Introduction to DNA Computing

Russell DeatonElec. & Comp. Engr.The University of MemphisMemphis, TN 38152rjdeaton@memphis.edu

Junghuei ChenDepartment of Chem & BiochemUniversity of DelawareNewark, DE 19716Junghuei@udel.edu

What is DNA Computing (DNAC) ?

The use of biological molecules, primarily DNA, DNA analogs, and RNA, for computational purposes.

Why Nucleic Acids?• Density (Adleman, Baum):

– DNA: 1 bit per nm3, 1020 molecules– Video: 1 bit per 1012 nm3

• Efficiency (Adleman)– DNA: 1019 ops / J– Supercomputer: 109 ops / J

• Speed (Adleman):– DNA: 1014 ops per s– Supercomputer: 1012 ops per s

What makes DNAC possible?• Great advances in molecular biology

– PCR (Polymerase Chain Reaction)– DNA Microarrays– New enzymes and proteins– Better understanding of biological molecules

• Ability to produce massive numbers of DNA molecules with specified sequence and size

• DNA molecules interact through template matching reactions

What are the basics from molecular biology that I need to

know to understand DNA computing?

PHYSICAL STRUCTURE OF DNA

Nitrogenous Base

34 Å

MajorGroove

Minor Groove

Central Axis

Sugar-PhosphateBackbone

20 Å5’ C

3’ OH

3’ 0HC 5’

5’

3’

3’

5’

INTER-STRAND HYDROGEN BONDING

Adenine Thymine

to Sugar-PhosphateBackbone

to Sugar-PhosphateBackbone

(+) (-)

(+)(-)

Hydrogen Bond

Guanine Cytosine

to Sugar-PhosphateBackbone

to Sugar-PhosphateBackbone

(-) (+)

(+)(-)

(+)(-)

STRAND HYBRIDIZATION

A B

a b

A B

ab

b

B

a

A

HEAT

COOL

ba

A B

OR

100° C

DNA LIGATION

’ ’

’ ’

Ligase Joins 5' phosphateto 3' hydroxyl

’ ’

RESTRICTION ENDONUCLEASES

EcoRI

HindIII

AluI

HaeIII

- OH 3’

5’ P -

- P 5’

3’ OH -

DNA Polymerase

DNA Sequencing

GEL ELECTROPHORESIS - SIZE SORTING

BufferGel

Electrode

Electrode

Samples

Faster

Slower

ANTIBODY AFFINITY

CACCATGTGAC

GTGGTACACTG B

PMP

+

Anneal

CACCATGTGAC

GTGGTACACTG B+

CACCATGTGAC

GTGGTACACTG B PMP

Bind

Add oligo withBiotin label

Heat and cool

Add Paramagnetic-Streptavidin

Particles

Isolate with MagnetN

S

POLYMERASE CHAIN

REACTION

What is a the typical methodology?

• Encoding: Map problem instance onto set of biological molecules and molecular biology protocols

• Molecular Operations: Let molecules react to form potential solutions

• Extraction/Detection: Use protocols to extract result in molecular form

What is an example?

• “Molecular Computation of Solutions to Combinatorial Problems”

• Adleman, Science, v. 266, p. 1021.

Algorithm

• Generate Random Paths through the graph.

• Keep only those paths that begin with vin and end with vout.

• If graph has n vertices, then keep only those paths that enter exactly n vertices.

• Keep only those paths that enter all the vertices at least once.

• In any paths remain, say “Yes”; otherwise, say “No”

Encoding0

1

2

‘GCATGGCC

‘AGCTTAGG

‘ATGGCATG

CCGGTCGA’

CCGGTACC’

‘GCATGGCCAGCTTAGG CCGGTCGA’

‘GCATGGCCATGGCATG CCGGTACC’

00 21

What are the success stories?

