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DNA Computing Tutorial Biointelligence Lab. School of Compute Science and Engineering, Seoul National University 2002. 3. 13 Outline Molecular Intelligence DNA computing operators Introduction of DNA computing Examples The first DNA computing approach Molecular theorem prover

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dna computing tutorial

DNA Computing Tutorial

Biointelligence Lab.

School of Compute Science and Engineering,

Seoul National University

2002. 3. 13

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

outline
Outline
  • Molecular Intelligence
  • DNA computing operators
  • Introduction of DNA computing
  • Examples
    • The first DNA computing approach
    • Molecular theorem prover
  • The difficulties of DNA computer
  • DNA computing applications
  • Conclusion

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

molecular intelligence
Molecular Intelligence
  • Solving artificial intelligence problems using molecular computing technology
  • Computing model: evolutionary computing
  • Computing devices: biomolecules (DNA)
  • Learning and inference using biomolecular computing
  • Biochemical reaction
  • Massively parallel
  • Micro or nanoscale
  • Storage capacity
  • Bio-compatible

Artificial

Intelligence

Molecular

Computing

Molecular Intelligence

biocomputing vs bioinformatics

BT

IT

Biocomputing vs. Bioinformatics

Bioinformatics

Biocomputing

what is dna computing

What is DNA Computing?

The use of biological molecules, primarily DNA, DNA analogs, and RNA, for computation purpose.

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

dna computing
DNA Computing

011001101010001

ATGCTCGAAGCT

a different approach
A different approach
  • What DNA computing is:
    • a completely new method among a few others (e.g., quantum computing) of general computation alternative to electronic/semiconductor technology
    • uses biochemical processes based on DNA
  • What DNA computing isn’t:
    • not to confuse with bio-computing which applies biological laws (evolution, selection) to computer algorithm design.

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

why try new stuff
Why try new stuff ?
  • Will need a dramatically new technology to overcome some CMOS limitations and offer new opportunity.
  • Certain types of problems (learning, pattern recognition, fault-tolerant system, large set search algorithms) are intrinsically very difficult to solve even with fast evolution of CMOS.
  • Hope of achieving massive parallelism.

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

why try dna computing
Why try DNA Computing?
  • 6.022  1023molecules / mole
  • Massively Parallel Search of All Possibilities
    • Desktop: 109 operations / sec
    • Supercomputer: 1012 operations / sec
    • 1 mmol of DNA: 1026 reactions
  • Favorable Energetics: Gibb’s Free Energy
    • 1 J for 2  1019 operations
  • Storage Capacity: 1 bit per cubic nanometer

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

why try dna computing10
Why try DNA Computing?
  • The fastest supercomputer vs. DNA computer
    • 106 op/sec vs. 1014 op/sec
    • 109 op/J vs. 1019 op/J (in ligation step)
    • 1bit per 1012 nm3 vs. 1 bit per 1 nm3 (video tape vs. molecules)

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

known cmos limitations
Known CMOS limitations

Relative Fab Cost

140 nm

Gate length

16

80 nm

4.0

4

60 nm

parameters approach molecule size

Inter-metal Dielectric K

2.7

45 nm

1.6-2.2

1

1.6-2.2

0.25

<1.5

2011

2002

2005

2008

1999

Source: Texas Instruments and

ITRS IC Design Technology Working Group

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

future technology enablers
Future technology enablers

True neural computing

Bio-electric computers

1e6-1e7 x lower power for lifetime batteries

Quantum computer, molecular electronics

This talk

Smart lab-on-chip, plastic/printed ICs, self-assembly

Full motion mobile video/office

Vertical/3D CMOS, Micro-wireless nets, Integrated optics

Wearable communications, wireless remote medicine,

‘hardware over internet’ !

Pervasive voice recognition, “smart” transportation

Metal gates,

Hi-k/metal oxides, Lo-k with Cu, SOI

+2

+4

+6

+8

+10

+12

Now

Source: Motorola, Inc, 2000

historical timeline
Historical timeline

Research

1950’s …

1994

1995

2000

2005

R.Feynman’s

paper on sub microscopic

computers

L.Adleman solves Hamiltonian path problem using DNA.

Field started

D.Boneh paper

on breaking DES

with DNA

Lucent

builds DNA

“motor”

DNA computer

architecture ?

