dna computing if any queries contact through mail subbu.sj14@gmail.com

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