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.
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!!!
