12-03-2011, 12:06 PM
Submitted By
JOM JOY KURIAN
seminar orginal.doc (Size: 681.5 KB / Downloads: 105)
ABSTRACT
Molecular biologists are beginning to unravel the information-processing tools such as
enzymes that evolution has spent billions of years refining. These tools are now been
taken in large numbers of DNA molecules and using them as biological computer
processors.
Dr. Leonard Adleman, a well-known scientist, found a way to exploit the speed and
efficiency of the biological reactions to solve the “Hamiltonian path problem”, also
known as the “traveling salesman problem”.
Based on Dr. Adleman’s experiment, we will explain DNA computing, its algorithms,
how to manage DNA based computing and the advantages and disadvantages of DNA
computing.
INTRODUCTION
DNA (Deoxyribose Nucleic Acid) computing, also known as molecular computing is a new approach to massively parallel computation based on groundbreaking work by Adleman. DNA computing was proposed as a means of solving a class of intractable
computational problems in which the computing time can grow exponentially with problem size (the 'NP-complete' or non-deterministic polynomial time complete problems).A DNA computer is basically a collection of specially selected DNA strands whose combinations will result in the solution to some problem, depending on the problem at hand. Technology is currently available both to select the initial strands and to filter the final solution. DNA computing is a new computational paradigm that employs (bio)molecular manipulation to solve computational problems, at the same time exploring natural processes as computational models. In 1994, Leonard Adleman at the Laboratory of Molecular Science, Department of Computer Science, University of Southern California surprised the scientific community by using the tools of molecular biology to solve a different computational problem. The main idea was the encoding of data in DNA strands and the use of tools from molecular biology to execute computational operations. Besides the novelty of this approach, molecular computing has the potential to outperform electronic computers. For example, DNA computations may use a billion times less energy than an electronic computer while storing data in a trillion times less space. Moreover, computing with DNA is highly parallel: In principle there could be billions upon trillions of DNA molecules undergoing chemical reactions, tha is, performing computations, simultaneously.
History & Motivation
"Computers in the future may weigh no more than 1.5 tons." So said Popular Mechanics in 1949. Most of us today, in the age of smart cards and wearable PCs would find that statement laughable. We have made huge advances in miniaturization since the days of room-sized computers, yet the underlying computational framework has remained the same. Today's supercomputers still employ the kind of sequential logic used by the mechanical dinosaurs of the 1930s. Some researchers are now looking beyond these boundaries and are investigating entirely new media and computational models. These include quantum, optical and DNA-based computers. It is the last of these developments that this paper concentrates on.
The current Silicon technology has following limitations:
Circuit integration dimensions
Clock frequency
Power consumption
Heat dissipation.
The problem's complexity that can be afforded by modern processors grows up, but great challenges require computational capabilities that neither most powerful and distributed systems could reach.
The idea that living cells and molecular complexes can be viewed as potential machinic components dates back to the late 1950s, when Richard Feynman delivered his famous paper describing "sub-microscopic" computers. More recently, several people have advocated the realization of massively parallel computation using the techniques and chemistry of molecular biology. DNA computing was grounded in reality at the end of 1994, when Leonard Adleman, announced that he had solved a small instance of a computationally intractable problem using a small vial of DNA. By representing information as sequences of bases in DNA molecules, Adleman showed how to use existing DNA-manipulation techniques to implement a simple, massively parallel random search. He solved the traveling salesman problem also known as the “Hamiltonian path" problem.
There are two reasons for using molecular biology to solve computational problems.
(i) The information density of DNA is much greater than that of silicon : 1 bit can be stored in approximately one cubic nanometer. Others storage media, such as videotapes, can store 1 bit in 1,000,000,000,000 cubic nanometer.
(ii) Operations on DNA are massively parallel: 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.