Commercial

2015

1970’s …

1996

2000

DNA used

in bio application

Human

Genome

Sequenced

Affymetrix sells GeneChip

DNA analyzer

Commercial computer ?

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

what s up to date
What’s up to date
  • Research still in very early stages but promise is big
  • First groundbreaking work by Adleman at USC only 6 years ago
  • No commercialization in sight within ~10 years
  • Main work on settling for a logic abstraction model, ways of using DNA to compute
  • Research sponsored by universities (Princeton, MIT, USC, Rutgers, etc) and NEC, Lucent Bell Labs, Telcordia, IBM
  • One way or another, DNA computing will have a significant impact

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

dna computers vs conventional computers
DNA Computers vs. Conventional Computers

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

dna computing takes advantage of
DNA Computing takes advantage of
  • Our ability to produce massive numbers of DNA molecules with specific properties (size, sequence)
  • The natural proclivity of specific DNA molecules to chemically interact according to defined rules to produce new molecules
  • Laboratory techniques that allow the isolation/identification of product molecules with specific properties
    • PCR, Ligation, Gel Electrophoresis, etc.

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

dna computing operator

DNA Computing Operator

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

basic building blocks

genes

nucleus

Cell

chromosome

Basic building blocks
  • Found in every cell’s nucleus, genes consist of tight coils of DNA’s double helix
  • Number of genes/length of DNA depends on species

DNA

dna structure
DNA Structure

Watson-Crick complement

dna memory

Number System (Base 4):

Complement

Nucleotide

Nucleotide

A

T

C

G

DNA binding

process

DNA Memory

A string composed of a series of four types of units (nucleotides), DNA may be viewed as logic memory or gate.

Two strings of DNA are bonded by paired nucleotides A-C and C-G which may be considered as complements. Example:

Number TTACAG has a complement AATGTC

dna memory21

a t c g g

t c a t a

g c a c t

0

0

0

DNA

memory

strands

1

0

1

t a g c c

c g t g a

t c a t a

a t c g g

DNA Memory

Writing : make DNA sequences

Reading : hybridization and readout

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

dna operators
DNA Operators
  • The bio-lab technology.
  • Hybridization
  • Ligation
  • Polymerase Chain Reaction (PCR)
  • Gel electrophoresis
  • Affinity separation (Bead)
  • Enzymes: restriction enzyme…

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

hybridization ligation
Hybridization & Ligation
  • Hybridization
    • base-pairing between two complementary single-strand molecules to form a double stranded DNA molecule
  • Ligation
    • Joining DNA molecules together
  • Solution generation step!

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

dna hybridization ligation
DNA Hybridization & Ligation

AGCTTAGGATGGCATGG

AATCCGATGCATGGC

+

CGTACCTTAGGCT

Hybridization

AGCTTAGGATGGCATGG

AATCCGATGCATGGC

CGTACCTTAGGCT

Ligation

AGCTTAGGATGGCATGGAATCCGATGCATGGC

CGTACCTTAGGCT

Dehybridization

AGCTTAGGATGGCATGGAATCCGATGCATGGC

+

CGTACCTTAGGCT

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

slide25
PCR
  • Polymerase Chain Reaction
  • Amplifies (produces identical copies of) selected dsDNA molecules.
  • Make 2ncopies (n : number of iteration)
  • Solution filtering or amplification step!

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

slide26

PCR

(Polymerase Chain Reaction)

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

gel electrophoresis
Gel electrophoresis
  • Molecular size fraction technique
  • Detect the specific DNA

Bead Separation

Solution detection or filtering step!

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

slide29

Bead Separation

Magnetic Beads

Magnet

Complementary

restriction enzyme
Restriction enzyme
  • Cut the specific DNA site.
  • Solution detection or filtering step!

A A G C T T

T T C G A A

OH 3’

5’ P

A

A C G T T

EcoRI

T T C G A

A

P 5’

3’ OH

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

the first dna computing method

The First DNA Computing Method

L. M. Adleman, Molecular Computation of Solutions to Combinatorial Problems, Science, 266:1021-1024, 1994

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

first special dna computer
First special DNA computer
  • Special problem: given N points, find a path visiting each and every point only once, and starting and ending at a given locations. (Hamiltonian path problem)
  • Solved with a DNA computer by Leonard Adleman in 1994 for N=6
  • Basic approach: code each point as an 8 unit DNA string, code each possible path, allow DNA bonding, suppress DNA with incorrect start/end points.

