dna computing proposal

8/8/2019 Dna Computing Proposal

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DNAComputing

PROJECT DESCRIPTION:

Apply DNA computing of PCR Problem “Hamiltonian Path Problem”

For Solving Route planning problem in Mansoura city aiming at reducing time and

choose the best Way to reach my goal with optimal solution.

DNA Computing demonstrates favorable performance on solving the

Combinatorial optimization problems. With comparing to traditional search

Algorithms, DNA Computing is able to automatically acquire and accumulate the

Necessary knowledge about the search space during its search process.

DNA computing is a form of computing which uses DNA, biochemistry

And molecular biology, instead of the traditional silicon-based computer

Technologies. DNA computing, or, more generally, bimolecular computing, is a

Fast developing interdisciplinary area. Research and development in this area

Concerns theory, experiments and applications of DNA computing

It "computes" using enzymes that react with DNA strands, causing chain reactions.

The chain reactions act as a kind of simultaneous computing or parallel

Processing, whereby many possible solutions to a given problem can be presented



Simultaneously with the correct solution being one of the results.

The DNA double helix is stabilized by hydrogen bonds between the bases

Attached to the two strands. The four bases found in DNA are adenine

(abbreviated A), cytosine (C), guanine (G) and thymine (T). These four bases are

Attached to the sugar/phosphate to form the complete nucleotide, as shown for

Adenosine monophosphate



DNA overview :

DNA Structure :

Double-stranded molecule twisted into a helix

Sugar Phosphate backbone

Each strand connected to a complimentary strand

Bonding between paired nucleotides :

Adenine and Thymine , Cytosine and Guanine

Data Storage :

Data encoded as 4 bases : A,T,C,G

Data density of DNA

One million Gbits/sq. inch!

Hard drive : 7 Gbits per square inch

Double Stranded Nature of DNA Base pairs – A and T , C and G

S is ATTACGTCG then S' is TAATGCAGC

Leads to error correction!



BACKGROUND INFORMATION:

DNA molecular is 1.7 meters long Stretch out the entire DNA in your cells and you could reach the moon 6000

times!

DNA is the basic medium of information storage for all living cells. It has

contained and transmitted the data of life for billions of years.



Roughly 10 trillion DNA molecules could fit into a space the size of a marble.

Since all these molecules can process data simultaneously, you could

theoretically have 10 trillion calculations going on in a small space at once.

Problem view:

Finding optimal solution to reach from start to end and any

constraints on the route must be taken on consideration

Hamiltonian cycle :

A cycle in an undirected graph which visits each vertex exactly once

and also Returns to the starting vertex.

Solution:

1-Generate all possible routes

2-Select paths that start with the proper city and end with the final city

3-Select paths with the correct number of cities

4-Select paths that contain each city only once

.

http://en.wikipedia.org/wiki/Cycle_(graph_theory)

http://en.wikipedia.org/wiki/Undirected_graph

http://en.wikipedia.org/wiki/Vertex_(graph_theory)

http://en.wikipedia.org/wiki/Vertex_(graph_theory)

http://en.wikipedia.org/wiki/Undirected_graph

http://en.wikipedia.org/wiki/Cycle_(graph_theory)



Step 1: Generate all possible routes (1):

Strategy :

Encode city names in short DNA sequences. Encode paths by

connecting the city sequences for which edges exist.

Process (Ligation Reaction) :

Encode the City

Encode the Edges

Generate above Strands by DNA synthesizer

Mixed and Connected together by enzyme - ligase

Step 1: Generate All possible routes (2):

Random routes generated by mixing city encoding with the route encoding.

To ensure all routes , use excess of all encoding ( 1013

strands of each type )

Numbers on our side (Microscopic size of DNA)

Step II: Select paths that start and end with the correct cities

Strategy :

Copy and amplify routes starting with LA and ending with NY

Process (Polymerase Chain Reaction) :

Allows copying of specific DNA

Iterative process using enzyme Polymerase

Working : Concept of Primers

Use primers complimentary to LA and NY

Step III: Select paths that contain the correct number of cities



Strategy :

Sort the DNA by length and select chains of 5 cities

Process (Gel Electrophoresis) :

force DNA through a gel matrix by using an electric field

gel matrix is made up of a polymer that forms a meshwork of linked

strands

Part IV: Select paths that have a complete set of cities

Strategy :

Successively filter the DNA molecules by city, one city at a time

Process (Affinity Purification) :

Attach the complement of a city to a magnetic bead

o Hybridizes with the required sequence

Affinity purify five times (once for each city)



Goals & Objectives

Speed :

1014

operations per second100x faster than current supercomputers !

Energy Efficiency : 2 x 10

19operations per joule.

Silicon computers use 109

times more energy !

Memory : 1 bit per cubic nanometer

1012 times more than a videotape

Clientele :

Anybody can use this project on condition he own the program.

Methods :

The primary methods for achieving the goals and objectives of the Project

Will be:

Issuing DNA strand and it’s Methodology using molecules.

AVAILABLE RESOURCES :

Encode city names in short DNA sequences. Encode paths by

connecting the city sequences for which edges exist.

generate random routes as the same asa DNA strands



Generate above Strands by DNA synthesizer mixed and Connected

together

Maps and computers are available

NEEDED RESOURCES :

We need real tubes to do experiments on it.

Also we need lab because when strands Fused make

New DNA and new strands.

Real Data to work on it.

Different ways other DNA computing:

Dijkstra algorithm :

DA is an exact algorithm it always determines the optimal route but cannot guarantee that

Realistic deadlines will be met

Best First A* Algorithms :

Best first search has been a framework for heuristics which speed up algorithms by using

Semantic information about a domain. It has been explored in database context for single pair

Path computation. A* is a special case of best first search algorithm. It uses an estimator

Function f (u, d) To estimate the cost of shortest path between node u and d.



References :

http://www.csd.uwo.ca/~jamie/.Refs/Courses/CS881/charlotte.html

http://en.wikipedia.org/wiki/DNA_computing

http://publish.uwo.ca/~jadams/dnaapps1.htm

http://en.wikipedia.org/wiki/DNA

http://en.wikipedia.org/wiki/DNA_computer

http://en.wikipedia.org/wiki/DNA_code_construction

http://www.michaelang.com/a/128/dna-computing-presentation.html







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