Who am I?
Who am I?
name
danny van noort
education
MSc. experimental physics
university of Leiden
The Netherlands
PhD. applied physics
Linköpings university
Sweden
Post docs
BioMIP
(BioMolecular Information Processing)
Institute of computer science and
mathematics (GMD)
Sankt Augustin
Germany
Dept. of Ecology and Evolutionary
Biology
Princeton University
USA
Where to find me
name
danny van noort
Office
Room 115
building
#138, ICT
tel:
880 9131 0r 881 9882
email
danny@bi.snu.ac.kr
web
http://bi.snu.ac.kr/
Where to find me
Where to find me
Course outline
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
Course outline
 Presentation
 Essay
idea
on literature
+ presentation on new molecular computing
Announcement
NO
date
Lecture
3th and 10th of June
What is DNA computing?
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.
What is DNA Computing?
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?
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?
011001101010001
ATGCTCGAAGCT
15
What is DNA Computing?
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
What is DNA Computing not?
 not
to confuse with bio-computing which
applies
biological
laws
(evolution,
selection) to computer algorithm design.
Biocomputing vs. Bioinformatics
Biomolecular computing
DNA computing
Known CMOS limitations
Gate length
140 nm
4.0
Inter-metal Dielectric K
Relative Fab Cost
16
80 nm
2.7
1
45 nm
1.6-2.2
0.25
1999
2002
parameters
approach
molecule
size
4
60 nm
2005
1.6-2.2
2008
<1.5
2011
Source: Texas Instruments and ITRS IC Design Technology Working Group
Future technology
True neural computing
Bio-electric
computers
1e6-1e7 x lower power
for lifetime batteries
Full motion
mobile
video/office
Metal gates,
Hi-k/metal
oxides, Lo-k
with Cu, SOI
Now
+2
Quantum computer,
molecular
electronics
Smart lab-on-chip,
plastic/printed
ICs, self-assembly
Vertical/3D CMOS,
Micro-wireless
nets, Integrated
optics
Wearable communications,
wireless remote medicine,
‘hardware over internet’ !
Pervasive voice
recognition, “smart”
transportation
+4
+6
+8
+10
+12
20
Source: Motorola, Inc, 2000
Historical timeline
Research
1994
1950’s …
R.Feynman’s
paper on sub
microscopic
computers
1995
2000
L.Adleman solves
D.Boneh paper
Hamiltonian path
on breaking
problem using DNA. DES with DNA
Field started
2005
Lucent
builds DNA
“motor”
DNA computer
architecture ?
Commercial
1970’s …
DNA used
in bio
application
1996
Affymetrix
sells GeneChip
DNA analyzer
2000
Human
Genome
Sequence
2015
Commercial
computer ?
DNA computers vs. conventional computers
DNA-based computers
Microchip-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
Speed of DNA computing
Computer speed
 number of parallel processors
 number of steps each processor can perform per unit of time
DNA computer
 3 grams of water contains 1022 molecules
 massively parallel
Electronic computer
 advantage in number of steps performed per unit of time
Density of DNA computing
information per space unit perform per unit of time
DNA computer
 106 Gbits per cm2 (1 bit per nm3)
Electronic computer
 1 Gbits per cm2
Efficiency of DNA computing
DNA computer
 1019 operations per Joule
Electronic computer
 109 operations per Joule
DNA as Computational Tool
DNA as computing tool
DNA as computing tool
DNA sequences consist of
 A, C, G, T
Nucleotide:
 purine or pyrimidine base
 deoxyribose sugar
 phosphate group
Purine bases
 A(denine), G(uanine)
Pyrimidine bases
 C(ytosine), T(hymine)
DNA as computing tool
DNA as computing tool
DNA as computing tool
All possible solutions
{000}
{010}
{100}
{110}
{001}
{011}
{101}
{111}
Negative selection
Selection principle
5'-ACACTGTGCTGATCTC-3'
5'-TAGCAGCTTCCTTACG-3'
3'-ATCGTCGAAGGAATGC-5'
Vn-2
Vn-1
Vn = 0
Vn-2
Vn-1
Vn = 1
Vn+1
Vn+1
Capture probe
(Vn = 1)
Bead
Vn+2
Vn+3
Vn+2
Vn+3
Word design with 16 bases
V0-1:
V1-1:
V2-1:
V3-1:
V4-1:
V5-1:
V6-1:
V7-1:
V8-1:
V9-1:
V10-1:
V11-1:
5'-AACCACCAACCAAACC
5'-TCAGTCAGGAGAAGTC
5'-TTTTCCCCCACACACA
5'-CGTTCATCTCGATAGC
5'-AAGGACGTACCATTGG
5'-CAACGGTTTTATGGCG
5'-TAGCAGCTTCCTTACG
5'-CACATGTGTCAGCACT
5'-GATGGGATAGAGAGAG
5'-ATGCAGGAGCGAATCA
5'-CCCAGTATGAGATCAG
5'-ATCGAGCTTCTCAGAG
V0-0:
V1-0:
V2-0:
V3-0:
V4-0:
V5-0:
V6-0:
V7-0:
V8-0:
V9-0:
V10-0:
V11-0:
5'-AAAACGCGGCAACAAG
5'-TCTTGGGTTTCCTGCA
5'-TTGGACCATACGAGGA
5'-AGAGTCTCACACGACA
5'-CTCTAGTCCCATCTAC
5'-GCGCAATTTGGTAACC
5'-ACACTGTGCTGATCTC
5'-TGTGTGTGCCTACTTG
5'-AATCCCACCAGTTGAC
5'-GCTTGTTCAACCTGGT
5'-CTGTCCAAGTACGCTA
5'-TGTAGAGGCTAGCGAT
Logic operations
Logic operations
Logic NOT operations
Logic AND operations
a  b
Logic OR operations
a  b
3x3 knight problem

((h  f)  a) 

((g  i)  b) 

((d  h)  c) 

((c  i)  d) 

((a  g)  f)
3x3 knight problem
Selection module
Positive selection module
magnet
Positive selection module
magnet
Some pictures
3.5mm
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
(wxy)  (wyz)  (xy)  (wy)=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
85
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
88
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 (former GMD, now Fraunhofer)

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/
94
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.
Books