of coded messages


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Outline

Introduction

Memory storage





Application


Conclusion


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ADVANTAGES

Perform millions of operations simultaneously;

Conduct large parallel processing

Massive amounts of working memory;

Generate & use own energy source via the input.

Four storage bits A T G C .

Miniaturization of data storage


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LIMITATIONS

DNA computing involves a relatively large amount of error

Requires human assistance!

Time consuming laboratory procedures. No universal method of data representation.


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Outline

Introduction

Memory storage





Application


Conclusion


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Conclusion

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..


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Outline

Introduction

Memory storage





Application


Conclusion


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References

Molecular Computation of Solutions to Combinatorial

Problems,L.M. Adleman,Science Vol.266 pp1021-1024,

11 Nov 1994

Computing With Cells and Atomsan introduction to

quantum, DNA and membrane computing, C.S. Calude andG. Paun, Taylor & Francis, 2001

The Cutting Edge Biomedical Technologies in the 21st

Century, Newton, 1999

Human Physiology: From Cells to Systems 4thEd.,L.Sherwood, Brooks/Cole, 2001


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dna based computing final2 7

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