26-03-2011, 02:38 PM
DNA Computing Application:Cryptography
PRESENTED BY:
Archana Das
DNA Computing Application-Cryptography.ppt (Size: 694.5 KB / Downloads: 155)
Introduction
The world of encryption appears to be ever shrinking. Several years ago the thought of a 56-bit encryption technology seemed forever safe, but as mankind's collective computing power and knowledge increases, the safety of the world’s encryption methods seems to disappear equally as fast.
Mathematicians and physicists attempt to improve on encryption methods while staying within the confines of the technologies available to us.
Existing encryption algorithms such as RSA have not yet been compromised but many fear the day may come when even this bastion of encryption will fall.
There is hope for new encryption algorithms however the science of our very genetic makeup is also showing promise for the information security world.
All organisms on this planet are made of the same type of genetic blueprint which bind us together. The way in which that blueprint is coded is the deciding factor as to whether you will be bald, have a bulbous nose, male, female or even whether you will be a human or an oak tree.
Within the cells of any organism is a substance called Deoxyribonucleic Acid (DNA) which is a double-stranded helix of nucleotides which carries the genetic information of a cell. This information is the code used within cells to form proteins and is the building block upon which life is formed.
Strands of DNA are long polymers of millions of linked nucleotides. These nucleotides consist of one of four nitrogen bases, a five carbon sugar and a phosphate group.
The nucleotides that make up these polymers are named after the nitrogen base that it consists of; Adenine (A), Cytosine ©, Guanine (G) and Thymine (T). These nucleotides will only combine in such a way that C always pairs with G and T always pairs with A.
Nitrogen Bases
The combination of these 4 nucleotides in the estimated million long polymer strands can result in billions of combinations within a single DNA double-helix.
These massive amount of combinations allows for the multitude of differences between every living thing on the planet from the large scale (mammal vs. plant), to the small (blue eyes vs. green eyes).
Origins Of DNA Computing
Leonard Adleman, a computer scientist at the University of Southern California was the first to suggest the theory that the makeup of DNA and it’s multitude of possible combining nucleotides could have application in brute force computational search techniques.
In early 1994, Adleman put his theory of DNA computing to the test on a problem called the Traveling Salesman Problem.
Basics Of DNA Computing :
The biological science can be applied to mathematical computation in a field known as DNA computing.
DNA computing or molecular computing are terms used to describe utilizing the inherent combinational properties of DNA for massively parallel computation.
The idea is that with an appropriate setup and enough DNA, one can potentially solve huge mathematical problems by parallel search.
Basically this means that you can attempt every solution to a given problem until you came across the right one through random calculation.
Utilizing DNA for this type of computation can be much faster than utilizing a conventional computer.
Advantages :
1. 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 !
2. Speed
Conventional computers can perform approximately 100 MIPS (millions of instruction per second). Combining DNA strands as demonstrated by Adleman, made computations equivalent to or better, arguably over 100 times faster than the fastest computer.
3. Storage Requirements :
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.
4. Minimal Power Requirements:
There is no power required for DNA computing while the computation is taking place. The chemical bonds that are the building blocks of DNA happen without any outside power source. There is no comparison to the power requirements of conventional computers.
Example :
The problem is that the salesman must find a route to travel that passes through each city
(A through G) exactly once, with a designated beginning and end.
This problem was chosen for Adleman’s DNA computing test as it is a type of problem that is difficult for conventional computers to solve.
The inherent parallel computing ability of DNA combination however is perfectly suited for the problem solving.
Adleman, using a basic 7 city, 13 street model for the Traveling Salesman Problem, created randomly sequenced DNA strands 20 bases long to chemically represent each city and a complementary 20 base strand that overlaps each city’s strand halfway to represent each street
By placing a few grams of every DNA city and street in a test tube and allowing the natural bonding tendencies of the DNA building blocks to occur, the DNA bonding created over answers in less than one second.
Of course, not all of those answers that came about in that one second were right answers as Adleman only needed to keep those paths that exhibited the following properties:
1. The path must start at city A and end at city G.
2. Of those paths, the correct paths must pass
through all 7 cities at least once.
3. The final path must contain each city in turn.
The ‘correct’ answer was determined by filtering the strands of DNA according to their end-bases to determine which strands begin from city A and end in city G and discarding those that did not.
The remaining strands were then measured through electrophoreic techniques to determine if the path they represent has passed through all 7 cities.
Adleman found his one true path for the ‘Salesman’ in his problem and the possible future of DNA computing opened up in front of him.
The ability to solve problems with larger numbers of cities and paths using the same techniques was immediately feasible.
