1. part 2

2. 1Introduction 2Theoretical background  Biochemistry/molecular biology  Computation 3Extension of theoretical background (biochemistry or computer science) 4History of the field 5Splicing systems 6P systems 7Hairpins 8Micro technology introductions Microreactors / Chips 9Microchips and fluidics 10Self assembly 11Regulatory networks 12Molecular motors 13DNA nanowires 14DNA computing - summery Course outline

3. namedanny van noort OfficeRoom 410 buildingRIACT tel:None yet emaildanny@bi.snu.ac.kr webhttp://bi.snu.ac.kr/ Where to find me

4. date8 th and 10 th of June Announcement NO Lecture

5. The highlights

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

7. 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. Lenard Adleman: hamiltonian path

8.  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. Eric Winfree: DNA self-assembly

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

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

14.  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. Gheorghe Păun: P-systems

15. ab c

16. ab bc aabc Gheorghe Păun: P-systems

17.  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. Tom Head: splicing systems

18. These are the techniques that are common in the microbiologist's lab and can be used to program a molecular computer. DNA can be:  synthezisedesired strands can be created  separatestrands can be sorted and separated by length  mergeby pouring two test tubes of DNA into one to perform union  extractextract those strands containing a given pattern  melt/annealbreaking/bonding two ssDNA molecules with complementary sequences  amplifyuse of PCR to make copies of DNA strands  cutcut DNA with restriction enzymes  rejoinrejoin DNA strands with 'sticky ends'  detectconfirm presence or absence of DNA Tom Head: splicing systems

19. Q. Liu: experiments on a surface

20. (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} Q. Liu: experiments on a surface

22. Computing in biology

23.  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. Computing in biology

24. The biology of computing

25. Pyrimidine pathway

26. Electronic pathway

27. Tokyo subway system

28. lacl cl tetRgfp P T P P L 2 P T - tetlacctgfp From Guet et al., Science 24 May 2002  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 Transcriptional regulators

29.  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 Instructional design

30. Instructional design: proteins

31. Instructional design: phage

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

34.  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 Applications of biomolecular computing

35. Future applications

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

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. Trillions of DNA NameTel.Address James419-1332Washington DC David352-4730La Jolla, CA. Paul648-7921Honolulu, HI Julia418-9362Palo Alto CA … Phone book Molecular storage

39. 39 DNA computing algorithmMEMS (Microfluidics) + Molecular computer on a chip

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.  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. Conclusions

44.  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) Research groups

45. 45  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 / Web resources

46.  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. Books