2. Who am I?

3. namedanny van noort educationMSc. experimental physics university of Leiden The Netherlands PhD. applied physics Linköpings university Sweden Post docsBioMIP (BioMolecular Information Processing) Institute of computer science and mathematics (GMD) Sankt Augustin Germany Dept. of Ecology and Evolutionary Biology Princeton University USA Who am I?

4. namedanny van noort OfficeRoom 115 building#138, ICT tel:880 9131 0r 881 9882 emaildanny@bi.snu.ac.kr webhttp://bi.snu.ac.kr/ Where to find me

7. Course outline

8. 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

9.  Essay + presentation on new molecular computing idea  Presentation on literature

10. date3 th and 10 th of June Announcement NO Lecture

11. What is DNA computing?

12. What is DNA Computing?  The field of DNA computing is concerned with the possibility of performing computations using biological molecules.  It is also concerned with understanding how complex biological molecules process information in an attempt to gain insight into new models of computation.  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 such biological phenomena and gain insight into early molecular evolution and the original of biological information processing.

13. Molecular electronics, Theoretical biology, Evolutionary biology, Emergent computation, Brain sciences, Organic chemistry, Biomimetic engineering, Parallel processing, Distributed computing, Behavioural ecology, Cytology, Discrete mathematics, Optimisation theory, Artificial Intelligence, Cognitive science, Botany, Psychology, Algorithmics, Clinical engineering, Biophysics, Connectionism, Integrative physiology, Technology transfer, Selectionism, Immunology, Automata theory, Evolutionary computation, Simulation of computational systems, Histology, Ethology, Medical computing, Signal transduction and processing, Cellular automata, Electronic engineering, Vision, Object oriented design, Philosophy of science, VLSI, Non-linear dynamical systems, Game theory, Communication, Bioengineering, Self-organisation, Biochemistry, Pattern recognition, Information theory, Machine learning, Biosystem simulation, Genetics, Mathematical biology, Microbiology, Zoology, Science education, Physiology, Systems theory, Biosensors, Analogue devices and sensors, Microtechnology, Robotics... What is DNA Computing?

14. Molecular electronics, Theoretical biology, Evolutionary biology, Emergent computation, Brain sciences, Organic chemistry, Biomimetic engineering, Parallel processing, Distributed computing, Behavioural ecology, Cytology, Discrete mathematics, Optimisation theory, Artificial Intelligence, Cognitive science, Botany, Psychology, Algorithmics, Clinical engineering, Biophysics, Connectionism, Integrative physiology, Technology transfer, Selectionism, Immunology, Automata theory, Evolutionary computation, Simulation of computational systems, Histology, Ethology, Medical computing, Signal transduction and processing, Cellular automata, Electronic engineering, Vision, Object oriented design, Philosophy of science, VLSI, Non-linear dynamical systems, Game theory, Communication, Bioengineering, Self-organisation, Biochemistry, Pattern recognition, Information theory, Machine learning, Biosystem simulation, Genetics, Mathematical biology, Microbiology, Zoology, Science education, Physiology, Systems theory, Biosensors, Analogue devices and sensors, Microtechnology, Robotics... What is DNA Computing?

15. 15 011001101010001ATGCTCGAAGCT What is DNA Computing?

16.  a completely new method among a few others (e.g., quantum computing) of general computation alternative to electronic/semi-conductor technology  uses biochemical processes based on DNA

17. What is DNA Computing not?  not to confuse with bio-computing which applies biological laws (evolution, selection) to computer algorithm design.

18. Biocomputing vs. Bioinformatics Biomolecular computing DNA computing

19. 1.6-2.2 Inter-metal Dielectric K 45 nm 16 Known CMOS limitations 1999 200220052008 2011 <1.5 Source: Texas Instruments and ITRS IC Design Technology Working Group 140 nm 80 nm 60 nm parameters approach molecule size Gate length 4.0 2.7 1.6-2.2 0.25 1 4 Relative Fab Cost

20. 20 Future technology Source: Motorola, Inc, 2000 Now +2+4+6+8+10+12 Full motion mobile video/office Metal gates, Hi-k/metal oxides, Lo-k with Cu, SOI Pervasive voice recognition, “smart” transportation Vertical/3D CMOS, Micro-wireless nets, Integrated optics Smart lab-on-chip, plastic/printed ICs, self-assembly Quantum computer, molecular electronics Bio-electric computers Wearable communications, wireless remote medicine, ‘hardware over internet’ ! 1e6-1e7 x lower power for lifetime batteries True neural computing