PRESENTED BY:
Archana Das
DNA Computing Application-Cryptography.ppt (Size: 694.5 KB / Downloads: 155)
Introduction
The world of encryption appears to be ever shrinking. Several years ago the thought of a 56-bit encryption technology seemed forever safe, but as mankind's collective computing power and knowledge increases, the safety of the world’s encryption methods seems to disappear equally as fast.
Mathematicians and physicists attempt to improve on encryption methods while staying within the confines of the technologies available to us.
Existing encryption algorithms such as RSA have not yet been compromised but many fear the day may come when even this bastion of encryption will fall.
There is hope for new encryption algorithms however the science of our very genetic makeup is also showing promise for the information security world.
All organisms on this planet are made of the same type of genetic blueprint which bind us together. The way in which that blueprint is coded is the deciding factor as to whether you will be bald, have a bulbous nose, male, female or even whether you will be a human or an oak tree.
Within the cells of any organism is a substance called Deoxyribonucleic Acid (DNA) which is a double-stranded helix of nucleotides which carries the genetic information of a cell. This information is the code used within cells to form proteins and is the building block upon which life is formed.
Strands of DNA are long polymers of millions of linked nucleotides. These nucleotides consist of one of four nitrogen bases, a five carbon sugar and a phosphate group.
The nucleotides that make up these polymers are named after the nitrogen base that it consists of; Adenine (A), Cytosine ©, Guanine (G) and Thymine (T). These nucleotides will only combine in such a way that C always pairs with G and T always pairs with A.
Nitrogen Bases
The combination of these 4 nucleotides in the estimated million long polymer strands can result in billions of combinations within a single DNA double-helix.
These massive amount of combinations allows for the multitude of differences between every living thing on the planet from the large scale (mammal vs. plant), to the small (blue eyes vs. green eyes).
Origins Of DNA Computing
Leonard Adleman, a computer scientist at the University of Southern California was the first to suggest the theory that the makeup of DNA and it’s multitude of possible combining nucleotides could have application in brute force computational search techniques.
In early 1994, Adleman put his theory of DNA computing to the test on a problem called the Traveling Salesman Problem.
Basics Of DNA Computing :
The biological science can be applied to mathematical computation in a field known as DNA computing.
DNA computing or molecular computing are terms used to describe utilizing the inherent combinational properties of DNA for massively parallel computation.
The idea is that with an appropriate setup and enough DNA, one can potentially solve huge mathematical problems by parallel search.
Basically this means that you can attempt every solution to a given problem until you came across the right one through random calculation.
Utilizing DNA for this type of computation can be much faster than utilizing a conventional computer.
Advantages :
1. 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 !
2. Speed
Conventional computers can perform approximately 100 MIPS (millions of instruction per second). Combining DNA strands as demonstrated by Adleman, made computations equivalent to or better, arguably over 100 times faster than the fastest computer.
3. Storage Requirements :
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.
4. Minimal Power Requirements:
There is no power required for DNA computing while the computation is taking place. The chemical bonds that are the building blocks of DNA happen without any outside power source. There is no comparison to the power requirements of conventional computers.
Example :
The problem is that the salesman must find a route to travel that passes through each city
(A through G) exactly once, with a designated beginning and end.
This problem was chosen for Adleman’s DNA computing test as it is a type of problem that is difficult for conventional computers to solve.
The inherent parallel computing ability of DNA combination however is perfectly suited for the problem solving.
Adleman, using a basic 7 city, 13 street model for the Traveling Salesman Problem, created randomly sequenced DNA strands 20 bases long to chemically represent each city and a complementary 20 base strand that overlaps each city’s strand halfway to represent each street
By placing a few grams of every DNA city and street in a test tube and allowing the natural bonding tendencies of the DNA building blocks to occur, the DNA bonding created over answers in less than one second.
Of course, not all of those answers that came about in that one second were right answers as Adleman only needed to keep those paths that exhibited the following properties:
1. The path must start at city A and end at city G.
2. Of those paths, the correct paths must pass
through all 7 cities at least once.
3. The final path must contain each city in turn.
The ‘correct’ answer was determined by filtering the strands of DNA according to their end-bases to determine which strands begin from city A and end in city G and discarding those that did not.
The remaining strands were then measured through electrophoreic techniques to determine if the path they represent has passed through all 7 cities.
Adleman found his one true path for the ‘Salesman’ in his problem and the possible future of DNA computing opened up in front of him.
The ability to solve problems with larger numbers of cities and paths using the same techniques was immediately feasible.