Computing With DNANov28th2009

8/14/2019 Computing With DNANov28th2009

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&Ashwin Balu Sunny Gupta:CISC879 Natural Computing

Queen s University


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OutlineOutline

Biology OverviewDNA Basics

Gene Expression and System

BiologyDNA Reactions

DNA Computations

DNA Computing ApplicationsCurrent Trends

Open Problems and Limitations


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BackgroundBackground

The use of biochemicals and biomolecularoperations to solve problems and to performcomputation

:Questions Can any algorithm be simulated by means of DNA

computing?

Is it possible to design a programmable molecularcomputer?


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MotivationMotivation

Unique features !DNA used as data and code structures Many ways of creating DNA computers

Usefulness

Massive parallelism Smaller hardware size

High energy efficiency Smaller information storage Can solve problems standard computers can t


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Need of DNA computer?Need of DNA computer?

Moores Law states that siliconmicroprocessors double incomplexity roughly every two

years.One day this will no longer hold

true when miniaturisation limits

are reached. Intel scientists sayit will happen in about the year2018.

Require a successor to silicon.


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The BeginningThe Beginning

Francis Crick & James D. Watson.co-discoverers of the structure of

the DNAmolecule in 1953

l l i l f h
http://en.wikipedia.org/wiki/DNAhttp://en.wikipedia.org/wiki/Moleculehttp://en.wikipedia.org/wiki/Moleculehttp://en.wikipedia.org/wiki/DNA


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Molecular Biology of theMolecular Biology of theCellCell

Cellular structures and processes result from a complexinteraction network of biological molecules


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CarbohydratesCarbohydrates


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LipidsLipids


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ProteinsProteins

Mammals only use 20 different aminoacids to make the immense variety of

.proteins it needs


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Molecular Forces &Molecular Forces &BondingBonding


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What is DNA?What is DNA?

Source code to lifeInstructions for building and

regulating cells

Data store for genetic inheritanceThink of enzymes as hardware,

DNA as software


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DNA MoleculeDNA Molecule

DNA is a medium of information storage using 4 base pairs.to store information for all living cells

It has contained and transmitted the data of life for

billions of years

DNA is organized into long structures called chromosomes
http://en.wikipedia.org/wiki/Chromosomehttp://en.wikipedia.org/wiki/Chromosome


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DNADNA

NA makes the building blocks for life


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DNA -DNA - Data and CodeData and Code

,NA doesn't just make proteins it hasnstructions on how the system should behave

Humanand chimpanzee DNA .is 98 5 percent identical

DNA of humans and mice is only around 60 percentsimilar


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Dense Information StorageDense Information Storage

This image shows 1.gram of DNA on a CD The

CD can hold 800 MB of.dataThe 1 gram of DNA can

hold about 1x1014

MB of.data


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Types of DNATypes of DNA

MitochondrialNuclear

Nuclear and mitochondrial DNA are thoughtto be of separate evolutionary origin

mtDNA being derived from the

circular genomes of the bacteria
http://en.wikipedia.org/wiki/Evolutionhttp://en.wikipedia.org/wiki/Circular_DNAhttp://en.wikipedia.org/wiki/Bacteriahttp://en.wikipedia.org/wiki/Bacteriahttp://en.wikipedia.org/wiki/Circular_DNAhttp://en.wikipedia.org/wiki/Evolution


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Mitochondrial DNAMitochondrial DNANuclear chromosomes encode around,30 000 genes in 3 billion bases

Mitochondrial DNA genome is tiny with

, . ,only around 16 500 bases However this16k of data is enough to encode

,several proteins and RNA molecules.containing exactly 35 genes


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From Large to SmallFrom Large to Small

2 nm

10 m .0 84 m

11 nm


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DNA MoleculesDNA Molecules

Important features 3 basic parts for each Numbering carbons : , :5 unattached phosphate group 3 unattached

hydroxyl group

A TC


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DNA BasesDNA Bases

Important features Complementarity

.Purines vs pyrimidines Hydrogen bonds Phosphodiester bonds

Antiparallelism Natural direction


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DNA MoleculesDNA Molecules

Simplest representation 5 CGTGTTCGAAGCCC 3 3 GCACAAGCTTCGGG 5

Important features Representation

Complementarity

Directionality

Sticky ends 5 CGTGTTCGA 3 3 GCACA 5


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Manipulating DNAManipulating DNA

