Z Ezziane- DNA computing: applications and challenges

8/3/2019 Z Ezziane- DNA computing: applications and challenges

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INSTITUTE OF PHYSICS PUBLISHING NANOTECHNOLOGY

Nanotechnology 17 (2006) R27R39 doi:10.1088/0957-4484/17/2/R01

TOPICAL REVIEW

DNA computing: applications andchallenges

Z Ezziane

Dubai University College, College of Information Technology, PO Box 14143, Dubai, UAE,

Middle East

Received 17 August 2005Published 21 December 2005

Online at stacks.iop.org/Nano/17/R27

AbstractDNA computing is a discipline that aims at harnessing individual moleculesat the nanoscopic level for computational purposes. Computation with DNAmolecules possesses an inherent interest for researchers in computers andbiology. Given its vast parallelism and high-density storage, DNAcomputing approaches are employed to solve many combinatorial problems.However, the exponential scaling of the solution space prevents applying anexhaustive search method to problem instances of realistic size, andtherefore artificial intelligence models are used in designing methods thatare more efficient. DNA has also been explored as an excellent material anda fundamental building block for building large-scale nanostructures,constructing individual nanomechanical devices, and performingcomputations. Molecular-scale autonomous programmable computers aredemonstrated allowing both input and output information to be in molecularform. This paper presents a review of recent advances in DNA computingand presents major achievements and challenges for researchers in theforeseeable future.

1. Introduction

DNA (deoxyribonucleic acid) computing research was

inspired by the similarity between the wayDNA works and the

operation of a theoretical device known as a Turing machine.

Turing machines process information and store them as asequence, or list of symbols, which is very naturally related

to the way biological machinery works.

Biomolecular computing, where computations are per-

formed by biomolecules, ischallenging traditional approaches

to computation both theoretically and technologically. The

idea that molecular systems can perform computations is not

new and was indeed more natural in the pre-transistor age.

Most computer scientists know of von Neumanns discussions

of self-reproducing automata in the late 1940s, some of which

were framed in molecular terms (McCaskill 2000).

Important was the idea, appearing less natural in the

current age of dichotomy between hardware and software,

that the computations of a device can alter the device itself.This vision is natural at the scale of molecular reactions,

although it may appear as a fantasy to those running huge chip

production facilities. Alan Turing also looked beyond purely

symbolic processing to natural bootstrapping mechanisms

in his work on self-structuring in molecular and biological

systems (McCaskill 2000).

In biology, the idea of molecular information processing

took hold starting from the unravelling of the genetic codeand translation machinery and extended to genetic regulation,

cellular signalling, protein trafficking, morphogenesis and

evolution, which all have progressed independently of the

development in the neurosciences. The essential role of

information processing in evolution and the ability to address

these issueson laboratory timescales at themolecular levelwas

first addressed by Adlemans key experiment (Adleman 1994),

which demonstrated that the tools of laboratory molecular

biology could be used to program computations with DNA

in vitro. DNA computing approaches can be performed either

in vitro (purely chemical) or in vivo (i.e. inside cellular life

forms). The huge information storage capacity of DNA

and the low energy dissipation of DNA processing led to anexplosion of interest in massively parallel DNA computing.

For serious proponents of the field however, there never was

0957-4484/06/020027+13$30.00 2006 IOP Publishing Ltd Printed in the UK R27
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5- A C C T G T T T G C -33- T G G A C A T A C G -5

Figure 1. Example of a DNA molecule.

a question of brute search with DNA solving the problem ofan exponential growth in the number of alternative solutions

indefinitely. Artificial intelligence methods are used to address

the combinatorial issue in DNA computing (Impagliazzo et al

1998, Sakamoto et al 1999), which will be discussed later in

this review.

Quantum computing is usually compared with DNA

computing. Quantum computing involves high physical

technology for the isolationof mixed quantum statesnecessary

to implement efficient computations solving combinatorially

complex problems such as factorization. DNA computing

operates in natural noisyenvironments, suchas a glassof water.

It involves an evolvable platform for computation in which the

computer construction machinery itself is embedded. SinceDNA computing is linked to molecular construction such

as nanomechanical devices and other nanoscale structures,

the computations may eventually also be employed to build

three-dimensional self-organizing partially electronic or more

remotely even quantum computers (McCaskill 2000).

2. The structure of DNA

DNA is the major example of a biological molecule that stores

information and can be manipulated, via enzymes and nucleic

acid interactions, to retrieve information. Similarly, as a string

of binary data is encoded with zeros and ones, a strand of DNA

is encoded withfour bases (known as nucleotides), representedbythe letters A, T, C, andG. Each strand, accordingto chemical

convention, has a 5 and a 3 end; hence, any single strand

has a natural orientation. Figure 1 presents a DNA molecule

composed of ten pairs of nucleotides. Bonding occurs by the

pairwise attraction of bases; A bonds with T and G bonds

with C. The pairs (A, T) and (G, C) are therefore known as

complementary base pairs.

