P1253814619GVUKM

1
(No Transcript)
2
(No Transcript)
3

• Overview

• Unremarkable Problem , Remarkable Technique

• DNA A Unique Data Structure !

• DNA vs Silicon

• Operations in a DNA Computer

• Major Breakthrough Adlemans Experiment

• Steps of His Experiement

• Pros and Cons

• Conclusion What does the future hold ?

4

• The Beginning

• 1994 , Leonard M. Adleman solved An
  unremarkable problem , A remarkable technique
• The Problem Hamiltonian Path Problem

• The Significance
• Use of DNA to solve computation problems
• Computation at molecular levels !
• DNA as a data structure !
• Massively Parallel Computation

5

• DNA as a Data Structure

• DNA Structure
• Double-stranded molecule twisted into a helix
• Sugar Phosphate backbone
• Each strand connected to a complimentary strand
• Bonding between paired nucleotides Adenine and
  Thymine , Cytosine and Guanine

• Data Storage
• Data encoded as 4 bases A,T,C,G
• Data density of DNA
• One million Gbits/sq. inch !
• Hard drive 7 Gbits per square inch
• Double Stranded Nature of DNA
• Base pairs A and T , C and G
• S is ATTACGTCG then S' is TAATGCAGC
• Leads to error correction !

6

• Silicon vs DNA

• Silicon
• Von Neumann Architecture
• Sequential "fetch and execute cycle"
• the inside of a computer is as dumb as hell, but
  it goes like mad! Richard Feynman

• DNA
• Non Von Neuman , stochastic machines !
• Approach computation in a different way
• Performance of DNA computing
• Affected by memory and parallelism
• Read write rate of DNA 1000 bits/sec

7

• Operations in a DNA Computer

• CPU Operations
• Addition, Bit-Shifting, Logical Operators (AND,
  OR, NOT NOR)

• DNA Operations
• Fundamental Model Of computation Apply a
  sequence of operations to a set of strands in a
  test tube
• Extract , Length , Pour , Amplify , Cut , Join,
  Repair, and many others !
• Many copies of the enzyme can work on many DNA
  molecules simultaneously !
• Massive power of DNA computation Parallel
  Computation

8

• Adleman's Experiment

• Leonard Adleman of the University of Southern
  California shocked the science world in 1994
• He solved a math problem using DNA The
  Hamiltonian Path Problem Published the paper
  Molecular Computation of Solutions of
  Combinatorial Problems in 1994 in Science
• The field combines computer science, chemistry,
  biology and other fields.
• Prompted an "explosion of work," David F. Voss,
  editor of Science magazine

9

• Solving the Hamiltonian Path Problem

• Exhaustive Search
• Branch and Bound
• 100 MIPS computer 2 years for 20 cities !
• Feasible using DNA computation
• 1015 is just a nanomole of material
• Operations can be done in parallel

ExampleProblem
10

• Adlemans approach

• Generate all the possible paths and then select
  the correct paths Advantage of DNA approach

Steps taken by Adleman
Generate all possible routes
Select paths that start with the proper city and
end with the final city
Select paths with the correct number of cities
Select paths that contain each city only once
11

• Step 1 Generate All possible routes(1)

• Strategy
• Encode city names in short DNA sequences. Encode
  paths by connecting the city sequences for which
  edges exist.

• Process ( Ligation Reaction )
• Encode the City
• Encode the Edges
• Generate above Strands by DNA synthesizer
• Mixed and Connected together by enzyme - ligase

12

• Step 1 Generate All possible routes(2)

• Random routes generated by mixing city encoding
  with the route encoding.
• To ensure all routes , use excess of all encoding
  ( 1013 strands of each type )
• Numbers on our side (Microscopic size of DNA)

• After This Step
• We have all routes between various cities of
  various lengths

13

• Step II Select paths that start
• and end with the correct cities

Strategy Copy and amplify routes starting with LA
and ending with NY

• Process (Polymerase Chain Reaction)
• Allows copying of specific DNA
• Iterative process using enzyme Polymerase
• Working Concept of Primers
• Use primers complimentary to LA and NY

After this Step Have routes of various lengths of
LA.NY
14
(No Transcript)
15

• Step III Select paths that contain
• the correct number of cities

Strategy Sort the DNA by length and select chains
of 5 cities

• Process (Gel Electrophoresis)
• force DNA through a gel matrix by using an
  electric field
• gel matrix is made up of a polymer that forms a
  meshwork of linked strands

After This Step Series of DNA bands gt select DNA
with 30 bases
16

• Part IV Select paths that have a
• complete set of cities

Strategy Successively filter the DNA molecules by
city, one city at a time

• Process (Affinity Purification)
• Attach the complement of a city to a magnetic
  bead
• Hybridizes with the required sequence
• Affinity purify five times (once for each city)

End result Path which start in LA, visit each
city once, and end in NY
17

• Reading Out The Answer

One Method Simply sequence the DNA strands

• Alternate Method Graduated PCR
• Series of PCR amplifications done
• Primer corresponding to LA and one other city
• Measure length of sequence for each primer pair
• Deduce position of city in the path

18

• Advantages

• Speed1014 operations per second100x faster than
  current supercomputers !
• Energy Efficiency2 x 1019 operations per joule.
  Silicon computers use 109 times more energy !
• Memory1 bit per cubic nanometer1012 times more
  than a videotape !

19

• Some Problems

• Amount Scales Exponentially
• For a 200 city HP problem , amount of DNA
  required gt Mass of earth !
• Stochastically driven process -gt high error rates
• Each step contains statistical errors
• Limits the number of iterations

20

• Future of DNA Computing

• Current Trends
• Richard Lipton , Georgia Tech
• Surface DNA Techniques U of Wisconsin
• 2010 The first DNA chip will be commercially
  available
• Huge advances in biotechnology
• DNA sequencing
• Faster analysis techniques DNA chips
• DNA Molecule of the century
• Might be used in the study of logic, encryption,
  genetic programming and algorithms, automata,
  language systems.

21

• THANK
• YOU

22

• References

• Molecular Computation of Solutions to
  Combinatorial
• Computing Problems
• Leonard M. Adleman , Department of Computer
  Science,University of Southern California , 1994
• On the Computation Power of DNA
• Dan Boneh , Christoper Dunworth , Richard J.
  LiptionDepartment of Computer Science,Princeton
  University1996
• DNA Computing A Primer
• Will Ryu