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Dna computing

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Dna computing

  1. 1. Swati Bandhewal CSE 2nd yr GS10UE0409
  2. 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. 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. 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. 5. Uniqueness of DNAWhy is DNA a Unique Computational Element???• Extremely dense information storage.• Enormous parallelism.• Extraordinary energy efficiency.
  6. 6. Dense Information StorageThis image shows 1 gram ofDNA on a CD. The CD can hold800 MB of data.The 1 gram of DNA can holdabout 1x1014 MB of data.The number of CDs requiredto hold this amount ofinformation, lined up edge toedge, would circle the Earth 375times, and would take 163,000centuries to listen to.
  7. 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. 8. How extraordinary is the energy efficiency?• Adleman figured his computer was running 2 x 1019 operations per joule.
  9. 9. Instructions in DNA Sequence to indicate the start of an instruction ……… Instruction that triggers Instruction for hair cells Hormone injection• Instructions are coded in a sequence of the DNA bases• A segment of DNA is exposed, transcribed and translated to carry out instructions
  10. 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. 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. 12. DNA COMPUTING A computer that uses DNA (deoxyribonucleic acids) to store information and perform complex calculations. The main benefit of using DNA computers to solve complex problems is that different possible solutions are created all at once. This is known as parallel processing.
  13. 13. Adleman’s Experiment• Hamilton Path Problem (also known as the travelling salesperson problem) Darwin Perth Alice Spring Brisbane Sydney Melbourne Is there any Hamiltonian path from Darwin to Alice Spring?
  14. 14. Adleman’s Experiment• Solution by inspection is: Darwin  Brisbane  Sydney  Melbourne  Perth  Alice Spring• BUT, there is no deterministic solution to this problem, i.e. we must check all possible combinations. Darwin Brisbane Perth Alice Spring Sydney Melbourne
  15. 15. Adleman’s Experiment1. Encode each city with complementary base - vertex molecules Sydney - TTAAGG Perth - AAAGGG Melbourne - GATACT Brisbane - CGGTGC Alice Spring – CGTCCA Darwin - CCGATG
  16. 16. Adleman’s Experiment (Cont’d)2. Encode all possible paths using the complementary base – edge molecules Sydney  Melbourne – AGGGAT Melbourne  Sydney – ACTTTA Melbourne  Perth – ACTGGG etc…
  17. 17. Adleman’s Experiment (Cont’d)3. Merge vertex molecules and edge molecules. All complementary base will adhere to each other to form a long chains of DNA molecules Solution with Merge Solution with vertex DNA & edge DNA molecules Anneal molecules Long chains of DNA molecules (All possible paths exist in the graph)
  18. 18. Adleman’s Experiment (Cont’d) • The solution is a double helix molecule:Darwin Brisbane Sydney Melbourne Perth Alice Spring CCGATG – CGGTGC – TTAAGG – GATACT – AAAGGG – CGTCCA TACGCC – ACGAAT – TCCCTA – TGATTT – CCCGCA Darwin Brisbane Sydney Melbourne Perth Brisbane Sydney Melbourne Perth Alice Spring
  19. 19. Operations (Cont’d)• Merging mixing two test tubes with many DNA molecules• Amplification DNA replication to make many copies of the original DNA molecules• Selection elimination of errors (e.g. mutations) and selection of correct DNA molecules
  20. 20. 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.
  21. 21. 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.
  22. 22. DNA Chip
  23. 23. Chemical IC
  24. 24. 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).
  25. 25. Pros and Cons+ Massively parallel processor DNA computers are very good to solve Non- deterministic Polynomial problems such as DNA analysis and code cracking.+ Small in size and power consumption
  26. 26. Pros and Cons (Cont’d)- Requires constant supply of proteins and enzymes which are expensive- Errors occur frequently a complex selection mechanism is required and errors increase the amount of DNA solutions needed to compute- Application specific- Manual intervention by human is required
  27. 27. 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.
  28. 28. 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.
  29. 29. Thank you

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