DNA COMPUTING

Swati BandhewalCSE 2nd yr

GS10UE0409

OverviewIntroduction to DNAWhat is DNA computingAdleman’s Hamiltonian path problem.Cutting Edge TechnologiesPros and ConsDNA Vs Electronic ComputersConclusion

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 set

of DNA• DNA is unique for each individual

Double Helix• “Sides”

Sugar-phosphate backbones• “ladders”

complementary base pairsAdenine & ThymineGuanine & Cytosine

• Two strands are held together by weak hydrogen bonds between the complementary base pairs

Uniqueness of DNA

Why is DNA a Unique Computational Element???

• Extremely dense information storage.• Enormous parallelism.• Extraordinary energy efficiency.

Dense Information StorageThis image shows 1 gram of DNA on a CD. The CD can hold 800 MB of data.

The 1 gram of DNA can hold about 1x1014 MB of data.

The number of CDs required to hold this amount of information, lined up edge to edge, would circle the Earth 375 times, and would take 163,000 centuries to listen to.

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

How extraordinary is the energy efficiency?

• Adleman figured his computer was running

2 x 1019 operations per joule.

Instructions in DNA

• Instructions are coded in 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 triggersHormone injection

Instruction for hair cells

………

A Little More………

Basic suite of operations: AND,OR,NOT & NOR in CPU while cutting, linking, pasting, amplifying and many others in DNA.

Complementarity makes DNA unique.

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

DNA COMPUTING

A computer that uses DNA (deoxyribonucleic acids) to store information and perform complex calculations.

The main benefit of using DNA computers to solve complex problems is that different possible solutions are created all at once. This is known as parallel processing.

Adleman’s 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?

Adleman’s Experiment • Solution by inspection is:

Darwin Brisbane Sydney Melbourne Perth Alice Spring

• BUT, there is no deterministic solution to this problem, i.e. we must check all possible combinations.

Perth

DarwinBrisbane

Sydney

Melbourne

Alice Spring

Adleman’s Experiment

1. Encode each city with complementary base - vertex moleculesSydney - TTAAGGPerth - AAAGGGMelbourne - GATACTBrisbane - CGGTGCAlice Spring – CGTCCADarwin - CCGATG

Adleman’s Experiment (Cont’d)

2. Encode all possible paths using the complementary base – edge moleculesSydney Melbourne – AGGGATMelbourne Sydney – ACTTTAMelbourne Perth – ACTGGGetc…

Adleman’s Experiment (Cont’d)

3. Merge vertex molecules and edge molecules.

All complementary base will adhere to each other to form a long chains of DNA moleculesSolution with vertex DNA molecules

Solution with edge DNA molecules

Merge&

Anneal

Long chains of DNA molecules (All possible paths exist in the graph)

Adleman’s Experiment (Cont’d)

• 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

Operations (Cont’d)

• Mergingmixing two test tubes with many DNA molecules

• AmplificationDNA replication to make many copies of the original DNA molecules

• Selectionelimination of errors (e.g. mutations) and selection of correct DNA molecules

THE FUTURE!Algorithm used by Adleman for the traveling

salesman problem was simple. As technology becomes more refined, more efficient algorithms may be discovered.

DNA Manipulation technology has rapidly improved in recent years, and future advances may make DNA computers more efficient.

The University of Wisconsin is experimenting with chip-based DNA computers.

DNA computers are unlikely to feature word processing, emailing and solitaire programs.

Instead, their powerful computing power will be used for areas of encryption, genetic programming, language systems, and algorithms or by airlines wanting to map more efficient routes. Hence better applicable in only some promising areas.

DNA Chip

Chemical IC

The Smallest Computer

• The smallest programmable DNA computer was developed at Weizmann Institute in Israel by Prof. Ehud Shapiro last year

• It uses enzymes as a program that processes on 0n the input data (DNA molecules).

Pros and Cons

+ Massively parallel processorDNA computers are very good to solve Non-deterministic Polynomial problems such as DNA analysis and code cracking.

+ Small in size and power consumption

Pros and Cons (Cont’d)

- Requires constant supply of proteins and enzymes which are expensive

- Errors occur frequentlya complex selection mechanism is required and errors increase the amount of DNA solutions needed to compute

- Application specific- Manual intervention by human is required

DNA Vs Electronic computers

At Present, NOT competitive with the state-of-the-art algorithms on electronic computersOnly small instances of HDPP can be

solved. Reason?..for n vertices, we require 2^n molecules.

Time consuming laboratory procedures.Good computer programs that can solve

HSP for 100 vertices in a matter of minutes.

No universal method of data representation.

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.

Thank you