This document summarizes a presentation on DNA computing. It begins with an introduction to DNA and DNA computing, explaining how biological techniques can be used for efficient computing. It then discusses logical operations in DNA computers and Adleman's solution to the Hamiltonian path problem. The document compares DNA chips to silicon chips and outlines some advantages and applications of DNA computing, as well as its limitations and future prospects. It concludes by discussing potential applications like cryptography and planning efficient routes.

1. DNA COMPUTING
Presented by:~
Abhiroop Roy Chowdhury
Aditi Roy
Affan Ahmed
Amit Singh
Ananya Roy
Aniket Biswas
Anirban Nag

2.  Little about DNA & DNA Computing
 Logical Operations in DNA Computer
 Adleman’s Hamiltonian path solution
 DNA chips vs. Silicon chips
 Advantages
 Application & limitations
 Future Prospects
 Conclusion
GNIT, IT, 3RD YEAR

3.  Double stranded helical staircase
structure
 Adenine (A) + Thymine (T)
Guanine (G) + Cytosine (C)
 Biological technique as efficient
computing vehicle
 Data are represented using strands of
DNA
GNIT, IT, 3RD YEAR
INTRODUCTION

4.  Logical operators AND, OR, NOT = DNA
cutting, copying, pasting
 Enzymes function simultaneously
 Massive parallelism
GNIT, IT, 3RD YEAR

5. DNA chips VS Silicon chips
DNA chips
• Parallel processing
• Increased memory
capacity=>better
performance
• cheap due to easy
availabilty
Silicon chips
• Sequential processing
• Increased CPU
speed=>better performance
• Relatively costlier
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6.  A directed graph with N vertices and E edges
 A fixed start(S ) and target(T ) vertex
 A path from S to T
 With no vertex repeating
 Having exactly N vertices
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HAMILTONIAN PATH PROBLEM
S
T

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8. 1. Generate Random paths
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ALGORITHM

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2. Keep paths starts from S to T
3. Keep only those that visit exactly N vertices
Contd..

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4. Keep only those that visit each vertex only once
5. If any DNA sequence remains then a path exists
6. Otherwise no path exist
Contd..

11.  Cellular organisms  Supply of DNA.
 Large supply of DNA - Cheap resource
 DNA performs error correction
 Smaller than today's computers, large
storage
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ADVANTAGES

12. GNIT, IT, 3RD YEAR
ADVANTAGES
1 gram of DNA =

13.  DNA Fingerprinting
 Pharmaceutical Applications
 Adleman’s experiment took over 7 days to end
 The experiment will fail for 200 or more cities
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APPLICATIONS & LIMITATIONS

14.  IBM seeks a fusion of DNA & Silicon
 Biologists are researching implementation of DNA
Computer to human cells
 ‘killer app’-domain of
DNA2DNA application
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FUTURE PROSPECTS

15. GNIT, IT, 3RD YEAR
o logical game development– Tic-Tac-Toe
o Cryptography
o Planning efficient airline routes
o Helps in implementing molecular
computation
CONCLUSION

16. [1] Intelligent Computing Everywhere Schuster, Alfons (Ed.), ISBN 978-1-
84628-943-9,2007
[2] Adleman, L. M. (1994). "Molecular computation of solutions to
combinatorial problems". Science 266 (5187): 1021–1024
[3] www.cdn-5.freeclipartnow.com
[4] www.idmarching.com
[5] NPTEL Videos
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References