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p a r t 2 Course outline 1 Introduction 2 Theoretical background Biochemistry/molecular biology Computation 3 Extension of theoretical background (biochemistry or computer science) 4 History of the field 5 Splicing systems 6 P systems 7 Hairpins 8 Micro technology introductions Microreactors / Chips 9 Microchips and fluidics 10 Self assembly 11 Regulatory networks 12 Molecular motors 13 DNA nanowires 14 DNA computing - summery Where to find me name danny van noort Office Room 410 building RIACT tel: None yet email [email protected] web http://bi.snu.ac.kr/ Announcement NO date Lecture 8th and 10th of June The highlights 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 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. 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. Eric Winfree: DNA self-assembly Eric Winfree: DNA self-assembly Eric Winfree: DNA self-assembly danny van noort, october 2001 Ned Seeman: DNA self-assembly danny van noort, october 2001 Ned Seeman: DNA self-assembly 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. Gheorghe Păun: P-systems a b c Gheorghe Păun: P-systems a b aabc bc 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. 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 separate merge extract melt/anneal amplify cut rejoin detect desired strands can be created strands can be sorted and separated by length by pouring two test tubes of DNA into one to perform union extract those strands containing a given pattern breaking/bonding two ssDNA molecules with complementary sequences use of PCR to make copies of DNA strands cut DNA with restriction enzymes rejoin DNA strands with 'sticky ends' confirm presence or absence of DNA Q. Liu: experiments on a surface Q. Liu: experiments on a surface (wxy) (wyz) (xy) (wy)=1 {0000} {0010} {0100} {0110} {1000} {1010} {1100} {1110} {0001} {0011} {0101} {0111} {1001} {1011} {1101} {1111} Q. Liu: experiments on a surface Computing in biology 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. The biology of computing Pyrimidine pathway Electronic pathway Tokyo subway system Transcriptional regulators 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 L PT cl lacl tet PT P2 lac PtetR ct gfp gfp From Guet et al., Science 24 May 2002 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 Instructional design: proteins Instructional design: phage Instructional design: phage Molecular motors 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 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 Future applications 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. or May be malignant biohazard 36 Evolvable biomolecular hardware Sequence programmable and evolvable molecular systems have been constructed as cell-free chemical systems using biomolecules such as DNA and proteins. Molecular storage Trillions of DNA Name Phone book Tel. Address James 419-1332 Washington DC David 352-4730 La Jolla, CA. Paul 648-7921 Honolulu, HI … Julia 418-9362 Palo Alto CA Molecular computer on a chip + DNA computing algorithm MEMS (Microfluidics) Microreactor PCR Detection Bead Gel Electrophoresis 39 BioMEMS Lab-on-a-chip technology Integrates sample handling, separation and detection and data analysis for: DNA, RNA and protein solutions using LabChip technology Conclusions 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. 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) 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/ 45 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 AutomataEvolutionary Algorithms-Quantum Computers, Wiley-Vch, 1997.
