This document discusses DNA computing, which uses DNA molecules to solve computational problems. It begins by introducing DNA computing and its inventor Leonard Adleman. It then explains what DNA is composed of and how its dense information storage and parallelism make it well-suited for computation. The document outlines Adleman's solution to the Hamiltonian Path Problem using DNA and provides an example of applying DNA computing to the Traveling Salesman Problem. It acknowledges current limitations of DNA computing compared to electronic computers but envisions future applications and improvements that may one day replace silicon-based computers.

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