21. Historical timeline Research 1950’s … R.Feynman’s paper on sub microscopic computers 1994 L.Adleman solves Hamiltonian path problem using DNA. Field started 1995 D.Boneh paper on breaking DES with DNA Commercial 1970’s … DNA used in bio application 1996 Human Genome Sequence 2000 DNA computer architecture ? Affymetrix sells GeneChip DNA analyzer 2015 Commercial computer ? 2005 Lucent builds DNA “motor”

22. DNA computers vs. conventional computers DNA-based computersMicrochip-based computers slow at individual operations fast at individual operations can do billions of operations simultaneously can do substantially fewer operations simultaneously can provide huge memory in small space smaller memory setting up a problem may involve considerable preparations setting up only requires keyboard input DNA is sensitive to chemical deterioration electronic data are vulnerable but can be backed up easily

23. Computer speed  number of parallel processors  number of steps each processor can perform per unit of time DNA computer  3 grams of water contains 10 22 molecules  massively parallel Electronic computer  advantage in number of steps performed per unit of time Speed of DNA computing

24. information per space unit perform per unit of time DNA computer  10 6 Gbits per cm 2 (1 bit per nm 3 ) Electronic computer  1 Gbits per cm 2 Density of DNA computing

25. DNA computer  10 19 operations per Joule Electronic computer  10 9 operations per Joule Efficiency of DNA computing

26. DNA as Computational Tool

27. DNA as computing tool

28. Nucleotide:  purine or pyrimidine base  deoxyribose sugar  phosphate group Purine bases  A(denine), G(uanine) DNA sequences consist of  A, C, G, T Pyrimidine bases  C(ytosine), T(hymine) DNA as computing tool

32. {000} {001} {010} {011} {100} {101} {110} {111} All possible solutions

33. Negative selection

34. Selection principle

35. V0-1: 5'-AACCACCAACCAAACC V0-0: 5'-AAAACGCGGCAACAAG V1-1: 5'-TCAGTCAGGAGAAGTC V1-0: 5'-TCTTGGGTTTCCTGCA V2-1: 5'-TTTTCCCCCACACACA V2-0: 5'-TTGGACCATACGAGGA V3-1: 5'-CGTTCATCTCGATAGC V3-0: 5'-AGAGTCTCACACGACA V4-1: 5'-AAGGACGTACCATTGG V4-0: 5'-CTCTAGTCCCATCTAC V5-1: 5'-CAACGGTTTTATGGCG V5-0: 5'-GCGCAATTTGGTAACC V6-1: 5'-TAGCAGCTTCCTTACG V6-0: 5'-ACACTGTGCTGATCTC V7-1: 5'-CACATGTGTCAGCACT V7-0: 5'-TGTGTGTGCCTACTTG V8-1: 5'-GATGGGATAGAGAGAG V8-0: 5'-AATCCCACCAGTTGAC V9-1: 5'-ATGCAGGAGCGAATCA V9-0: 5'-GCTTGTTCAACCTGGT V10-1: 5'-CCCAGTATGAGATCAGV10-0: 5'-CTGTCCAAGTACGCTA V11-1: 5'-ATCGAGCTTCTCAGAGV11-0: 5'-TGTAGAGGCTAGCGAT Word design with 16 bases

36. Logic operations

38. Logic NOT operations

39. a  b Logic AND operations

40. a  ba  b Logic OR operations

41.  ((  h   f)   a)   ((  g   i)   b)   ((  d   h)   c)   ((  c   i)   d)   ((  a   g)   f) 3x3 knight problem

43. Selection module

44. magnet Positive selection module

45. magnet Positive selection module

46. Some pictures

47. 3.5mm

54. The highlights

55. 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

56. 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

57.  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

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

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

63.  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

64. ab c

65. ab bc aabc Gheorghe Păun: P-systems

66.  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

67. 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

68. Q. Liu: experiments on a surface

69. (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

71. Computing in biology

72.  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

73. The biology of computing

74. Pyrimidine pathway

75. Electronic pathway

76. Tokyo subway system

77. 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

78.  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

79. Instructional design: proteins

80. Instructional design: phage

82.  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

83.  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

84. Future applications

85. 85 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

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

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

88. 88 DNA computing algorithmMEMS (Microfluidics) + Molecular computer on a chip

89. BioMEMS

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

91. Conclusions

92.  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

93.  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 (former GMD, now Fraunhofer)  Leiden Center for Natural Computation (LCNC) Research groups

94. 94  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

95.  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