Dna Computing Proposal
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Transcript of 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
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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
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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!
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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.
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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
.
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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
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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)
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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
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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.
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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