) ( )1 Denaturation melting

) ( )2 Annealing renaturation


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)3 Polymerase extension

( )5 TCGATT 3 primer ( )3 AGCTAACTT 5 template

5 TCGATTG 3 3 AGCTAACTT 5

5 TCGATTGA 3 3 AGCTAACTT 5

5 TCGATTGAA 3 3 AGCTAACTT 5


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)4 Nuclease degradation

5 TCGATTGAA 3 3 AGCTAACTT 5

5 TCGATTGA 3 3 GCTAACTT 5

5 TCGATTG 3 3 CTAACTT 5

5 TCGATT 3 3 TAACTT 5

5 TGAATTCCG 3 3 ACTTAAGGC 5

5 TG 3 5 AATTCCG 3 3 ACTTAA 5

3 GGC 5

5 TGCCCGGGA 3 3 ACGGGCCCT 5

5 TGCCC 3 5 GGGA 3

3 ACGGG 5 3 CCCT 5


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)5 Ligation OH

P 5 TC

GATTGAA 3 3 AGCTAA

CTT 5 P

OH

OH P 5 TCGATTGAA 3 3 AGCTAACTT 5 OH

P


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)6 Amplification

.1 Denaturatation

.2 Add primers

.3 Annealling


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) ( )6 Amplification cont d

.4 Polymerase extension


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)7 Gel electrophoresis


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) , ,8 Modify nucleotides insert delete substitute ) 9 Filtering magnetic bead separation

)10 Synthesis of a single strand

)11 Sequencing


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DNA manipulations:DNA manipulations:

If we want to use DNA as aninformation bulk, we must be able tomanipulate it .

However we are talking of handlingmolecules

ENZYMES = Natural CATALYSERS.So instead of using physical processes,

we would have to use natural ones,more effective: for lengthening: polymerases for cutting: nucleases (exo/endo-

nucleases) for linking: ligases

Serialization: 1985: Kary Mullis PCR Thank this reaction we get millions of identical


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DNA MachineDNA Machine


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Introduction to DNAIntroduction to DNA

ComputingComputing

What is DNA computing ? Around 1950 first idea (precursor

Feynman)

Molecular level (just greater than10-9 meter)

Massive parallelism.

In a liter of water, with only 5grams of DNA we get around 1021 bases !

Each DNA strand represents a

processor !


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...DNA Computing Begins...DNA Computing Begins

1970s Much speculation

1994

Leonard Max Adleman Molecular Computation Solutions to

Combinatorial Problems -Used DNA computing to solve an NP complete

:problem Hamiltonian Path Problem

- Biology and computer science life and computation.are related


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Hamiltonian Path ProblemHamiltonian Path Problem

:Solution .1 Generate random paths through the graph

.2 Keep only those paths beginning with vinandending with vout

.3 If graph has n ,vertices keep only those paths

with exactly nvertices .4 Keep only those paths that enter all vertices

of the graph at least once . , ; ,5 If any path remains say YES else say NO

0

1

5

2 3

4

6


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Hamiltonian Path ProblemHamiltonian Path Problem

Instance of the HPP solved by Adleman

0

1

5

2 3

4

6


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Adleman s HPP SolutionAdleman s HPP Solution

- -Adleman translated this solution step by stepinto molecular biology Encoded each vertex as a single stranded

nucleotide of length 20 randomized codes Each possible edge synthesized

Connect edges by enzymatic ligation

TGAATCCGACGTCCAGTGA ATGAACTATGGCACGCTATC

GCAGGTCACTTACTTGATAC

v1 v2

e1 2


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The basic idea is to have a setof molecules with unique

sequences representing thevertices and edges of the graph


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Adlemans HPP SolutionAdlemans HPP Solution







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Let Eibe the oligonucleotide of edge i Let Eibe the complement of Ei

Using E0and E6 ,as primers PCR product of Step

1 Only paths containing vertex 0 and vertex 6

remain

Use filtering operation to separate outstrands starting at vertex 0 and ending with

vertex 6


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Separate product of Step 2 by gelelectrophoresis Identify DNA molecules with 7 vertices

* =7 vertices 20 bases each 140 bp Repeat cycles of PCR and gel electrophoresis

to purify the product further

: - ,Result 7 vertex molecules that start with 0end with 6

:Examples

, , , , , ,0 1 2 3 4 5 6 , , , , , ,0 3 2 3 4 5 6 , , , , , ,0 1 1 1 1 1 6


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Probe single stranded DNA with complementaryoligonucleotides attached to magnetic beads Can pull sequences with specific vertices out

of the solution Use one step for each vertex


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PCR the remnants after Step 4 Analyze it by gel electrophoresis , If anything exists obtain YES Hamiltonian

path found , Else obtain NO no Hamiltonian path

available


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!!! WE JUST COMPUTED WITH DNA

Th ht Ab t Adl Th ht Ab t Adl


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Thoughts About AdlemansThoughts About Adlemans

SolutionSolution

Practical details of experiment are notrelevant Experiment took 7 days of lab work ,However distinctive advantage

# of oligonucleotides needed will increase

linearly in relation to the number of verticesinvolved - ; ( )NP complete in classical computing O n in DNA

computing



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SolutionSolution

Adleman s solution has weaknesses # of single strands necessary to encode vertices

and edges of generic HPP is of the order !n Drastic limitations on the size of problems that

can be solved by this procedure General strategy to work around this is to

diminish set of candidate solutions to begenerated

- , Since HPP is NP complete Adleman s DNAtechnique can solve any NP problem

But not necessarily in a feasible way



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SolutionSolution

Brute force was used Speed of any computer determined by

Parallelism

Number of steps per unit time

DNA ClassicalOperations(per second)

106 - 1012 1014 - 1020

Energy used(operations per joule) 2*10

19

Theoretical:34*1019

10

9

Storage size of one bit(per cubic nanometer)

1012 1


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SAT ProblemSAT Problem

Satisfiability problem for prepositionalformulae Logical variables = {E e1, e2, , en}

Clauses Cj = {e1j, e2j, , enj} ,joined by AND,OR NOT

:Problem Given C1^ C2 ^ ^ Cmassign a Boolean value to

each variable such that the entire statement isTRUE

- !NP complete


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Liptons SAT SolutionLiptons SAT Solution

Possibly represent this as a graph searchproblem :Two phases

)1 Generate all paths in the graph ) ( )2 Search filter for truth assignment set that

satisfies formula ,Basically same principles as Adleman

Assume formula with nvariables FALSE e1

0 e20 e3

0 e -n 10 en

0

v0 v1 v2 v -n 1 vn

TRUE e10 e2

0 e30 e -n 1

0 en0


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LiptonsLiptons


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Gene ExpressionGene Expression

, ,Used by all known life eukaryotes prokaryotes and viruses

Modulates the macromolecular machinery for life

, , , -Transcription RNA splicing Translation post translational modifications of

Gene regulation gives the cell control over structure and functionMetaprogramming


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Gene ExpressionGene ExpressionTranscription

Translation

,Turing machines invented by,Alan Turing in 1936 are

extremely simple computers-that consist of a finitestate compute head that can

- -move back and forth on an-infinite one dimensional.memory tape


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Systems BiologySystems Biology


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Gene Regulation NetworkGene Regulation Network

Cytoscape DemoCytoscape Demo


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Cytoscape DemoCytoscape Demo


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Internet connection mapInternet connection map

-Asia Pacific Red/Europe Middle

/East Central/ -Asia Africa Green

-North AmericaBlueLatin American and

Caribbean -Yellow

RFC1918 IP-Addresses Cyan-Unknown White


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DNA ComputerDNA Computer

Given enough strands of DNA andcertain biological operations

DNA can model 1-tape

nondeterministic Turing machine DNA compare to formulas DNA can work like a state

machine


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DNA Logic GatesDNA Logic Gates

DNA can work like a statemachine

Catalytic DNA or DNAzyme

DNAzymes are used to build logicgates DNAzymes are limited to 1-, 2-,

and 3-input gates


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DNA MultiplicationDNA Multiplication


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Restriction Enzyme Digests


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DNA Code BreakingDNA Code Breaking


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DNA Code BreakingDNA Code Breaking


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DNA Associative MemoryDNA Associative Memory

Vessel containing DNAEncode a word-appropriate single

strand DNA molecule encoding it


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Stickers model:Stickers model:

Memory complex = Strand of DNA(single or semi-double).

Stickers are segments of DNA,

that are composed of a certainnumber of DNA bases.

To use correctly the stickersmodel, each sticker must beable to anneal only at a specificplace in the memory complex.


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To visualize:To visualize:

0:Memory complex

-Semi double

1 00 1 0 0 1 00

Zoom

=

A G AC T G TA

:Soup of stickers

i i


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DNA Associative MemoryDNA Associative Memory


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About a stickers machine?About a stickers machine?

Simple operations: merge, select,detect, clean.

Tubes are considered (cylinders

with two entries)However for a mere computation(DES):

Great number of tubes is needed(1000).

Huge amount of DNA needed aswell.

