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1Introduction 2Theoretical background Biochemistry/molecular biology 3Theoretical background computer science 4History of the field 5Splicing systems.

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Presentation on theme: "1Introduction 2Theoretical background Biochemistry/molecular biology 3Theoretical background computer science 4History of the field 5Splicing systems."— Presentation transcript:


2 1Introduction 2Theoretical background Biochemistry/molecular biology 3Theoretical background computer science 4History of the field 5Splicing systems 6P systems 7Hairpins 8Detection techniques 9Micro technology introduction 10Microchips and fluidics 11Self assembly 12Regulatory networks 13Molecular motors 14DNA nanowires 15Protein computers 16DNA computing - summery 17Presentation of essay and discussion Course outline

3 Introduction

4 What is self-organisation? System with discrete components Spontaneously ordered properties Global Order from Local, random interactions

5 Self-organized catalytic set of molecules Origin of life RNA world Driving force is G Goal is self-replication Living systems

6 Self-Reproducing (cellular) Automata Artificial Neural Networks Boolean Networks Artificial Life Systems Evolutionary Systems DNA Systems Artificial self-organisation systems

7 Seeman-Winfree Construction of Specific Geometrical and Topological Targets from DNA Construction Process => Computation Cellular Automata and Tilings Basic Building Block is Stiff DNA Double-Crossover Molecule (DX) Self-organisation DNA systems

8 A process involving the spontaneous self- ordering of substructures into superstructures. Is a Bottom-up Process rather than a Top- Down process used in most manufacturing or lithography processes Self-assembly

9 Cells perform a multiplicity of self-assemblies: Cell walls (via lipids), Microtubules Cellular Superstructures and Transport Structures Utilize the specificity of ligand affinities to direct the self-assembly Cellular self-assembly

10 Construction with smart brick

11 Molecular affinity hydrogen bonding of complementary DNA or RNA bases Magnetic attraction (U. of Wisconsin materials science group) pads with magnetic orientations constructed by curing polymer/ferrite composites in the presence of strong magnet fields, or pads with patterned strips of magnetic orientations [Reif]. Capillary force [Whitesides], [Rothmemund, 1999] using hydrophobic/hydrophilic (capillary) effects at surface boundaries that generate lateral forces. Shape complementarity [Whitesides] using the conformational shape affinity of the tile sides to hold them together. Tiles binding mechanisms

12 Scale of tiling assembly Meso-scale tiling assemblies have tiles of size a few millimeters up to a few centimeters. Molecular-scale tiling assemblies have tiles of size up to a few hundred Angstroms.

13 Magnetic meso-scale self-assembly Wisconsin material sciences group Self assembly on Water/Air Interface. Pads with magnetic orientations constructed by curing polymer/ferrite composites in the presence of strong magnet fields.

14 Magnetic meso-scale self-assembly Wisconsin material sciences group

15 Magnetic meso-scale self-assembly Wisconsin material sciences group

16 Programming 2-d DNA lattices for the construction of molecular scale structures for rendering patterns at the molecular level

17 A 2D DNA lattice is constructed by a self-assembly process Begins with the assembly of DNA tile nanostructures DNA tiles of size 14 x 7 nanometers Composed of short DNA strands with Holliday junctions These DNA tiles self-assemble to form a 2D lattice: The assembly is programmable Tiles have sticky ends that provide programming for the patterns to be formed. Alternatively, tiles self-assemble around segments of a DNA strand encoding a 2D pattern. Programming 2-d DNA lattices

18 Patterning Each of these tiles has a surface perturbation depending on the pixel intensity. pixel distances 7 to 14 nanometers Key Applications Assembly of molecular electronic components and circuits molecular robotic components image rendering cryptography mutation detection

19 Programming 2-d DNA lattices

20 DX is double crossover Antiparallel strands 4-arm junctions Full turn in B-form of DNA (10.5 bp) Even or Odd number of half turns DAE, DAO DX molecules

21 DNA crossover molecules self-assembled from artificially synthesized single stranded DNA.

22 DX molecules

23 Double-crossover (DX) Tiles [Winfree, Seeman]: consist of two double-helices fused by crossover strands. DAE contains an Even number of helical half-turns between crossover points. DAO contains an Odd number. Anti-parallel crossovers: cause a reversal in direction of strand propagation through the tile following exchange of strand to a new helix. DAO and DAE are double-crossover DX tiles with two anti-parallel crossovers. DNA tiles

24 Pads: Tiles have sticky ends that preferentially match the sticky ends of certain other DNA tiles. The sticky ends facilitate the further assembly into tiling lattices. Total of 4 Pads of single stranded DNA at ends. DNA tiles

25 TX tiles [LaBean et al, J. Am. Chem. Soc., 2000] Triple-crossover (TX) Tiles consist of three double-helices fused by crossover strands. TAE contains an even number of helical half- turns between crossover points. TAO contains an odd number. Total of 6 Pads of single stranded DNA at ends.