DNA computing relies on developing algorithms that

solve problems using the encoded information in the sequence

of nucleotides that make up DNAs double helix and then

breaking and making new bonds between them to reach the

answer.

The nucleotides are spaced every 0.35 nm along the DNAmolecule, giving a DNA a remarkable data density estimated

as one bit per cubic nanometre, and potentially exabytes (1018)

amounts of information ina gram of DNA (Chen etal 2004). In

two dimensions, assuming one base per square nanometre, the

data density is over one million Gbits per square inch, whereas

the data density of a typical high performance hard drive is

about 7 Gbits per square inch (Ryu 2000). DNA computing

is also massively parallel and can reach approximately 1020

operations s1 compared to existing teraflop supercomputers.

Another important property of DNAis its double-stranded

nature. The bases A and T, and C and G, can bind

together, forming base pairs. Therefore, every DNA sequence

has a natural complement. For example, sequence S isAATTCGCGT, its complement, S, is TTAAGCGCA. Both

S and S will hybridize to form double-stranded DNA. This

A C C T G G A A T TC C T T A A A T A C G

Figure 2. A DNA molecule with sticky ends.

complementarity can be used for error correction. If the erroroccurs in one of the strands of double-stranded DNA, repairenzymes can restore the proper DNA sequence by using the

complement strand as a reference. In DNA replication, there

is one error for every 109 copied bases whereas hard drives

have one error for every 1013 for ReedSolomon correction(Ryu 2000).

From the basic principle of base pair complementarity,

DNA contains two elements crucial to any computer: (1) aprocessing unit in the form of enzymes that denature, replicate

and anneal DNA, which are operations capable of cutting,copying, and pasting; and (2) a storage unit encoded in DNA

strings (Thaker2004). Hence,when enzymes workonmultiple

DNA at the same time DNA computing becomes massivelyparallel and ultimately very powerful. The power in DNA

computing comes from the memory capacity and parallel

processing. For example, in bacteria, DNA can be replicated

at a rate of about 500 base pairs a second, which is 10 timesfaster than human cells. This represents about 1000 bits s1,

but when many copies of the replication enzymes are to work

on DNA in parallel, the rate of DNA strands will increase

exponentially (2n after n iterations). Subsequently, after 30

iterations it increases to 1 Tbits s1.

2.1. Matching DNA sticky ends

Restriction enzymes catalyse the cutting of both strands of a

DNA molecule at very specific DNA base sequences, called

recognition sites. Recognition sites are typically 48 DNAbase pairs long. Figure 2 shows a DNA molecule in which

its four nucleotides in the left end and five in the right end

are not paired with nucleotides from the opposite strand. This

molecule has sticky ends.

There are over 100 different restriction enzymes, eachof which cuts at its specific recognition site(s). A restriction

enzymecuts tiny stickyends of DNA that will match andattachto stickyends of any other DNA thathas been cut with the same

enzyme. DNA ligase joins the matching sticky ends of the

DNA pieces from different sources that have been cut by the

same restriction enzyme. Many restriction enzymes work by

finding palindrome sections of DNA (regions where the orderof nucleotides at one end is the reverse of the sequence at the

opposite end).The process of joining the matching sticky DNA ends is

used extensively in the field of DNA technology to producesubstances such as insulin and interferon, and to splice genes

that alter a cell or organism from its original DNA for some

benefit. Forexample, inagriculturewe haveused gene splicing

to delay the ripening process of tomatoes, to make more

nutritious corn, to make rice that contains carotenes and toproduce plants with natural pesticides.

3. DNA computers

A DNA computer is a collection of specially selected DNA

strands whose combinations will result in the solution to

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UniversalComputer

UniversalConstructor

Figure 3. The von Neumann architecture for a self-replicating

system.

some problem, and a nanocomputer is considered as a

machine that uses DNA to store information and perform

complex calculations. Benenson et al (2003) observed the

unique properties of DNA being a fundamental building block

in the fields of supramolecular chemistry, nanotechnology,

nanocircuits, molecular switches, molecular devices, and

molecular computing. Many designs for miniature computers

aimed at harnessing the massive storage capacity of DNA have

been proposed over the years. Earlier schemes have relied on a

molecule known as ATP, which is a common source of energy

for cellular reactions, as a fuel source. However, Benensonet al (2003) designed a new model where a DNA molecule

provides both the initial data and sufficient energy to complete

the computation.

Both models of the molecular computer are so-called

automatons. Given an input string comprised of two different

states, an automaton uses predetermined rules to arrive at an

output value that answers a particular question. Then a specific

enzyme acts as the computers hardware by cutting a piece of

theinputmoleculeand releasingthe energy storedin thebonds.

This heat energy then powers the next computation (Graham

2003).

Positional control combined with appropriate molecular

toolsshouldenable researchers andpractitioners to build a trulyoverwhelming range of molecular structures. Subsequently,

one of the outcomes will be building a general-purpose

programmable device, which is able to make copies of itself.

von Neumann carried out a detailed analysis of self-replicating

systems in a theoretical cellular automatamodel. In this model,

as depicted in figure 3, he used a universal computer forcontrol

and a universal constructor to build more automata. The

universal constructor was a robotic arm that, under computer

control, could move in two dimensions and alter the state of

the cell at the tip of its arm. By sweeping systematically back

and forth, the arm could eventually build any structure that

the computer instructed it to. In his three-dimensional model,

von Neumann retained the idea of a positional device and acomputer to control it.

