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Dna computing

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  • 1. DNA COMPUTING Shashwat Shriparv dwivedishashwat@gmail.com InfinitySoft
  • 2. 2 Introduction  Ever wondered where we would find the new material needed to build the next generation of microprocessors???? HUMAN BODY (including yours!)…….DNA computing.  “Computation using DNA” but not “computation on DNA”  Dr. Leonard Adleman is often called “The inventor of DNA Computers”.
  • 3. What is a DNA? 3 A nucleic acid that carries the genetic information in the cells. DNA is composed of A (Adenine), C (Cytosine), G (Guanine) and T (Thymine)
  • 4. 4 DNA MEMORY A DNA string can be viewed as a memory resource to save info:  4 types of units (A,C,G,T)  Complementary units: A-T,C-G
  • 5. 5 Uniqueness of DNA Why is DNA a Unique Computational Element???  Extremely dense information storage.  Enormous parallelism.
  • 6. 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.
  • 7. DNA Computing It can be defined as the use of biological molecules, primarily DNA , to solve computational problems that are adapted to this new biological format 7
  • 8. Computers Vs DNA computing DNA based Computers Microchip based Computers  Slow at Single Operations  Fast at Single Operations (Fast CPUs)  Able to simultaneously perform Millions of operations  Can do substantially fewer operations simultaneously  Huge storage capacity  Smaller capacity  Require considerable preparations before  Immediate setup 8
  • 9. 9 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
  • 10. 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
  • 11. 11 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?
  • 12. 12 An example of NP-problem: the Traveling Salesman Problem 1. Code each city (node) as an 8 unit DNA string 2. Code each permitted link with 8 unit DNA strings 3. Generate random paths between N cities (exponential) 4. Identify the paths starting at A and ending at Z 5. Keep only the correct paths (size, hamiltonian)
  • 13. 13 Coding the paths 1, Atlanta – Boston: ACTTGCAGTCGGACTG |||||||| CGTCAGCC R:(GCAGTCGG) 2,(A+B)+Chicago: ACTTGCAGTCGGACTGGGCTATGT |||||||| TGACCCGA R:(ACTGGGCT) Solution A+B+C+D: ACTTGCAGTCGGACTGGGCTATGTCCGAGCAA (Hybridization and ligation between city molecules and intercity link molecules)
  • 14. 14 Filter the correct solutions 1.Identify the paths starting at A and ending at Z  PCR for identifying sequences starting with the last nucleotides of A and ending at the first nucleotides of Z 2. Keep only the paths with N cities (N=number of cities)  Gel electrophoresis 3. Keep only those paths with all of the cities (once)  Antibody bead separation with each vertex (city) The sequences passing all of the steps are the solutions
  • 15. 15 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. 16 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.  No universal method of data representation.
  • 17. 17 Advantages  Ample supply of raw materials.  No toxic by-products.  Smaller compared to silicon chips.  Efficiency in parallel computation.
  • 18. Disadvantages  Time consuming.  Occasionally slower.  Reliability.  Human Assistance.
  • 19. 19 Danger of Errors possible  Assuming that the operations used by Adleman model are perfect is not true.  Biological Operations performed during the algorithm are susceptible to error  Errors take place during the manipulation of DNA strands. Most dangerous operations:  The operation of Extraction  Undesired annealings.
  • 20. 20 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
  • 21. 21 Applications  Satisfiability and Boolean Operations  Finite State Machines  Road Coloring  DNA Chip  Solving NP-hard problems  Turing Machine  Boolean Circuits
  • 22. 22 Conclusion  DNA Computing uses DNA molecules to computing methods  DNA Computing is a Massive Parallel Computing because of DNA molecules  Someday, DNA Computer will replace the silicon-based electrical computer
  • 23. 23 Future! It will take years to develop a practical, workable DNA computer. But…Let’s all hope that this DREAM comes true!!!
  • 24. THANK YOU 24 Shashwat Shriparv dwivedishashwat@gmail.com InfinitySoft

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