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DNA Computing

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What is DNA computing ? Around 1950 first idea (precursor Feynman) ... However DNA computers won't flourish soon in our daily environment due to the ... – PowerPoint PPT presentation

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Title: DNA Computing


1
DNA Computing
  • By Thierry Metais
  • Email metais_at_enst.fr

2
Introduction to DNA
  • The lifes molecule

3
Introduction
  • What is DNA computing ?
  • Around 1950 first idea (precursor Feynman)
  • First important experiment 1994 Leonard Adleman
  • Molecular level (just greater than 10-9 meter)
  • Massive parallelism.
  • In a liter of water, with only 5 grams of DNA we
    get around 1021 bases !
  • Each DNA strand represents a processor !

4
A bit of biology
  • The DNA is a double stranded molecule.
  • Each strand is based on 4 bases
  • Adenine (A)
  • Thymine (T)
  • Cytosine (C)
  • Guanine (G)
  • Those bases are linked through a sugar
    (desoxyribose)
  • IMPORTANT
  • The linkage between bases has a direction.
  • There are complementarities between bases
    (Watson-Crick).
  • (A)?? (T)
  • (C)??(G)

5
DNA manipulations
  • If we want to use DNA as an information bulk, we
    must be able to manipulate it .
  • However we are talking of handling molecules
  • ENZYMES Natural CATALYSERS.
  • So instead of using physical processes, we would
    have to use natural ones, more effective
  • for lengthening polymerases
  • for cutting nucleases (exo/endo-nucleases)
  • for linking ligases
  • Serialization 1985 Kary Mullis ? PCR
  • Thank this reaction we get millions of identical
    strands, and we are allowed to think of massive
    parallel computing.

6
And what now ?
  • Situation
  • Molecular level.
  • Lots of agents. (strands)
  • Tools provided by nature. (enzymes)
  • How can we use all this? If there is a utility

7
Coding the information
  • 1994 THE Adlemans experiment.
  • Given a directed graph can we find an hamiltonian
    path (more complex than the TSP).
  • In this experiment there are 2 keywords
  • massive parallelism (all possibilities are
    generated)
  • complementarity (to encode the information)
  • This experiment proved that DNA computing wasnt
    just a theoretical study but could be applied to
    real problems like cryptanalysis (breaking DES
    ).

8
Adleman experiment
  • Each node is coded randomly with 20 bases.
  • Let Si be a code, h be the complementarity
    mapping.
  • h(ATCG) TAGC.
  • Each Si is decomposed into 2 sub strands of
    length 10 Si Si Si
  • Edge(i,j) will be encode as h(SiSj)?( preserve
    edge orientation).
  • Code
  • Input(N) //All vertices and edges are mixed,
    Nature is working
  • N?B(N,S0) //S0 was chosen as input vertice.
  • N?E(N,S4) //S4 was chosen as output vertice.
  • N?E(N,lt140) // due to the size of the coding.
  • For i1 to 5 do N?N(N, Si) //Testing if
    hamiltonian path
  • Detect(N) //conclusion

9
Example
0
1
6
5
2
3
4
S0
S2
S4
S1
S3
S5
S6
E0-2
E2-5
E5-3
E3-1
E1-4
E4-6
10
New generation of computers?
  • In the second part of 1, it is proven through
    language theory that DNA computing guarantees
    universal computations.
  • Many architectures have been invented for DNA
    computations.
  • The Adleman experiment is not the single
    application case of DNA computing

11
Stickers model
  • Memory complex Strand of DNA (single or
    semi-double).
  • Stickers are segments of DNA, that are composed
    of a certain number of DNA bases.
  • To use correctly the stickers model, each sticker
    must be able to anneal only at a specific place
    in the memory complex.

12
To visualize
0
1
0
0
1
0
0
1
0
0
Memory complex Semi-double
Soup of stickers

A
G
A
C
T
G
T
A
Zoom
13
About a stickers machine?
  • Simple operations merge, select, detect, clean.
  • ? Tubes are considered (cylinders with two
    entries)
  • However for a mere computation (DES)
  • Great number of tubes is needed (1000).
  • Huge amount of DNA needed as well.
  • Practically no such machine has been created.
  • ? Too much engineering issues.

14
Why dont we see DNA computers everywhere?
  • DNA computing has wonderful possibilities
  • Reducing the time of computations (parallelism)
  • Dynamic programming !
  • However one important issue is to find the
    killer application.
  • Great hurdles to overcome

15
Some hurdles
  • Operations done manually in the lab.
  • Natural tools are what they are
  • Formation of a library (statistic way)
  • Operations problems

16
Conclusion
  • The paradigm of DNA computing has lead to a very
    important theoretical research.
  • However DNA computers wont flourish soon in our
    daily environment due to the technologic issues.
  • Adleman renouncement toward electronic computing.
  • Is all this work lost ?
  • NO ! ? Wet computing

17
Bibliography
  • DNA Computing, New Computing Paradigms. Gheorghe
    Paun,Grzegorz Rozenberg, Arto Salomaa
  • DIMACS DNA based computers
  • Reducing Errors in DNA Computingby Appropriate
    Word Design. wdesign.pdf

18
Links
  • http//www.cs.wayne.edu/kjz/KPZ/NaturalComputing.
    html
  • http//dna2z.com/dnacpu/dna.html
  • http//www.intermonde.net/adn/liens.html
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