The architecture for Drexlers assembler, as depicted in

figure 4, is a specialization of the more general architecture

proposed by von Neumann. Similarly, there is a computer

and constructor. However, the computer has shrunk to

a molecular computer while the constructor combines two

features: a robotic positional device and a well-defined

set of chemical operations that take place at the tip

of the positional device (http://www.zyvex.com/nanotech/

MITtecRvwSmlWrld/article.html).

The complexity of a self-replicating system must be

reasonable and acceptable. In addition, the complexity of

an assembler, in terms of bytes, should not be beyond thecomplexity that can be dealt with by todays engineering

capabilities. As indicated in table1, the primary observation to

Molecular Computer Molecular Constructor

Molecular Positional Capability Tip Chemistry

Figure 4. Drexlers architecture for an assembler.

Table 1. Complexity of self-replicating systems (Megabytes).

von Neumanns universal constructor About 0.63Internet Worm About 0.63Mycoplasma genitalia 0.14E. coli 1.16Drexlers assembler 12.5Human 800NASA Lunar Manufacturing Facility 13 000

be drawn from these data is that simpler designs and proposals

for self-replicating systems both exist and are well within

current design capabilities. The engineering effort required

to design systems of such complexity will be significant, but

shouldnot be greater than thecomplexityinvolved in thedesign

of such existing systems as computers.

Self-replication is used as a means to an end, not as

an end in itself. A system able to make copies of itself

but unable to make much of anything else would not be

very useful. The purpose of self-replication in the context

of manufacturing is to permit the low-cost replication of a

flexible and programmable manufacturing system. Hence,

the objective is to build a system that can be reprogrammed

to make a very wide range of molecularly precise structures

(http://www.zyvex.com/nanotech/selfRep.html).

3.1. Self-assembling nanostructures with DNA

DNA molecular structures and intermolecular interactions

are particularly known to be amenable to the design and

synthesis of complex molecular objects. Winfree et al (1998)

used a molecular self-assembly approach to the fabrication

of objects specified with nanometre precision. Their results

demonstrated the potential of using DNA to create self-

assembling periodic nanostructures, and therefore leading the

way to nanotechnology.A few years later, Mao et al (2000) reported a one-

dimensional algorithm self-assembly of DNA triple-crossover

molecules that can be used to execute four steps of a logical

(cumulative XOR) operation on a string of binary bits. Their

results suggest that computation by self-assembly may be

scalable. Figure 5 depicts a simplified version for the

implementation of the XOR cellular automaton using the

Sierpinski rules (Rothemund et al 2004). Figure 4 has four

horizontal parts: (A), (B), (C), (D), and (E). On the left of (A),

the two timesteps ofthe execution drawn are shown as a space

time history and cells are updated synchronously according

to XOR function. The right side of (A) shows the Sierpinski

triangle. Part (B) translates thespacetime history into a tiling,in which for each possible input pair a tile T-xy is generated

so that it bears the inputs represented as shapes on the lower

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t= 0

t= 1 . . .. . . 0 1 1 0 0

0 0 1 0 0 0

A

Bz

x y

outputs

inputs x

zz

y

z = x xor y

T-xy

T-xy

C0

0

0

0

0

1

0

1

T-00 T-11 T-01

1

0

1

1

T-10

1

1

1

0

Initial conditions for the computation are provided by nucleatingstructures (0s and 1s)

Error-free growth results in the Sierpinski pattern

Error-prone growth including mismatch errors

D

E

Figure 5. The XOR cellular automaton implementation using tile-based self-assembly.

(This figure is in colour only in the electronic version)

half of each side and the output as shapes duplicated on the

top half of each side. Part (C) represents the four Sierpinski

rule tiles; T-00, T-11, T-01, and T-10, represent the four entries

of the truth table for XOR: 0 XOR 0 = 0, 1 XOR 1 = 0, 0

XOR 1 = 1, and 1 XOR 0 = 1. Part (D) is concerned with

the growth results in the Sierpinski pattern, and part (E) uses

symbols to indicate mismatch errors.

DNA nanostructures provide a programmable methodol-

ogy for bottom-up nanoscale construction of patterned struc-

tures, utilizing macromolecular building blocks called DNA

tiles based on branched DNA. These tiles have sticky ends that

match the sticky ends of other DNA tiles, facilitating further

assembly into larger structures known as DNA tiling lattices.

In principle, DNA tiling assemblies can be made to form anycomputable two- or three-dimensional pattern, however com-

plex, with the appropriate choice of the tiles component DNA

(Reifet al 2005).