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

hamiltonian path problem

4

3

1

0

6

2

5

Hamiltonian Path Problem

The Hamiltoian path problem: as the number of cities grows, even supercomputers have difficulty finding the path.

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

adleman s molecular computer a brute force method
Adleman’s Molecular Computer: A Brute Force Method

Each city (vertex) is represented by a different sequence of nucleotides (6 here, but Adleman used 20).

A DNA linker (edge) joining two city (vertex) strands.

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

encoding basic concept
Encoding (Basic Concept)

4

3

1

0

AGCT TAGG

P1A

P1B

6

2

5

1

ATCC TACC

ATCC GCTA

P1B

P2A

P1B

P3A

3

2

ATGG CATG

CGAT CGAA

P2A

P2B

P3A

P3B

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

procedure
Procedure

Generate random paths

through the graph

Hybridization & Ligation

PCR with vin and vout

Keep only those paths that

begin with vin and end with vout

If the graph has n vertices,

then keep only those paths

that enter exactly n vertices

Gel electrophoresis

Keep only those paths that

enter all of the vertices

of the graph at least once.

Antibody bead separation

With vi

If any paths remain, say “Yes”;

otherwise, say “No.”

Gel electrophoresis &

Sequecing

slide37

Vertex 1

Vertex 2

ATGGCATG

AGCTTAGG

ATCCTACC

16 bp

56 bp

Edge 12

Step 4 : Gel Electrophoresis

AGCTTAGG

ATGGCATG

ATCC

TACC

Step 1 : Hybridization

AGCTTAGGATGGCATGGAATCCGA…

TCGAATCC

AGCTTAGG

ATGGCATG

ATCCTACC

Bead for vertex 1

Step 2 : Ligation

Step 5 : Magnetic Bead

Affinity Separation

Vertex 4

Vertex 1

AGCTTAGGATGGCATGGAATCCGATGCATGGC

TCGAATCC

ACGTACCG

Step 3 : PCR

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

slide38

Node 0 : ACG Node 3 : TAA

Node 1 : CGA Node 4 : ATG

Node 2 : GCA Node 5 : TGC

Node 6 : CGT

HPP

Encoding

Ligation

TAAACG

TGC

4

3

...

ATG

...

1

ATGTGCTAACGAACG

CGA

0

ACG

GCA

TAA

ACGCGAGCATAAATGTGCACGCGT

...

...

...

6

...

...

...

CGT

...

...

TAAACGGCAACG

...

...

...

...

2

5

...

ACGCGAGCATAAATGTGCCGT

...

ACGCGAGCATAAATGCGATGCACGCGT

...

CGACGTAGCCGT

...

...

CGACGT

...

...

...

...

...

PCR

(Polymerase

Chain

Reaction)

ACGCGAGCATAAATGTGCCGT

ACGGCATAAATGTGCACGCGT

Gel Electrophoresis

ACGCGAGCATAAATGCGATGCCGT

Solution

Affinity Column

4

3

Decoding

ACGCGTAGCCGT

1

...

0

ACGCGAGCATAAATGTGCCGT

...

...

6

...

ACGCGAGCATAAATGTGCACGCGT

...

ACGCGAGCATAAATGTGCCGT

ACGCGT

2

5

...

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

...

...

...

...

ACGCGAGCATAAATGCGATGCACGCGT

dna finds a solution
DNA finds a solution!

A Hamiltonian path with all vertices included is isolated and recovered

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

another practical example

Another Practical Example

Molecular theorem prover

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

molecular theorem prover
Molecular Theorem Prover
  • Resolution refutation method
  • Problem under consideration:
  • Turn into , add R as

R is true!

