Dna computing


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Dna computing

  1. 1. DNA COMPUTING Shashwat Shriparv dwivedishashwat@gmail.com InfinitySoft
  2. 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. 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. 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. 5 Uniqueness of DNA Why is DNA a Unique Computational Element???  Extremely dense information storage.  Enormous parallelism.
  6. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 17 Advantages  Ample supply of raw materials.  No toxic by-products.  Smaller compared to silicon chips.  Efficiency in parallel computation.
  18. 18. Disadvantages  Time consuming.  Occasionally slower.  Reliability.  Human Assistance.
  19. 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. 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. 21 Applications  Satisfiability and Boolean Operations  Finite State Machines  Road Coloring  DNA Chip  Solving NP-hard problems  Turing Machine  Boolean Circuits
  22. 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. 23 Future! It will take years to develop a practical, workable DNA computer. But…Let’s all hope that this DREAM comes true!!!
  24. 24. THANK YOU 24 Shashwat Shriparv dwivedishashwat@gmail.com InfinitySoft