One potential approach is to use patterned DNA as

scaffoldsor templatesfororganizing andpositioningmolecular

electronics and other components such as molecular sensors

with precision and specificity. The programmability lets

this scaffolding have the patterning required for fabricating

complex devices made of these components. Sung etal (2004)

discussed the fabrication and characterization of an original

class of nanostructures based on the DNA scaffolds. They

reported on the self-assembly of one- and two-dimensional

DNA scaffolds, which served as templates for the targeteddeposition of ordered nanoparticles and molecular arrays.

Turberfield (2003) proposed to use self-assembling DNA

nanostructures as scaffolds for constructing and positioning

molecular-scale electronic devices and wires.

A principal challenge in DNA tiling self-assemblies is the

control of assembly errors. This is predominantly relevant

to computational self-assemblies, which, with complex

patterning at the molecular scale, are prone to a quite high rate

of error, ranging from approximately between 0.5% and 5%

(Reifet al 2005). The limitation and/or elimination of these

errors in self-assembly represent the most important major

challenge to nanostructure self-assembly.

3.2. DNA nanomachines

DNA has been explored as an excellent material for

building large-scale nanostructures, constructing individualnanomechanical devices, and performing computations

(Seeman 2003). A variety of DNA nanomechanical devices

have been previously constructed that demonstrate motions

such as open/close (Yurke etal 2000, Simmel and Yurke 2001,

2002, Liu and Balasubramanian 2003), extension/contraction

(Li and Tan 2002, Alberti and Mergny 2003, Feng et al 2003),

andmotors/rotation(Maoetal 1999, Yan etal 2002, Niemeyer

and Adler 2002), mediated by external environmental changes

such as the addition and removal of DNA fuel strands (Li and

Tan 2002, Alberti and Mergny 2003, Simmel and Yurke 2001,

2002, Yan et al 2002, Yurke et al 2000) or the change of

ionic composition of the solution (Mao et al 1999, Liu and

Balasubramanian 2003).The DNA walker could ultimately be used to carry

out computations and to precisely transport nanoparticles of

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material. The walker can be programmed in several ways

in this direction. For example, information can be encoded

in the walker fragments as well as in the track so that,while performing motion, the walker simultaneously carries

out computation. Yin et al (2005a), (2005b) designed an

autonomous DNA walking device in which a walker movesalong a linear track unidirectionally. Sherman and Seeman(2004) have constructed a DNA walking device controlled by

DNA fuel strands.

Reif(2003) designedanautonomous DNA walking deviceand an autonomous DNA rolling device that move in a random

bidirectional fashion along DNA tracks. Shin and Pierce(2004) designed the DNA walker for molecular transport.

Recently, Yin et al (2005a), (2005b) encoded computationalpower into a DNA walking device embedded in a DNA lattice

and therefore accomplished the design for an autonomous

nanomechanical device capable of universal computation and

translational motion.

Implementing controllable molecular nanomachinesmade of DNA is one of the objectives of DNA computing

and DNAnanotechnology (Takahashi etal 2005). ControllingDNA machines have been implemented using different

methods: (1) DNA strands that hybridize with target machines

and drive their state transition, (2) DNA strands can also beused as catalysts for the formation of double helices in such

nanomachines, and (3) BZ transition of DNA capable of

switching the confrontation of the DNA motor (Mao et al1999).

Various approaches have implemented the first method.

Yurke etal (2000) reported theconstruction of a DNA machine

in which DNA is used not only as a structural material, but

also as fuel. Simmel and Yurke (2001) described a DNA-

basedmolecular machine, whichhas twomovablearmsthatarepushed apart when a strand of DNA, the fuel strand, hybridizes

with a single-stranded region of the molecular machine. Yan

et al (2002) implemented a robust DNA mechanical device

controlled by hybridization topology.On the other hand, implementations of the second method

have also been reported. Seelig (2004) presented experimental

results on the control of the decay rates of a metastableDNA fuel. They also discussed how the fuel complex

can serve as the basic ingredient for a DNA hybridizationcatalyst. They also proposed a method for implementing

arbitrary digital logic circuits. Turberfield and Mitchel

(2003) described kinetic control of DNA hybridization, which

has the potential to increase the flexibility and reliabilityof DNA self-assembly through inhibiting the hybridizationof complementary oligonucleotides. The proposed DNA

catalysts were shown to be effective in promoting thehybridization and forusing DNA as a fuel to drive free-running

artificial molecular machines.

4. DNA computing

DNA computing is a novel and fascinating development at theinterface of computer science and molecular biology. It has

emerged in recent years, not simply as an exciting technologyfor information processing, butalso as a catalyst for knowledge

transfer between information processing, nanotechnology, andbiology. This area of research has the potential to change ourunderstanding of the theory and practice of computing.

4.1. Biomolecular computing

Biomolecular computers are molecular-scale, programmable,

autonomous computing machines in which the input, output,

software, and hardware are made of biological molecules

(Benenson and Shapiro 2004). Biomolecular computers hold

the promise of direct computational analysis of biological

information in its native biomolecular form, avoiding its

conversion into an electronic representation (Adar et al 2004).

This has led to pursing autonomous, programmable computers

which are considered as finite automata (McAdams and Arkin

1997).