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

molecular theorem prover abstract implementation

¬S

¬T

Q

¬Q

¬P

R

¬S

¬T

Q

¬Q

¬P

R

¬S

¬T

Q

¬Q

¬P

R

Molecular Theorem Prover(Abstract Implementation)

P

¬R

S

T

P

¬R

S

T

P

¬R

S

T

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

experimental procedure
Experimental Procedure

Step 1. DNA Sequence Design

Step 2. DNA Synthesis

Step 3. Chemical Reaction in a Test Tube

  • Hybridization
  • Ligation

Step 4. Detection of Solutions

  • PCR
  • Gel Electrophresis

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

sequence design
Sequence Design

¬Q ¬P  R :

5’3’

Q  ¬T  ¬S :

3’ 5’

S : 5’ 3’

T : 3’ 5’

P : 5’ 3’

R : 5’ 3’

¬R : 3’ 5’

CGT ACG TAC GCT GAA CTG CCT TGC GTT GAC TGC GTT CAT TGT ATG

TTC AGC GTA CGT ACG TCA ATT TGC GTC AAT TGG TCG CTA CTG CTT

AAG CAG TAG CGA CCA

ATT GAC GCA AAT TGA

GTC AAC GCA AGG CAG

TGC GTT CAT TGT ATG

CAT ACA ATG AAC GCA

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

reaction in a test tube
Reaction in a Test Tube

Q  ¬T  ¬S

P

T

R  ¬P¬Q

S

¬R

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

hybridization and ligation

Q  ¬T  ¬S

TTC AGC GTA CGT ACG TCA ATT TGC GTC AAT TGG TCG CTA CTG CTT

P

AGT TAA ACG CAG TTA

¬R

GTC AAC GCA AGG CAG

T

AGT TAA ACG CAG TTA

¬R

P

Q  ¬T  ¬S

P

CAT ACA ATG AAC GCA

¬R

T

Q  ¬T  ¬S

ACC AGC GAT GAC GAA

GTC AAC GCA AGG CAG

GTC AAC GCA AGG CAG

TTC AGC GTA CGT ACG TCA ATT TGC GTC AAT TGG TCG CTA CTG CTT

CAT ACA ATG AAC GCA

S

CAT ACA ATG AAC GCA

TTC AGC GTA CGT ACG TCA ATT TGC GTC AAT TGG TCG CTA CTG CTT

GTA TGT TAC TTG CGT CAG TTG CGT TCC GTC AAG TCG CAT GCA TGC

ACC AGC GAT GAC GAA

AGT TAA ACG CAG TTA

GTA TGT TAC TTG CGT CAG TTG CGT TCC GTC AAG TCG CAT GCA TGC

R  ¬P¬Q

T

S

R  ¬P¬Q

ACC AGC GAT GAC GAA

GTA TGT TAC TTG CGT CAG TTG CGT TCC GTC AAG TCG CAT GCA TGC

R  ¬P¬Q

S

Hybridization and Ligation

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

molecular theorem prover47
Results

II. Denaturation

( 95°C 10 min)

IV. Polyacrylamide gel Electrophoresis(20%)

( PAGE )

V. Detection of solution

: 75bp ds DNA

I. MIX

100pmol/each  Total 20 ul

1

2

3

4

6

5

200 bp

III. Annealing

95°C 1 min  15 °C : 1°C down/min

20 bp

Molecular Theorem Prover

20 bp DMA marker (Talara)

Mixture Reaction

  • Experimental Steps

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

the difficulties of dna computer

The difficulties of DNA computer

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

the reality of dna based computation
The Reality of DNA based computation
  • Symmetry makes control difficult.
  • Importance of Non-Watson-Crick Pairing.
  • Importance of hybridization protocols.
  • Importance of concentration and environment.
  • Fidelity of ligation is high, but NOT perfect.
  • DNA molecules breath.

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

the reality of dna based computation50
The Reality of DNA based computation
  • Affinity binding is a filtration process.
  • Complex ligation produces complex products.
  • Restriction enzymes produces partial digestion products.
  • Base stacking is often determining interaction.
  • DNA is a physical chemical system.

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

encoding problems
Encoding Problems
  • Encoding Problems
    • encoding problem is mapping the problem instance onto a set of DNA molecules and molecular biology protocols so that the resulting products contain an answer to instance of the problem
    • prevent errors
    • enable extraction

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

operational problems
Operational Problems
  • It takes TOO long times
    • hybridization/ligation operation over 4 hours
    • In Adleman’s experiments : 7 days!

