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part 2

part 2. Course outline. Where to find me. Announcement. NO Lecture. The highlights. DNA computing: the highlights. Leonard Adleman Molecular computation of solutions to combinatorial problems Science, 266, 1021-1024, 1994. Richard Lipton

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part 2

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  1. part 2

  2. Course outline

  3. Where to find me

  4. Announcement NO Lecture

  5. The highlights

  6. DNA computing: the highlights Leonard Adleman Molecular computation of solutions to combinatorial problems Science, 266, 1021-1024, 1994 Richard Lipton DNA solution to hard combinatorial problems problem Science, 268, 542-545, 1995 Q. Ouyang et al. DNA solution to the maximal clique problem Science, 278, 446-449, 1997 Q. Liu et al. DNA computing on a chip Nature, vol. 403, pp. 175-179, 2000

  7. Lenard Adleman: hamiltonian path Hamilton path problem • Millions of DNA strands, diffusing in a liquid, can self-assemble into all possible path configurations. • A judicious series of molecular maneuvers can fish out the correct solutions. • Adleman, combining elegance with brute force, could isolate the one true solution out of many probability.

  8. Eric Winfree: DNA self-assembly • universal computation can be performed by the sequence-directed self-assembly of DNA into a 2D sheet • experimental investigations have demonstrated that 2D sheets of DNA will self-assemble • Wang tiles, branched DNA with sticky ends, reduces this theoretical construct to a practical one • this type of assembly can be shown to emulate the operation of a Universal Turing Machine.

  9. Eric Winfree: DNA self-assembly

  10. Eric Winfree: DNA self-assembly

  11. Eric Winfree: DNA self-assembly

  12. Ned Seeman: DNA self-assembly danny van noort, october 2001

  13. Ned Seeman: DNA self-assembly danny van noort, october 2001

  14. Gheorghe Păun: P-systems • A P system is a computing model which abstracts from the way the alive cells process chemical compounds in their compartmental structure. In short, in the regions defined by a membrane structure we have objects which evolve according to given rules. • The objects can be described by symbols or by strings of symbols (in the former case their multiplicity matters, that is, we work with multisets of objects placed in the regions of the membrane structure; in the second case we can work with languages of strings or, again, with multisets of strings). • By using the rules in a nondeterministic, maximally parallel manner, one gets transitions between the system configurations. A sequence of transitions is a computation. With a halting computation we can associate a result, in the form of the objects present in a given membrane in the halting configuration, or expelled from the system during the computation. • Various ways of controlling the transfer of objects from a region to another one and of applying the rules, as well as possibilities to dissolve, divide or create membranes were considered.

  15. Gheorghe Păun: P-systems a b c

  16. Gheorghe Păun: P-systems a b aabc bc

  17. Tom Head: splicing systems • There is a solid theoretical foundation for splicing as an operation on formal languages. • In biochemical terms, procedures based on splicing may have some advantages, since the DNA is used mostly in its double stranded form, and thus many problems of unintentional annealing may be avoided. • The basic model is a single tube, containing an initial population of dsDNA, several restriction enzymes, and a ligase. Mathematically this is represented as a set of strings (the initial language), a set of cutting operations, and a set of pasting operations. • It has been proved to a Universal Turing Machine.

  18. Tom Head: splicing systems • These are the techniques that are common in the microbiologist's lab and can be used to program a molecular computer. DNA can be: • synthezise desired strands can be created • separate strands can be sorted and separated by length • merge by pouring two test tubes of DNA into one to perform union • extract extract those strands containing a given pattern • melt/anneal breaking/bonding two ssDNA molecules with complementary sequences • amplify use of PCR to make copies of DNA strands • cut cut DNA with restriction enzymes • rejoin rejoin DNA strands with 'sticky ends' • detect confirm presence or absence of DNA

  19. Q. Liu: experiments on a surface

  20. Q. Liu: experiments on a surface (wxy)  (wyz)  (xy)  (wy)=1 {0000} {0001} {0010} {0011} {0100} {0101} {0110} {0111} {1000} {1001} {1010} {1011} {1100} {1101} {1110} {1111}

  21. Q. Liu: experiments on a surface

  22. Computing in biology

  23. Computing in biology • Cells and nature compute by reading and rewriting DNA by processes that modify sequence at the DNA or RNA level. DNA computing is interested in applying computer science methods and models to understand biological phenomena and gain insight into early molecular evolution and the origin of biological information processing.

