Dna computing
- 1. DNA COMPUTING Ch. Subba Rayudu 3rd CSE 12711A052 NEC::NELLORE
- 2. Overview • Introduction to DNA • What is DNA computing • Adleman’s Hamiltonian path problem. • Cutting Edge Technologies • Pros and Cons • DNA Vs Electronic Computers • Conclusion
- 3. 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
- 4. 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
- 5. Uniqueness of DNA Why is DNA a Unique Computational Element??? • Extremely dense information storage. • Enormous parallelism. • Extraordinary energy efficiency.
- 6. 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.
- 7. 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
- 8. How extraordinary is the energy efficiency? • Adleman figured his computer was running 2 x 1019 operations per joule.
- 9. 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 triggers Hormone injection Instruction for hair cells ………
- 10. 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.
- 11. 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
- 12. Why do we investigate about “other” computers? •Certain types of problems (learning, pattern recognition, fault-tolerant system, large set searches, cost optimization) are intrinsically very difficult to solve with current computers and algorithms •NP problems: We do not know any algorithm that solves them in a polynomial time all of the current solutions run in a amount of time proportional to an exponential function of the size of the problem 12
- 13. Adleman’s solution of the Hamiltonian Directed Path Problem(HDPP). I believe things like DNA computing will eventually lead the way to a “molecular revolution,” which ultimately will have a very dramatic effect on the world. – L. Adleman
- 14. 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?
- 15. 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)
- 16. 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”. 16
- 17. 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.
- 18. 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.
- 19. DNA Chip
- 20. 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).
- 21. 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.
- 22. 22 Advantages Ample supply of raw materials. No toxic by-products. Smaller compared to silicon chips. Efficiency in parallel computation.
- 23. Disadvantages Time consuming. Occasionally slower. Reliability. Human Assistance.
- 24. 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
- 25. 25 Applications Satisfiability and Boolean Operations Finite State Machines Road Coloring DNA Chip Solving NP-hard problems Turing Machine Boolean Circuits
- 26. 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.
- 27. Thank you Any Queries Please