Practically no such machine has


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Technological DevelopmentsTechnological Developments

US team shows that DNAcomputing can be simplified

by attaching the molecules.to a surface

DNA molecules were applied

to a small glass plate.overlaid with gold

,Exposure to certain enzymes

destroyed the molecules withwrong answers leaving only

the DNA with the right.answers

i i iDNA Li it ti


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DNA LimitationsDNA Limitations

DNA denaturing (temperature,time, PH)

Length of the DNA strand=size of

the problemWhile the number of strandscould be exponential ..1021 is theupper bound (volume issues)

DNA algorithms need to be morenoise tolerant

Making DNA Computers ErrorMaking DNA Computers Error


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Making DNA Computers ErrorMaking DNA Computers ErrorResistantResistant

DNA computing not error free

DNA calculations fall into 3 basic

classes1.Decreasing Volume (# strands

are reduced with each step)

2.Constant Volume (# strandsconstant throughout all steps)

3.Mixed Algorithms



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Aldemanand Lipton are even

more special. Each strand is

good or badGood strands encode a solution

Bad strands do not

If a good strand is damaged orlost the algorithm fails

If a bad strand is not removedand many are left at end then


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Two sources of errors1.Every operation can cause an error

(extraction)

extraction is not perfect usually 95%strands match the desired pattern

In addition, strands that do notmatch will sometimes be removedanyways.

Rates typically 1 part in 106

2.DNA has life, and decays at a finite

rate. If an algorithm takes months

S h dlS h dl


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Some hurdles:Some hurdles:

Operations done manually in thelab.

Natural tools are what they are

Formation of a library (statisticway)Operations problems

M l l C tiM l l C ti


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Molecular ComputingMolecular Computing Wayne State Universitys Michael Conrad has defined

his vision of a molecular computer in which proteinsintegrate multiple input modes to perform afunctional output (Conrad, 1986). In addition tosmaller size scale, protein based molecularcomputing offers different architectures and

computing dimensions. Conrad suggests that non-von Neumann, nonserial and non-silicon computerswill be context dependent, with input processed asdynamical physical structures, patterns, or analogsymbols. Multidimensional conditions determine the

conformational state of any one protein:temperature, pH, ionic concentrations, voltage,dipole moment, electroacoustical vibration,phosphorylation or hydrolysis state, conformationalstate of bound neighbor proteins, etc. Proteinsintegrate all this information to determine output.

Thus each protein is a rudimentary computer and


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M l l C tiMolecular Computing


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Molecular ComputingMolecular ComputingCells and organisms are natural molecular computers

Allowing proteins to fold producing computation

M l l C tiMolecular Computing


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Molecular ComputingMolecular Computing

Allowing proteins to fold producingcomputation

P t i F ldiProtein Folding


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Protein FoldingProtein Folding

Mainly guided by:Hydrophobic interactions

Intramolecular hydrogen bonds

Van der Waals forces



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Folding is a free energy minimization processthat depends on the interactions amongamino acids

Protein change as fast as femtoseconds (10-15 sec)

Folding ProteinsFolding Proteins


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All proteins begin to fold into threedimensional structures after synthesis These structures gives proteins its

functionally (lock and key receptors)

Folding is a free energy minimization process

Protein Folding ProblemProtein Folding Problem


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Protein Folding ProblemProtein Folding Problem

Considered to be an NP-complete problem

Massively parallel computers to derivesolutions by brute force have failed

Molecular pathway too complex

Genetic Algorithms do better but cannotguarantee polynomial time, fitnessrelies on structure, and since thestructure is not known you have thetermination problem in GA


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Protein based computingProtein based computing


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Protein based computingProtein based computing

Different architectures and computingdimensions

Non-von Neumann, non-serial and non-silicon

Context dependent Input processed as dynamical physicalstructures, patterns, or analog symbols

Multidimensional conditions

Temperature, pH, ionic concentrations, voltage,dipole moment, electroacoustical vibration,phosphorylation or hydrolysis state,conformational state of bound neighborproteins, etc.

Proteins integrate all this

Protein Folding MatrixProtein Folding Matrix


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gg

ComputerComputer

Use Rose scale matrixLet the protein folding solve large matrixproblems



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ggApplicationsApplications Possible to generate a vast combinatorial of

different protein shapes just by changingthe DNA base sequence

Encrypting data (lock and key) Decrypting data Encryption breaking Pattern Recognition

D N A C O M P U TE R V s S ILIC O N C O M P U TE RD N A C O M P U TE R V s S ILIC O N C O M P U TE R


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D N A C O M P U TE R V s S ILIC O N C O M P U TE RD N A C O M P U TE R V s S ILIC O N C O M P U TE R

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

AdvantagesAdvantages


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AdvantagesAdvantages

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

LimitationsLimitations


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LimitationsLimitations

DNA computing involves a relativelylarge amount of error

Requires human assistance!

Time consuming laboratoryprocedures.

No universal method of data

representation.

Slides to goSlides to go


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Slides to goSlides to go

I'm putting together a few slideson associative memory,cryptographic problems, DNAbased addition and matrixmultiplication, parallel machinesand DNA computer limitations.

Computing With DNANov28th2009

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