  24. The biology of computing

  25. Pyrimidine pathway

  26. Electronic pathway

  27. Tokyo subway system

  28. Transcriptional regulators  L  T P P T P P - 2  cl tet lac ct gfp lacl tetR gfp • lac- strain CMW101 • three promoter genes: lacl,  cl, tetR • the binding state of lacl and tetR can be changed with IPTG (isopropyl -D-thiogalactopyranoside) and aTc (anhydro-tetracycline). • only signal when aTc but no IPTG From Guet et al., Science 24 May 2002

  29. Instructional design • RNA can be used to programme a cell to produce a specific output, in form of proteins or nanostructures. • (self)-replication is contained in propagation and can be compared with the goal to produce to build self replicating machines in silico. • cell are the factories, RNA is the input

  30. Instructional design: proteins

  31. Instructional design: phage

  32. Instructional design: phage

  33. Molecular motors • Bacteria swim by rotating flagella • Motor located at junction of flagellum and cell envelope • Motor can rotate clockwise (CW) orcounterclockwise (CCW) CW CCW CW

  34. Applications of biomolecular computing • Massively parallel problem solving • Combinatorial optimization • Molecular nano-memory with fast associative search • AI problem solving • Medical diagnosis, drug discovery • Cryptography • Further impact in biology and medicine: • Wet biological data bases • Processing of DNA labeled with digital data • Sequence comparison • Fingerprinting

  35. Future applications

  36. Interesting possibilities a) Self-replication: Two for one Based on DNA self-replication b) Self-repair: Based on regeneration c) DNA computer mutation/evolution d) New meaning of a computer virus ? Learning. May be malignant or biohazard

  37. Evolvable biomolecular hardware Sequence programmable and evolvable molecular systems have been constructed as cell-free chemical systems using biomolecules such as DNA and proteins.

  38. Molecular storage Trillions of DNA Phone book …

  39. Molecular computer on a chip Microreactor PCR Gel Electrophoresis Bead Detection + DNA computing algorithm MEMS (Microfluidics)

  40. BioMEMS

  41. Lab-on-a-chip technology Integrates sample handling, separation and detection and data analysis for: DNA, RNA and protein solutions using LabChip technology

  42. Conclusions

  43. Conclusions • DNA Computing uses DNA molecules to computing or storage materials. • DNA computing technology has many interesting properties, including • Massively parallel, solution-based, biochemical • Nano-scale, biocompatible • high energy efficiency • high memory storage density • DNA computing is in very early stage of development.

  44. Research groups • MIT, Caltech, Princeton University, Berkeley, Yale, Duke, Irvine, Delaware, Lucent • Molecular Computer Project (MCP) in Japan • EMCC (European Molecular Computing Consortium) is composed of national groups from 11 European countries • BioMIP (BioMolecular Information Processing) at the German National Research Center for Information Technology (GMD) • Leiden Center for Natural Computation (LCNC)

  45. Web resources • Biomolecular Computation (BMC)www.cs.duke.edu/~reif/ • Leiden Center for Natural Computation (LCNC)www.wi.leidenuniv.nl/~lcnc/ • BioMolecular Information Processing (BioMip)www.gmd.de/BIOMIP • European Molecular Computing Consortium (EMCC)http://openit.disco.unimib.it/emcc/ • DNA Computing and Informatics at Surfaces www.corninfo.chem.wisc.edu/writings/DNAcomputing.html • DNA nanostructres http://seemanlab4.chem.nyu.edu/

  46. Books • Cristian S, Calude and Gheorghe Paun, Computing with Cells and Atoms: An introduction to quantum, DNA and membrane computing, Taylor & Francis, 2001. • Pâun, G., Ed., Computing With Bio-Molecules: Theory and Experiments, Springer, 1999. • Gheorghe Paun, Grzegorz Rozenberg and Arto Salomaa, DNA Computing, New Computing Paradigms, Springer, 1998. • C. S. Calude, J. Casti and M. J. Dinneen, Unconventional Models of Computation, Springer, 1998. • Tono Gramss, Stefan Bornholdt, Michael Gross, Melanie Mitchell and thomas Pellizzari, Non-Standard Computation: Molecular Computation-Cellular Automata-Evolutionary Algorithms-Quantum Computers, Wiley-Vch, 1997.

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