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DNA Computing

DNA Computing. By Thierry Metais Email: metais@enst.fr. Introduction to DNA:. The life’s molecule:. 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)

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DNA Computing

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  1. DNA Computing By Thierry Metais Email: metais@enst.fr

  2. Introduction to DNA: • The life’s 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. 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) A bit of biology

  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 Adleman’s 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 wasn’t 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(Si’’Sj’)( 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,<=140) // due to the size of the coding. • For i=1 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 S5 S3 S1 S4 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 0 0 1 0 1 0 0 1 0 Memory complex: Semi-double Soup of stickers: = A G C A T G A T 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 don’t 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 won’t 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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