26 TX tiles [LaBean et al, J. Am. Chem. Soc., 2000]

27 Unique Sticky Ends on DNA tiles. Input layers can be assembled via unique sticky-ends at each tile joint thereby requiring one tile type for each position in the input layer. Tiling self-assembly proceeds by the selective annealing of the pads of distinct tiles, which allows tiles to compose together to form a controlled tiling lattice. TX tiles


29 Another way

30 Still another way

31 Or another way

32 A tiling is an arrangement of tiles (shapes) that covers a plane Tiles fit based on matching rules (complementary shapes) Self assembly and computation

33 XOR tile Self assembly and computation

34 Wang Tile Self assembly and computation

35 Given a Turing machine, tiles and matching rules can be designed so that the tilings formed correspond to a simulation of the Turing Machine. Computation by tiling is hence Universal i.e. all SA structures can be viewed as computation. Self assembly and computation

36 C-tile, P-tile and XOR tile Error rate 0.2%, 2.2%, 14.7% for C, P and XOR tiles; % error= mismatches/(mismatches+bonds)

37 The powerful molecular recognition system of base pairing can be used in Nanotechnology to direct the assembly of highly structured materials with specific nanoscale features DNA computation to process complex information. Appealing features include Minuscule size, with a diameter of about 2 nanometres Short structural repeat (helical pitch) of about 3.4–3.6 nm, Stiffness', with a persistence length (a measure of stiffness) of around 50 nm. DNA

38 Sticky ended cohesion-ligation DNA as building material

39 Assembly of branched junctions into a 2-d lattice

40 DNA as building material Holiday junction

41 DNA as building material Flexibility of DNA branched junctions

42 DNA drawn as a series of right angle turns Each edge of square contain 2 turns of helix in a but only 1.5 turns in b DNA as building material ba

43 Constructing DNA objects

44 Borromean RingsTruncated Octaheadron

45 Design & Synthesize Oligonucleotides Formation of H-bonded Complex Purification using Gel Elecrophoresis to eliminate the linear strands Phosphorylation and Ligation Construction of tiles


47 Single molecule gaps Crossover molecules

48 Fault tolerance: Result is probabilistic, e.g. 2-5% error in XOR computation Only open one set of sticky ends at a time to prevent incorrect binding (correct competes with partially correct) Performance highly sensitive to process (melting) conditions Differences from periodic tiling Correct tiles compete with partially correct tiles, thus amplifying error Efficiency (for small problems): Many serial chemistry steps for preparation, ligation, and analysis, e.g. a few days for XOR computation Scalability Reporter strand technique limited to ligated crossovers Then can we layout 3D materials, e.g. circuit patterns? Limitations

49 Ned Seeman DNA topological structures

50 Ned Seeman

51 DNA topological structures Ned Seeman

52 Imaging

53 TX tiles

54 Metallic nanoparticles. Triangles or multi-triangle tiles. Biotin-streptavidin (with or without nanogold). Multi-tile subassemblies. New tile topologies. Stem-loops Imaging

55 DNA Stem-loops: DNA tiles with additional stem-loops of 8 to 16 basepairs, directed out of the plane of the tile helix axes, are used in DX and TX lattices to evaluate successful assembly of periodic arrays. Stem-loops can also be directed orthogonal to the tile helix axes within the tile plane in single layer assemblies. These loops are used mark binary values on the tiles where the presence of a loop indicates a 1 and the absence indicates 0. Modification of protruding stems or stem-loops with gold or biotin-streptavidin increases their visibility Imaging

56 Modified DNA tiles

57 Facilitates visualization by imaging devices such as AFM.

58 Modified DNA tiles

59 Cartoon of DNA lattice composed of two types of TAO tile: B with (dark) and A without (light) stem-loops directed out of the lattice plane. TEM image of TAO AB* lattice

60 Platinum rotary-shadow TEM image of DNA lattice assembled by stoichiometric annealing of 8 oligos designed to form two tile types (A and B): A tiles (lighter) only associate with B tiles (darker) and vice versa. B tiles appear darker due to increased platinum deposition on an extra loop of DNA directed out of the lattice plane. Stripes of dark B tiles have approximately 28 nm periodicity, as designed. TEM image of TAO AB* lattice


62 Applications

63 A method for assembly of complex patterns Use artificially synthesized DNA strands that specify the pattern and around which 2D DNA tiles assemble into the specified pattern. The permanent features of the 2D pattern are generated uniquely for each case. Directed Nucleation Self Assembly Steps an input DNA strand is synthesized that encodes the required pattern then specified tiles assemble around blocks of this input DNA strand, forming the required 1D or 2D pattern of tiles. Directed nucleation assembly

64 Cumulative XOR Inputs = x i Outputs= y i 1 Choose x 1, then set y 1 = x 1 2 Then for i > 1y i = y i-1 XORx i XOR xy

65 Start keysInputs (x = 0, 1) Outputs: y i = f(x i,y i-1 ) Tiles XOR

66 Assembled XOR arrays y i = y i-1 XOR x i

67 Assembled XOR arrays

68 X 1 tiles Y 1 tiles Y 2 tiles C tiles X 2 tiles Sticky ends binds Reporter strand Ligation PCR with primers for Reporter Strand Algorithmic assembly

69 Reporter strand EcoR(1) cutPvuII(0) cut EcoR: GATATC PvuII: CAGCTG Extraction of results

70 Barcode lattice displays banding patterns dictated by the sequence of bit values programmed on the input layer. Extends 2D arrays into simple aperiodic patterning: The pattern of 1s and 0s is propagated up the growing tile array. The 1-tiles are decorated with a DNA stem-loop pointing out of the tile plane (black rectangle) and 0-tiles are not. Columns of loop-tiles and loopless-tiles can be distinguished by AFM as demonstrated with periodic AB* lattice. Directed nucleation assembly

71 Barcode Lattice for Readout Input Strand Directed nucleation assembly

72 Applications Molecular Scale Patterning of Molecular Electronics and Molecular Motors. Image Storage: a region 100km x 100km imaged by a satellite to 1 cm resolution resulting image is of size 1,000,000 x 1,000,000, containing 1012 pixels requires a DNA lattice of size 2 millimeters on a side. Directed nucleation assembly


74 Computation by self-assembly Tiling Self-assembly can Provide arbitrarily complex assemblies using only a small number of component tiles. Execute computation, using tiles that specify individual steps of the computation. Computation by DNA tiling lattices First Proposed by [Winfree, 98]. First Experimentally demonstrated by [Mao, et al 2000] Mao, C., T.H. LaBean, J. H. Reif, and N.C. Seeman, An Algorithmic Self-Assembly, Nature, Sept 28, p (2000).

75 Pads complementary base sequences determining neighbour relations of tiles in final assembly Large-Scale Computational Tilings formed during assembly encode valid mappings of input to output. local tile association rules insure only valid computational lattices form regardless of temporal ordering of binding events. Key Advantageof DNA Self-Assembly for DNA Computing Use a sequence of only 4 laboratory procedures: mixing the input oligonucleotides to form the DNA tiles, allowing the tiles to self-assemble into superstructures, ligating strands that have been co-localized, and performing a single separation to identify the correct output. Computation by self-assembly

76 A tiling assembly using `Smart Bricks' to sort 8 keys. AB BA AB AB Computation with smart bricks Sorting

77 Defined by Wang [Wang61] Input a finite set of unit size square tiles, Tile pads: each of whose sides are labeled with symbols over a finite alphabet. initial placement of a subset of certain tiles, dimensions of the region where tiles must be placed. Domino Tiling Problem assuming arbitrarily large supply of each tile place the tiles to completely fill the given region each pair of abutting tiles must have identical symbols on their contacting sides. Domino tiling problem

78 Speed of DNA self-assembly reactions Between a few seconds to many minutes. Far slower per assembly than silicon technology. Concurrent DNA self-assembly Concurrent assemblies execute computations independently. Executes massively parallel computation at molecular scale. Degree of parallelism from to Rates of self-assembly

79 Mao, et al. Logical computation using algorithmic self- assembly of DNA triple-crossover molecules, Nature 407:493, Winfree, E. Algorithmic self-assembly of DNA: Theoretical motivations and 2D assembly experiments, J. Biomolecular Structure and Dynamics, 11:263, LaBean, et al. Construction, analysis, ligation, and self-assembly of DNA triple crossover complexes, JACS, 122:1848, Rothemund, et al. Using Capillary forces to compute by self-assembly, PNAS, 97: , 2000 Seeman, et al. Nucleic acid nanostructures and topolgy, Angew. Chem. Int. Edn. Engl. 37, , 1998 References

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