426bdna Computing Ppt

8/3/2019 426bdna Computing Ppt

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By

SRINIVASAN KRISHNAMURTHY


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Introduction:

Around 1950 first idea (precursor Feynman)

First important experiment 1994: Leonard Adleman

What is DNA computing ?

Molecular level (just greater than 10-9 meter)

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

Each DNA strand represents a processor !

Massive parallelism.


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INTRODUCTION TO DNA:


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A bit of biologyThe DNA is a double stranded molecule.

Adenine (A)

Thymine (T)

Cytosine (C)

Guanine (G)

Each strand is based on 4 bases:

Those bases are linked through a sugar (desoxyribose)

The linkage between bases has a direction.

There are complementaritiesbetween bases (Watson-Crick).

(A) (T)

(C)(G)

IMPORTANT:


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DNA manipulations

If we want

to use DNAas an

informationbulk, wemust be

able tomanipulateit .

Howeverwe are

talking ofhandling

molecules

ENZYMES =Natural

CATALYSERS.

So instead ofusing physicalprocesses, wewould have to

use naturalones, moreeffective: for

lengthening:polymerases

for cutting:nucleases(exo/endo-nucleases)

for linking:ligases

Serialization:1985: Kary Mullis PCR(polymerase

chain reaction)

Thank thisreaction we getmillions ofidenticalstrands, and weare allowed to

think of massiveparallelcomputing.


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And what now ?

Situation:

Molecular level.

Lots of agents.(strands)

Tools providedby nature.(enzymes)

How can we useall this? If thereis a utility


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Coding the information:

1994: THEAdlemans

experiment.

Given a directedgraph can we find

an hamiltonianpath (more

complex than theTSP).

In this experimentthere are 2 keywords:

massive parallelism

(all possibilities aregenerated)

complementarity (toencode theinformation)

This experimentproved that DNA

computing wasnt just

a theoretical study butcould be applied toreal problems like

cryptanalysis (breakingDES )


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Adleman experiment:

Each node iscoded

randomly with20 bases.

Let Si be a code, h be the

complementaritymapping.

h(ATCG) = TAGC.

Each Si isdecomposed into 2

sub strands of

length 10_9

Si = Si Si

Edge(i,j) will be encodeas h(SiSj)( preserve

edge orientation).

Code:

Input(N) //All vertices and edges are mixed,Nature is working

NB

(N,S0) //S0 was chosen as input vertice. NE(N,S4) //S4 was chosen as output vertice.

NE(N,


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New generation of computers?

In the second part of [1], it is proven throughlanguage theory that DNA computingguarantees universal computations.

Many architectures have been invented for

DNA computations.

The Adleman experiment is not the singleapplication case of DNA computing


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Mother Board


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DNA MOLECULE

ARRANGEMENT IN

CHIP


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Stickers model:

Memory complex =Strand of DNA(single or semi-

double).

Stickers aresegments of DNA,that are composedof a certain number

of DNA bases.

To use correctly thestickers model, eachsticker must be ableto anneal only at aspecific place in thememory complex.


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About a stickers machine?

Simple operations:

merge, select, detect,clean.

Tubes areconsidered(cylinderswith twoentries)

However for amere

computation(DES):

Great numberof tubes isneeded (1000).

Huge amount ofDNA needed as

well.


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Why dont wesee DNA

computerseverywhere?

DNAcomputinghaswonderfulpossibilitie

s: Reducing

the timeofcomputations*(parallelism)

Dynamicprogram

ming !

However

oneimportant issue isto findthekiller

applicati

on.

Greathurdles to

overcome


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Some hurdles:

Operations donemanually in the

lab.

Natural tools arewhat they are

Formation of a

library (statisticway)

Operationsproblems


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FEATURES OFDNA

COMPUTER:

Storage capacity:The information

density could go upto 1 bit

High parallelism: everymolecule could act as a

small processor on nano-scale and the number of

such processors per volumewould be potentially

enormous. In an in vitroassay we could handleeasily with about 1018processors working in

parallel.

Speed: Although theelementary operations

(electrophoresisseparation, legation, and

PCR-amplifications) wouldbe slow compared to

electronic computers, theirparallelism would stronglyprevail, so that in certain

models the number ofoperations per second

could be in an order


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Conclusion:

.

Theparadiagram

of DNA

computinghas lead to avery

importanttheoreticalresearch.

However DNAcomputers

wont flourishsoon in our

dailyenvironment

due to the

technologicissues.

Adlemanrenouncemen

t toward

electroniccomputing.

Is all thiswork lost

?

NO ! Wet

computingstillalive toimpleme

nt


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THANK YOU

426bdna Computing Ppt

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