An automaton can be stochastic, namely has two or more

competing transitions for each state-symbol combination,

each with a prescribed probability. A stochastic automaton

is useful for processing uncertain information, like most

biological information. Because of the stochastic nature of

biomolecular systems, a stochastic biomolecular computer

would be morefavourable for analysing biological information

than a deterministic one (McAdams and Arkin 1997).Stochastic molecular automata have been constructed in

which stochastic choice is realized by means of competition

between alternative paths, and choice probabilities were

programmed by the relative molar concentrations of the

software molecules coding for the alternatives. This approach

was used in the construction of a molecular computer capable

of probabilistic logical analysis of disease-related molecular

indicators (Adar et al 2004).

Benenson et al (2001) described a programmable finite

automaton comprising DNA and DNA-manipulating enzymes

that solves computational problems autonomously. The

automatons hardware consists of a restriction nuclease and

ligase, the software and input are encoded by double-stranded DNA, and programming amounts to choosing

appropriate software molecules. Their experiments used 1012

automata, which were sharing identical software, and running

independently and in parallel on inputs in 120 l solution at

room temperature at a combined rate of 109 transitions s1

with a transition fidelity greater than 99.8%, consuming less

than 1010 W.

It has also been demonstrated that a single DNA molecule

can provide both the input data and all of the necessary fuel

for a molecular automaton (Benenson et al 2003). Those

experiments showed that 3 1012 automata l1 performing

6.6 1010 transitions s1 l1 with transition fidelity of

99.9% dissipating about 5 10

9 W l

1 as heat at ambienttemperature.

An autonomous biomolecular computer was described

recently (Benenson et al 2004) which analyses the levels of

messenger RNA (mRNA) species, and in response generates a

molecule capable of affecting levels of gene expression. The

designed biomolecule computer works at a concentration of

close to 1012 computers l1. The modularity of their design

facilities improved each biomolecular computer component

independently. They demonstrated how computer regulation

by other biological molecules such as proteins, the output of

other biologically active molecules such as RNA interference,

can all be explored concurrently and independently.

Progress in the development of molecular computersmay lead to a Doctor in Cell which is represented by

a biomolecular computer that operates inside the living

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organism, for example the human body, programmed with

medical information to diagnose potential diseases andproduce therequireddrugs in situ. This will ultimately lead to a

device capable of processing DNA inside the human body and

finding abnormalities and creating healing drugs. However,

major changes will be needed for the molecular computer tooperate in vivo (Shapiro et al 2004).

Shapiro Lab is renowned for the creation of biomolecular

computingdevices, whichareso tiny that more than a trillion fit

into one drop of water. These manufactured devices are made

entirely of DNA and other biological molecules. A recent

version was programmed by Shapiro and his research team to

identify signs of specific cancers in a test tube, to diagnose

the type of cancer and to release drug molecules in response.

Though cancer-detecting computers are still in the very earlystages, and can thus far only function in test tubes, Shapiro

and his research team envision future biomolecular devices

that may be injected directly into the human body to detect

and prevent or cure disease.At the Shapiro Lab, their recent research mainly deals

with the aspect of energy consumption by a computer. They

were able to constructa molecularcomputer whose sole energy

source is its input, a combination that is unthinkable in the

realm of electronic computers. This energy is extracted as the

input data molecule is destroyed during computation (http://

www.wisdom.weizmann.ac.il/udi/).

Recently, they initiated the BioSPI project which is

concerned with developing predictive models for molecular

and biochemical processes. Such processes, carried out by

networks of proteins, mediate the interaction of cells with their

environment and are responsible for most of the information

processing inside cells. To this end, they developed a

new computer system, called BioSPI, for representation and

simulation of biochemical networks (Shapiro et al 2002).

4.2. Solving problems using DNA computing

4.2.1. Finite state problems. To compete with silicon,

it is important to develop the capability of biomolecular

computation to quickly execute basic operations, such as

arithmetic and Boolean operations, that are executed in single

steps by conventional machines. In addition, these basic

operations should be executable in massively parallel fashion(Reif 1998). Guarnieri and Bancroft (1999) developed a

DNA-based addition algorithm employing successive primer

extension reactions to implement the carries and the Booleanlogic required in binary addition (similar methods can be used

for subtraction). Guarnieri, Fliss, and Bancroft prototyped

(Guarnieri et al 1996) the first biomolecular computation

addition operations (on single bits) in recombinant DNA.

They presented the development of a DNA-based algorithm

for addition. The DNA representation of two non-negative

binary numbers was presented in a form permitting a chain of

primer extension reactions to carry out the addition operation.

They demonstrated the feasibility of this algorithm through

executing biochemically a simple example. However, itsuffered from some limitations: (1) only two numbers were

added, so it did not take advantage of the massive parallel

processing capabilities of biomolecular computation; and (2)the outputs were encoded distinctly from the inputs, hence it

did not allow for repetitive operations.