• Self-Assembling Computations Demonstrated (Winfree and Seeman)

• New Approaches and Protocols Developed – Surface-based (Wisconsin-Madison, Dimacs II)– Evolutionary Approaches (Wood and Chen,

Gecco-99, DNA-5)

• How do cells and nature compute? (Kari

and Landweber, Dimacs IV)

Source: http://seemanlab4.chem.nyu.edu/

Source: Winfree, DIMACS IV

Source: http://corninfo.chem.wisc.edu/writings/dnatalk/dna01.html

Source: http://www.princeton.edu/~lfl/washpost.html

What are the challenges?

• Error: Molecular operations are not perfect.

• Reversible and Irreversible Error

• Efficiency: How many molecules contribute?

• Encoding problem in molecules is difficult.

• Scaling to larger problems

• Applications

Mismatches

DNA Word Design

• Design of DNA Sequences that hybridize as planned (that is, minimize mismatches)

• Reliability: False Positives and Negatives

• Efficiency: Hybridizations that Contribute to Solution

• Hybridizations are Templates for Subsequent Enzymatic Steps

DNA Word Design

• Minimum Distance Codes to Prevent Hybridization Error

• Distance Measure– Combinatoric (Hamming)– Energetic (Base Stacking Energy)

• Design DNA Words with Evolutionary Algorithms

• Good Codes Achievable

Code Word

Hybridization

Code Word

Hybridization

Base Stacking

What are the possible applications?

• DNAC and Conventional Computers

• DNAC and Evolutionary Computation

• DNAC and Biotechnology

DNAC and Electronic Computing

• Solution versus solid state

• Individual molecules versus ensembles of charge carriers

• The importance of shape in biological molecules

• Programmability/Evolvability Trade-off (Conrad)

Edna

• Electronic DNA

• Virtual Test Tube for Design and Simulation of DNA Computations

• Molecules as Cellular Automata

• Solve Adleman and Other Problems

• Distributed Edna to Solve Large Problems

• New Paradigm

In Vitro Evolutionary Computation

• Randomness and Uncertainty Inherent in Biomolecular Reactions

• Never Level of Control like EE over Solid State Devices

• Use Nature’s ToolBox: Enzymes, Reaction/Diffusion, Adaptability, and Robustness

• Evolved, Not Designed

DNAC and Biotechnology

• “Computationally Inspired Biotechnology”

• DNA2DNA “killer app”

• Automation of protocols

• DNA Word Design (Gene Expression Chips)

• Exquisite Detection of Biomaterials

• Bio-engineered Materials

What developments can we expect in the near-term (1999)?

• Increased use of molecules other than DNA

• Evolutionary approaches

• Continued impact by advances in molecular biology

• Some impact on molecular biology by DNA computation

• Increased error avoidance and detection

What are the long-term prospects?

• Cross-fertilization among evolutionary computing, DNA computing, molecular biology, and computational biology

• Niche uses of DNA computers for problems that are difficult for electronic computers

Where can I learn more?• Web Sites:

• http://www.wi.leidenuniv.nl/~jdassen/dna.html• http://dope.caltech.edu/winfree/DNA.html• http://www.msci.memphis.edu/~garzonm/bmc.html• (Conrad) http://www.cs.wayne.edu/biolab/index.html

• DIMACS Proceedings: DNA Based Computers I (#27), II (#44), III (#48), IV (Special Issue of Biosystems), V (MIT, June 1999), VI (Leiden, June 2000)• Other: Genetic Programming 1 (Stanford, 1997), Genetic Programming 2 (Wisconsin-Madison, 1998), GECCO-1999,IEEE International Conference on Evolutionary Computation (Indianapolis, 1997)• G. Paun (ed.), Computing with Biomolecules: Theory and Experiment, Springer-Verlag, Singapore 1998.• “DNA Computing: A Review,” Fundamenta Informaticae, vol. 35, pp. 231-245.•M. H. Garzon and R. J. Deaton, “Biomolecular Computing and Programming,” IEEE Transactions on Evolutionary Computation, vol. 3, pp. 236-250, 1999.