Dna computing

DNA COMPUTING

Ch. Subba Rayudu

3rd CSE

12711A052

NEC::NELLORE

Overview

• Introduction to DNA

• What is DNA computing

• Adleman’s Hamiltonian path problem.

• Cutting Edge Technologies

• Pros and Cons

• DNA Vs Electronic Computers

• Conclusion

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 pairs

Adenine & Thymine

Guanine & 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 Storage

This 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

Why do we investigate about “other” computers?

•Certain types of problems (learning, patternrecognition, fault-tolerant system, large set searches, cost optimization) are intrinsically very difficult to solvewith current computers and algorithms

•NP problems: We do not know any algorithm thatsolves them in a polynomial time all of the currentsolutions run in a amount of time proportional to anexponential function of the size of the problem

12

Adleman’s solution of the Hamiltonian Directed Path Problem(HDPP).

I believe things like DNA computing will eventuallylead the way to a “molecular revolution,” which ultimately will have a very dramatic effect on the world. – L. Adleman

14

An example of NP-problem: the Traveling

Salesman Problem

TSP: A salesman must go from the city A to the city

Z, visiting other cities in the meantime. Some of the

cities are linked by plane. Is it any path from A to Z

only visiting each city once?

Coding the paths

1, Atlanta – Boston:

ACTTGCAGTCGGACTG

||||||||

CGTCAGCC

R:(GCAGTCGG)

2,(A+B)+Chicago:

ACTTGCAGTCGGACTGGGCTATGT

||||||||

TGACCCGA R:(ACTGGGCT) 15

Solution A+B+C+D:

ACTTGCAGTCGGACTGGGCTATGTCCGAGCAA

(Hybridization and ligation between city molecules and intercity link molecules)

Algorithm

1.Generate Random paths

2.From all paths created in step 1, keep only those that start at s and end at t.

3.From all remaining paths, keep only those that visit exactly n vertices.

4.From all remaining paths, keep only those that visit each vertex at least once.

5.if any path remains, return “yes”;otherwise, return “no”.

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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

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).

DNA Vs Electronic computers

At Present, NOT competitive with the state-of-the-art algorithms on electronic computers

Only 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.

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Advantages

Ample supply of raw materials.

No toxic by-products.

Smaller compared to silicon chips.

Efficiency in parallel computation.

Disadvantages

Time consuming.

Occasionally slower.

Reliability.

Human Assistance.

24

Error Restrictions

DNA computing involves a relatively large

amount of error.

As size of problem grows, probability of

receiving incorrect answer eventually

becomes greater than probability of receiving

correct answer

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Applications

Satisfiability and Boolean Operations

Finite State Machines

Road Coloring

DNA Chip

Solving NP-hard problems

Turing Machine

Boolean Circuits

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

Any Queries Please