Subsequent proposed methods (Orlian et al 1998, Leete

et al 1997, Gupta et al 1997) for basic operations such as

arithmetic (addition and subtraction) allow chaining of the

output of these operations into the inputs to supplementary

operations, and to allow operations to be executed in massive

parallel fashion. Rubin et al (1997) presented an experimentaldemonstration of a biomolecular computation method for

chained integer arithmetic.

4.2.2. Combinatorial problems. DNA computing methods

were employed in complex computational problems such as

the Hamilton path problem (HPP) (Adleman 1994), maximal

clique problem (Ouyang et al 1997), satisfiability problem

(SAT) (Liu et al 2000), and chess problems (Faulhammer

et al 2000). The advantage of these approaches is the huge

parallelism inherent in DNA-based computing, which has the

potential to yield vast speedups over conventional electronic-

based computers for such search problems.

The computational problem considered by Adleman(1994) was a simple instant of the directed travelling salesmen

problem (TSP) also called Hamilton path problem (HPP).

The technique used for solving the problem was a new

technological paradigm, termed DNA computing. Adlemans

experiment represents a landmark demonstration of data

processing and communication on the level of biological

molecules. It was the first DNA computer set up to solve the

TSP. Thisproblem usesthescenario of a door-to-doorsalesman

who must visit several connected cities without going through

any city twice. To solve this problem using DNA, the first step

is to assign a genetic sequence to each city. For example, the

city of Los Angeles might be coded GCACAGT. If two cities

connect, then the connecting genetic sequence is assigned thefirst three letters of one city and the last three letters of the

other. For example, if Los Angeles connected to New York,

the first three letters of Los Angeles (GCA) would connect to

the last three letters of New York (CGT).

The TSP seems a simple puzzle; however, the most

advanced supercomputers would take years to calculate the

optimal route for 50 cities (Parker 2003). Adleman solved

the problem for seven cities within a second, using DNA

molecules in a standard reaction tube. He represented each of

the seven cities as separate, single-stranded DNA molecules,

20 nucleotides long, and all possible paths between cities

as DNA molecules composed of the last ten nucleotides of

the departure city and the first ten nucleotides of the arrivalcity. Mixing the DNA strands with DNA ligase and adenosine

triphosphate (ATP) resulted in the generation of all possible

random paths through the cities. However, the majority of

these paths were not applicable to the situation because they

were either too long or too short, or they did not start or

finish in the right city. Adleman then filtered out all the paths

that neither started nor ended with the correct molecule and

those that did not have the correct length and composition.

Any remaining DNA molecules represented a solution to the

problem.

The DNA computer provides enormous parallelism in one

fiftieth of a teaspoon of solution, approximately 1014 DNA

representing flight numbers were simultaneously concatenatedin about one second. The Adleman approach to the HPP is

shown in figure 6. An instance of the HPP which is solved

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Start

Generate strands encoding random paths

Monitor the quantities of DNA generated for thespecific graph

StrandencodesHPP?>

Remove strands that do not encode the HPP

Keep only the potential solutions

Discard the strandsNo

Identify uniquelythe HP solution

Figure 6. Adlemans approach to HPP.

2

3

45

6

1

Figure 7. Instance of the HPP solved by Adleman.

by Adleman is depicted in figure 7, with the Hamiltonian path

(HP) highlighted by a dashed line.

The DNA sequences were set to replicate and create

trillions of new sequences based on the initial input sequences

in a matter of seconds (DNA hybridization). The theory

holds that the solution to the problem was one of the new

sequence strands. By process of elimination, the correct and

final solution would be found. Based on Adlemans method,

the amount of DNA scales exponentially, for example, solving

a 200-city TSP would take probably an amount of DNA

that weighed more than the earth. The error rate for eachoperation is another hurdle for DNA computing as the number

of iterations increases (Ryu 2000).Lipton (1995) argued that all NP (non-deterministic

polynomial time) problems could be efficiently reduced to the

HPP. He also demonstrated how DNA computing solvesa two-variable SAT problem. Lipton (1995) proposed a solution

to the SAT. Figure 8 depicts the approach followed in orderto solve the SAT problem. An initial set S contains many

strings, each encoding a single n-bit sequence. All possible

n-bit sequences are represented in S. An instance, I, of SAT

consists of a set of clauses. The problem is to assign a Boolean

value to a variable in Wsuch that at least one variable in each

clause has the value true. If this is the case then I is satisfiable.Sakamoto et al (1999) showed that many NP-complete

problems can be solved by a single series of successive

Y

Start

Letj = 1, w and x represent literals

wi=xj ?

Generate all possible n-bit strings in S

Extract from S strings

encodings wi = 1Extract from Sstrings encoding wi= 0

Increment i

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Topical Review

computing; however, apparently, there has been a lack of

progress in solving NP-problems since 2000.

4.3. Classes of DNA computing

Essentially, three classes of DNA computing arenow apparent:(1) intramolecular, (2) intermolecular, and (3) supramolecular.