DNA Fundamentals
DNA (deoxyribonucleic acid) is a double stranded sequence of four nucleotides; the four nucleotides that compose a strand of DNA are as follows:
1) adenine (A),
2) guanine (G),
3) cytosine©,
4) thymine (T);
they are often called bases. DNA supports two key functions for life:
JOM JOY KURIAN
seminar orginal.doc (Size: 681.5 KB / Downloads: 105)
ABSTRACT
Molecular biologists are beginning to unravel the information-processing tools such as
enzymes that evolution has spent billions of years refining. These tools are now been
taken in large numbers of DNA molecules and using them as biological computer
processors.
Dr. Leonard Adleman, a well-known scientist, found a way to exploit the speed and
efficiency of the biological reactions to solve the “Hamiltonian path problem”, also
known as the “traveling salesman problem”.
Based on Dr. Adleman’s experiment, we will explain DNA computing, its algorithms,
how to manage DNA based computing and the advantages and disadvantages of DNA
computing.
INTRODUCTION
DNA (Deoxyribose Nucleic Acid) computing, also known as molecular computing is a new approach to massively parallel computation based on groundbreaking work by Adleman. DNA computing was proposed as a means of solving a class of intractable
computational problems in which the computing time can grow exponentially with problem size (the 'NP-complete' or non-deterministic polynomial time complete problems).A DNA computer is basically a collection of specially selected DNA strands whose combinations will result in the solution to some problem, depending on the problem at hand. Technology is currently available both to select the initial strands and to filter the final solution. DNA computing is a new computational paradigm that employs (bio)molecular manipulation to solve computational problems, at the same time exploring natural processes as computational models. In 1994, Leonard Adleman at the Laboratory of Molecular Science, Department of Computer Science, University of Southern California surprised the scientific community by using the tools of molecular biology to solve a different computational problem. The main idea was the encoding of data in DNA strands and the use of tools from molecular biology to execute computational operations. Besides the novelty of this approach, molecular computing has the potential to outperform electronic computers. For example, DNA computations may use a billion times less energy than an electronic computer while storing data in a trillion times less space. Moreover, computing with DNA is highly parallel: In principle there could be billions upon trillions of DNA molecules undergoing chemical reactions, tha is, performing computations, simultaneously.
History & Motivation
"Computers in the future may weigh no more than 1.5 tons." So said Popular Mechanics in 1949. Most of us today, in the age of smart cards and wearable PCs would find that statement laughable. We have made huge advances in miniaturization since the days of room-sized computers, yet the underlying computational framework has remained the same. Today's supercomputers still employ the kind of sequential logic used by the mechanical dinosaurs of the 1930s. Some researchers are now looking beyond these boundaries and are investigating entirely new media and computational models. These include quantum, optical and DNA-based computers. It is the last of these developments that this paper concentrates on.
The current Silicon technology has following limitations:
Circuit integration dimensions
Clock frequency
Power consumption
Heat dissipation.
The problem's complexity that can be afforded by modern processors grows up, but great challenges require computational capabilities that neither most powerful and distributed systems could reach.
The idea that living cells and molecular complexes can be viewed as potential machinic components dates back to the late 1950s, when Richard Feynman delivered his famous paper describing "sub-microscopic" computers. More recently, several people have advocated the realization of massively parallel computation using the techniques and chemistry of molecular biology. DNA computing was grounded in reality at the end of 1994, when Leonard Adleman, announced that he had solved a small instance of a computationally intractable problem using a small vial of DNA. By representing information as sequences of bases in DNA molecules, Adleman showed how to use existing DNA-manipulation techniques to implement a simple, massively parallel random search. He solved the traveling salesman problem also known as the “Hamiltonian path" problem.
There are two reasons for using molecular biology to solve computational problems.
(i) The information density of DNA is much greater than that of silicon : 1 bit can be stored in approximately one cubic nanometer. Others storage media, such as videotapes, can store 1 bit in 1,000,000,000,000 cubic nanometer.
(ii) Operations on DNA are massively parallel: 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.
DNA Fundamentals
DNA (deoxyribonucleic acid) is a double stranded sequence of four nucleotides; the four nucleotides that compose a strand of DNA are as follows:
1) adenine (A),
2) guanine (G),
3) cytosine©,
4) thymine (T);
they are often called bases. DNA supports two key functions for life: