Course outline
1
Introduction
2
Theoretical background
Biochemistry/molecular biology
3
Theoretical background computer science
4
History of the field
5
Splicing systems
6
P systems
7
Hairpins
8
Detection techniques
9
Micro technology introduction
10
Microchips and fluidics
11
Self assembly
12
Regulatory networks
13
Molecular motors
14
DNA nanowires
15
Protein computers
16
DNA computing - summery
17
Presentation of essay and discussion
Introduction
What is self-organisation?

System with discrete components

Spontaneously ordered properties

Global Order from Local, random
interactions
Living systems

Self-organized catalytic set of molecules

Origin of life

RNA world

Driving force is G

Goal is self-replication
Artificial self-organisation systems

Self-Reproducing (cellular) Automata

Artificial Neural Networks

Boolean Networks

Artificial Life Systems

Evolutionary Systems

DNA Systems
Self-organisation DNA systems

Seeman-Winfree

Construction of Specific Geometrical
and Topological Targets from DNA

Construction Process => Computation

Cellular Automata and Tilings

Basic Building Block is Stiff
Double-Crossover Molecule (DX)
DNA
Self-assembly

A process involving the spontaneous selfordering
of
substructures
into
superstructures.

Is a Bottom-up Process rather than a TopDown process used in most manufacturing or
lithography processes
Cellular self-assembly

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
Construction with smart brick
Tiles binding mechanisms
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.
Scale of tiling assembly
Meso-scale tiling assemblies
have
tiles
millimeters
centimeters.
of
up
size
to
a
a
few
few
Molecular-scale tiling assemblies
have tiles of size
hundred Angstroms.
up
to
a
few
Magnetic meso-scale self-assembly

Self assembly on Water/Air Interface.

Pads with magnetic orientations constructed by curing
polymer/ferrite composites in the presence of strong magnet
fields.
Wisconsin material sciences group
Magnetic meso-scale self-assembly
Wisconsin material sciences group
Magnetic meso-scale self-assembly
Wisconsin material sciences group
Programming 2-d DNA lattices

for the construction
scale structures

for
rendering
molecular level
of
patterns
molecular
at
the
Programming 2-d DNA lattices
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
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
Programming 2-d DNA lattices
DX molecules

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

DNA
crossover
molecules
self-assembled
from
artificially synthesized single stranded DNA.
DX molecules
DNA tiles

Double-crossover
Seeman]:
(DX)
Tiles
 consist
of two double-helices
crossover strands.
[Winfree,
fused
by
 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

Pads:
 Tiles have sticky ends that preferentially
match the sticky ends of certain other DNA
tiles.
 The
sticky ends facilitate the
assembly into tiling lattices.
further
 Total of 4 Pads of single stranded DNA at
ends.
TX tiles

Triple-crossover (TX) Tiles consist of three
double-helices fused by crossover strands.

TAE contains an even number of helical halfturns between crossover points.

TAO contains an odd number.

Total of 6 Pads of single stranded DNA at
ends.
[LaBean et al, J. Am. Chem. Soc., 2000]
TX tiles
[LaBean et al, J. Am. Chem. Soc., 2000]
TX tiles
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
Another way
Still another way
Or another way
Self assembly and computation

A tiling is an arrangement of tiles
(shapes) that covers a plane

Tiles fit based on matching
(complementary shapes)
rules
Self assembly and computation
XOR tile
Self assembly and computation
Wang Tile
Self assembly and computation

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.
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)
DNA
The powerful molecular
pairing can be used in

recognition
system
Nanotechnology to direct the assembly
structured
materials
with
specific
of
base
of highly
nanoscale
features

DNA computation to process complex information.
Appealing features include

Minuscule size,
nanometres
with
a
diameter
of
about
2

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 as building material
Sticky ended cohesion-ligation
DNA as building material
Assembly of branched junctions into a 2-d lattice
DNA as building material
Holiday junction
DNA as building material
Flexibility of DNA branched junctions
DNA as building material
a
b

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
Constructing DNA objects
Constructing DNA objects
Borromean Rings
Truncated Octaheadron
Construction of tiles

Design & Synthesize Oligonucleotides

Formation of H-bonded Complex

Purification using Gel Elecrophoresis to
eliminate the linear strands

Phosphorylation and Ligation
Construction of tiles
Crossover molecules
Single molecule gaps
Limitations

Fault tolerance:
 Result is probabilistic,
e.g. 2-5% error in XOR computation
 Only
sticky
open
one
set
of
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 20-30 ligated crossovers
 Then can we layout 3D materials,
e.g. circuit patterns?
DNA topological structures
Ned Seeman
DNA topological structures
Ned Seeman
DNA topological structures
Ned Seeman
Imaging
TX tiles
Imaging

Metallic nanoparticles.

Triangles or multi-triangle tiles.

Biotin-streptavidin
nanogold).

Multi-tile subassemblies.

New tile topologies.

Stem-loops
(with
or
without
Imaging
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
Modified DNA tiles
Modified DNA tiles
Facilitates visualization by imaging devices such as AFM.
Modified DNA tiles
TEM image of TAO AB* lattice
1
3
A
2
4
A
B
A
B
A
A
B
A
4’
2’
B
3’
A
A
A
A
B
B
A
B
B
B
A
B
B
B
B
1’
A
A
A
B
B
B
A
A
A
B
B
A
B
A
A
B
B
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
Platinum rotary-shadow TEM
assembled by stoichiometric
image of
annealing
DNA lattice
of 8 oligos
designed to form two tile types (A and B):

A tiles (lighter) only
(darker) and vice versa.
associate
with
B
tiles

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
periodicity, as designed.
have
approximately
28
nm
TEM image of TAO AB* lattice
Applications
Directed nucleation assembly
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.
XOR
x
y
XOR
0
0
0
0
1
1
1
0
1
1
1
0
Cumulative XOR
Inputs = xi
Outputs = yi
1 Choose x1, then
set y1 = x1
2 Then for i > 1
yi = yi-1XORxi
Tiles XOR
Inputs (x = 0, 1)
Start keys
Outputs: yi = f(xi,yi-1)
Assembled XOR arrays
yi = yi-1 XOR xi
Assembled XOR arrays
Algorithmic assembly
X1 tiles
C tiles
Y1 tiles
X2 tiles
Reporter strand
Y2 tiles
PCR with primers for
Reporter Strand
Sticky
ends
binds
Ligation
Extraction of results
Reporter strand
EcoR:
GATATC
PvuII: CAGCTG
EcoR(1) cut
PvuII(0) cut
Directed nucleation assembly
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
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
and
loop-tiles
0s
and
is
propagated
loopless-tiles
up
can
the
be
distinguished by AFM as demonstrated with periodic
AB* lattice.
Directed nucleation assembly
Barcode Lattice for Readout
1
0
1
1
0
0
0
1
0
1
1
1
Input Strand
Directed nucleation assembly
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
Computation by self-assembly
Tiling Self-assembly can

Provide arbitrarily complex assemblies
only a small number of component tiles.
using

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 493-495 (2000).
Computation by self-assembly
Pads
 complementary
base
sequences
determining
relations of tiles in final assembly
neighbour
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 with smart bricks
0
0
1
1
0
1
1
0
1
4
4
1
A
B
A
B
1
1
2
2
0
4
4
0
Sorting
2
4
4
2
0
3
3
0
3
4
4
3
2
3
3
2
A
B
B
A
3
3
5
5
2
2
6
6
5
5
6
6
5
6
6
5
6
6
7
7
5
7
7
5
7
7
7
7
A tiling assembly using `Smart Bricks' to sort 8 keys.
Domino tiling problem
Defined by Wang [Wang61]
Input

a finite set of unit size square tiles,

Tile pads: each of whose sides
symbols over a finite alphabet.

initial placement of a subset of certain tiles,

dimensions of the region where tiles must be placed.
are
labeled
with
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.
Rates of self-assembly
Speed of DNA self-assembly reactions

Between a few seconds to many minutes.

Far slower
technology.
per
assembly
than
silicon
Concurrent DNA self-assembly

Concurrent assemblies execute computations
independently.

Executes massively
at molecular scale.

Degree of parallelism
parallel
from
computation
1015 to 1018.
References

Mao, et al. “Logical computation using algorithmic selfassembly
of
DNA
triple-crossover
molecules”,
Nature
407:493, 2000.

Winfree, E. “Algorithmic self-assembly of DNA: Theoretical
motivations and 2D assembly experiments”, J. Biomolecular
Structure and Dynamics, 11:263, 2000.

LaBean,
et
al.
“Construction,
analysis,
ligation,
and
self-assembly of DNA triple crossover complexes”, JACS,
122:1848, 2000.

Rothemund, et al. “Using Capillary forces to compute by
self-assembly”, PNAS, 97: 984-989 , 2000

Seeman, et al. “Nucleic acid nanostructures and topolgy”,
Angew. Chem. Int. Edn. Engl. 37, 3220-3238 , 1998