The Japanese Project lead by Hagiya (Takahashi et al 2005)

focuses on intramolecular DNA computing, constructing

programmable state machines in single DNA molecules,

which operate by means of intramolecular conformational

transitions. Intermolecular DNA computing, of which

Adlemans experiment is an example, focusing on the

hybridization between different DNAmolecules as a basicstep

of computations. Finally, supramolecular DNA computing, as

pioneered by Winfree (2003), harnesses the process of self-

assembly of rigid DNA molecules with different sequences to

perform computations.

4.3.1. Example of intramolecular DNA computing.

Sakamoto et al (1999) described one example of intramolecu-

lar implementationand namedit successive localized polymer-

ization, and it is described by a single-stranded DNA molecule

of the form stopper state1 state1 stopper state2 state2

stopper staten staten , where in each pair (statei statei ) of

states, statei denotes the state before a transition, and state ithe state after the transition. Each state is represented by an

appropriate number of bases, called a state sequence. This

process of state transitions can be repeated in a single tube by

a simple thermal program consisting of thermal cycles for de-

naturation, annealing, and polymerization. The state machine

DNA is assumed to form a hairpin, and transitions occur in anintramolecular manner rather than intermolecular ones.

This approach might enhance the power of Adlemans

approach to DNA computing (Adleman 1994, Lipton 1995).

For solving instances of NP-complete problems, they first

generate the space of candidate solutions in a tube, where each

candidate is represented by a DNA molecule. Hybridization

and ligation are employed for the generation of the candidate

space; recently, the technique of parallel overlap assembly has

also been used (Ouyang et al 1997). The candidate space is

then explored by a number of laboratory steps that together

implement the condition for a candidate to be a real solution.

This intramolecular method can be employed in this

second step of exploring the candidate space and extractingthe real solutions. NP-complete problems are solved by a

single series of successive state transitions as described above.

Since a series of state transitions can be considered as one big

step, this means that the number of laboratory steps needed to

explore the candidate space is constant, i.e. O(1).

4.3.2. Example of supramolecular DNA computing.

Supramolecular assembly is the creating of molecular

assemblies that are beyond the scale of one molecule. The

self-assembly of smallmolecular building blocks programmed

to form larger, nanometre-sized elements is an important goal

of molecular nanotechnology. This approach is motivated by

the magnificent examples occurring in nature: for instance, thesupramolecular complex of the E. coli ribosome consisting of

52 protein and three RNA molecules.

Innovation and application of supramolecular assemblies

have reached impressive new heights. For example,

organizations involving nucleic acids have been used for drugs

or DNA delivery, and can also be efficient as sensors for

detection purposes.

The interactions of various low-molecular weightsubstances with DNA are naturally relevant mechanisms in

the cellular cycle and so also used in medicinal treatment

(Bischoff and Hoffmann 2002). Depending on the particular

drug structure, DNA-binding modes, like groove-binding,

intercalating and/or stacking, give rise to supramolecularassemblies of the polynucleotides, as well as influence the

DNAprotein binding.

5. Intelligent systems based on DNA computing

5.1. Smart DNA chips

A gene expression experiment with a single DNA chip can

provide a visual display of how thousands of genes are

expressed simultaneously and a huge amount of information

on the genes. This field has a critical implication to vital

pathogenetic applications such as drug design and disease

classification. In order to capitalize the abundance of new

information made available by DNA chips, a key challenge

remains of how to design and develop intelligent machine

learning techniques so as to effectively explore such a vastamount of information.

The problem of over fitting is a leading concern with

machine learning approaches to DNA chip data. These

medical data are characterized by class imbalance, non-linear

response, highnoise, andlarge numbers of attributes. Pomeroy

et al (2002) published DNA chip data for 60 cancer patients.Their attempts to model the data using unsupervised learning

techniques were unsuccessful at predicting patient survival;

however, they claim statistically significant success using

nearest neighbour and other supervised learning techniques.Li et al (2001) obtained good results using three nearest

neighbours after selecting genes with a multi-run evolutionary

approach on similarly sized DNA expression data.

Intelligent DNA chips have been applied to the prediction

and diagnosis of cancer, so that it expectedly helps us to

exactly predict and diagnose cancer. To precisely classify

cancer Cho and Won (2003) have to select genes related to

cancer because extracted genes from DNA chips have many

noises. This approach explored many features and classifiersusing three benchmark datasets to systematically evaluate the

performances of the feature selection methods and machine

learning classifiers such as k-nearest neighbour, support vector

machine. Kung and Mak (2005) also studied intelligent DNA

chips and showed that machine learning techniques offers a

viable approach to identifying and classifying biologically

relevant groups in genes and conditions.

The enormous width of DNA gene chip data makes

over fitting an ever present danger, particularly with powerful

machine learning approaches. Langdon and Buxton (2004)

used genetic programming in combination with leave one out

cross validation and a principled objective function to evolve

many non-linear functions of gene expression values. Theapproach was to whittle down the thousands of data attributes

(gene expression measurements) into a few predictive ones.

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Intelligent DNA memory has been designed by Chen et al

(2004) as an attempt to capture global information about a

population of an organisms, or wholegenomegeneexpressions

under certain conditions. Furthermore, the DNA memory

incorporates intelligent processing and reasoning capabilities

into the test tube. After the data gathering and analysis stageis complete, the high storage capacity and parallelism of DNA

are used to draw inferences on the entire in vitro knowledge

base.