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

operational problems53
Operational Problems
  • Not Perfect Operation
    • Hybridization Mismatches
    • Extraction Errors
    • Volume and Mass to solve a problem
  • False Negatives
  • False Positives

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

hybridization mismatches
Hybridization Mismatches
  • Reliability : False Positives and Negatives
  • Efficiency

T

C

A

AGG

AGGCTTAGCT

C

C

TCC

TCCAGATCGA

T

G

T

Mismatched Hybridization

Hairpin Hybridization

AGGCTTAGCT

CGAATCGAGC

Shifted Hybridization

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

detection problems
Detection problems
  • NOT exact detection
  • Gel electrophoresis is widely used detection technique.
    • We can not separate the exact solution!

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

slide56

DNA filter

5% error rate

Heterogenous

mix

Single DNA

solution

ATGGA..

ATTGA..

error

Sources of errors

a) Common operation in handling DNA is filtering:

Leads to high output error rate

b) Chain Reaction for DNA amplification.

Rare type of error, but does present a problem

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

slide57

Sources of errors

c) Binding error. Expect binding between complements

C

G

Formation of

double helix

T

A

G

C

A

C

A

T

error causes bulges, missed bonds -- translates into output errors

  • Error resilient computation techniques required:
    • classic error correction with redundant representation
    • careful data encoding - trade off density for resiliency
    • varying environment conditions may improve performance
  • Question: are errors always bad ? In this case, an occasional mutation may lead to improved learning
dna computing applications

DNA Computing Applications

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

applications of biomolecular computing
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
  • Biocomputer
  • Further impact in biology and medicine:
    • Wet biological data bases
    • Processing of DNA labeled with digital data
    • Sequence comparison
    • Fingerprinting

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

dna adder

The Addition Reaction: Primer Extension

5’

3’

Template DNA

Primer DNA

(= 2nd digit)

5’

Polymerase

Primer Extension

DNA Adder
  • [UC Berkely, USA]

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

massively parallel theorem proving

A

A

D

C

C

A

A

B

B

B

B

D

D

C

Massively Parallel Theorem Proving
  • [Warsaw Univ. of Technology, Poland]

IF Premise THEN Conclusion

Premise

Conclusion

breaking des
Breaking DES
  • [Princeton, USA]

B2(0)

BN(0)

B1(0)

S1

SN

S0

…..

B1(1)

B2(1)

BN(1)

DES circuit initial graph

S0l

S0r

B1l(0)

B1r(0)

S1l

S1r

SNl

SNr

B2l(1)

B2r(1)

S0l

S0r

S1l

S1r

SNl

SNr

B1l(0)

B1r(0)

B2l(1)

B2r(1)

S2l

BNr(1)

A path in the graph

future applications

Future Applications

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

interesting possibilities
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.

May be

malignant

or

biohazard

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

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

molecular storage for massively parallel information retrieval
Molecular Storage for Massively Parallel Information Retrieval

Trillions of DNA

Phone book

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

molecular computer on a chip

Microreactor

PCR

Gel Electrophoresis

Bead

Detection

Molecular Computer on a Chip

+

DNA computing

algorithm

MEMS (Microfluidics)

Real DNA computer

biomems the next transistors
BioMEMS – the Next Transistors

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

slide69

Lab-on-a-chip Technology

Integrates sample handling, separation and detection and data analysis for: DNA, RNA and protein solutions using LabChip technology.

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

conclusion
Conclusion
  • 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.

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

research groups
Research Groups
  • MIT, Caltech, Princeton University, Bell Labs
  • EMCC (European Molecular Computing Consortium) is composed of national groups from 11 European countries
  • BioMIP Institute (BioMolecular Information Processing) at the German National Research Center for Information Technology (GMD)
  • Molecular Computer Project (MCP) in Japan
  • Leiden Center for Natural Computation (LCNC)

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

web resources
Web Resources
  • European Molecular Computing Consortium (EMCC): http://www.csc.liv.ac.uk/~emcc/
  • BioMolecular Information Processing (BioMip): http://www.gmd.de/BIOMIP
  • Leiden Center for Natural Computation (LCNC): http://www.wi.leidenuniv.nl/~lcnc/
  • Biomolecular Computation (BMC): http://bmc.cs.duke.edu/
  • DNA Computing and Informatics at Surfaces: http://www.corninfo.chem.wisc.edu/writings/DNAcomputing.html
  • SNU Molecular Evolutionary Computing (MEC) Project:

http://scai.snu.ac.kr/Research/

books
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 Automata-Evolutionary Algorithms-Quantum Computers, Wiley-Vch, 1997.

(C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/