Sakakibara and Suyama (2000) proposed DNA chips

with logical operations called intelligent DNA chips. They

combined the DNA-computing method for representing and

evaluating Boolean functions with the DNA Coded Number

(DCN) method, and implemented DNA chips with logical

operations executable. The developed DNA chips are

consideredintelligent because theDNAchips notonlydetected

gene expression but also found logical formulae of gene

expressions. This intelligent DNA would be able to provide

logical inference for such diagnoses based on detected geneexpression patterns.

5.2. Applying artificial intelligence methods in DNA

computing

Since Adlemans solution to the HPP (Adleman 1994), DNA

and RNA solutions of some NP-complete problems, such

as the 3-SAT problem (Braich et al 2002), the maximal

clique problem (Ouyang et al 1997), and the knight problem

(Faulhammer et al 2000) were proposed. The power of

parallel, high-density computation by molecules in solution

allows DNA computers to solve hard computational problems

such as NP-complete problems in polynomial increasing time,while a conventional Turing machine requires exponentially

increasing time (Impagliazzo etal 1998, Sakamoto etal 1999).

However, all the current DNA computing strategies

are based on enumerating all candidate solutions, and then

using selection processes to eliminate incorrect DNA. This

algorithm requires that thesize of the initial data pool increases

exponentially with the number of variables in the calculation.

For example, to calculate a DNA solution of an NP-complete

problem, the number of molecules in the solution increases

exponentially with respect to the problem size. As the

problem size keeps increasing, the brute-force method will

be infeasible. Therefore, the design of artificial intelligence

techniques in DNA computing will serve to break the barrierof this brute-force method and get a final solution from a very

small initial data pool, avoiding enumerating all candidate

solutions.

5.2.1. Evolutionary and genetic algorithms. Evolution is

a concept of obtaining adaptation through the interplay of

selection and diversity. The tendency of evolving populations

to minimize the sampling of large, low-fitness individuals

suggests that a DNA-based evolutionary approach might be

effective for an exhaustive search. Of all evolutionary inspired

approaches, genetic algorithms (GAs) seem particularly suited

to implementation using DNA. This is because genetic

algorithms are generally based on manipulating populations ofbit strings using both crossover and mutation operators (Chen

et al 1999).

The combination of the massive parallelism and high

storage density inherent in DNA computing with the direct

search capability of GAs represent major advantages for

DNA-GA approaches. The GA is one of the possible

ways to break the limit of the brute-force method in DNA

computing (Yuan and Chen 2004). One gram of a single-stranded DNA is approximately 1.8 1021 nucleotides or

about 1022 bytes. Individuals and answers can be encoded

in DNA molecules using binary representations. Larger

populations can carry on larger ranges of genetic diversity

and hence can generate high-fitness chromosomes in fewer

generations thus effectively reducing the size of the search

space. Furthermore, experimenting in vitro operations on

DNA inherently involve errors. These are more tolerable in

executing genetic algorithms than in executing deterministic

algorithms. Ina sense, errorsmay beregardedas a contributing

factor to genetic diversity.

A DNA-based GA was proposed as an application of

an evolution program searching for good encodings (Deatonet al 1997). Yoshikawa et al (1997) combined the DNA-

encoding method with the pseudo-bacterial GA. Chen et al

(1999) proposed the laboratory implementation of the DNA-

GA for some simple problems such as the Max 1s, the royal

road, and the cold war problems. Wood et al (1999) designed

and implemented a DNA-based in vitro genetic algorithm for

the Max 1s problem. Wood and Chen (1999) proposed and

implemented a DNA strand design suited for the royal road

problem using a genetic algorithm, where in vitro evolution

started with a randomized population of DNA strands. A few

years later, Rose et al (2002) proposed a DNA-based in vitro

genetic algorithm for the HPP.

Evolutionary and genetic DNA computing were proposedto solve the maximum clique (Back et al 1999, Yuan and

Chen 2004). Yuan and Chen (2004) designed a DNA best

GA for the maximal clique problem, which was capable to

produce correct solutionwithin a fewcycles at highprobability.

Their simulation indicated that the time requirement of their

approach was approximately a linear function of the number

of vertices in the network.

Wood et al (2001) employed in vitro evolutionary DNA

computing to learn game playing and find adaptive game-

theoretic strategies. They applied their approach for the game

of poker where they constructed two single-stranded DNAs to

represent the two possible plays. Stojanovic and Stefanovic

(2003) designed a DNA computer named MAYA capable ofplaying tic-tac-toe.

Ren et al (2003) proposed a new approach to the

virus DNA-based evolutionary algorithm (VDNA-EA) to

implement self-learning of a class of TakagiSugeno (TS)

fuzzy controllers. The VDNA encoding method was used to

encode the design parameters of the fuzzy controllers which

has shortened the code length of the DNA chromosome.

The frameshift decoding method was used to decode the

DNA chromosome into the design parameters of the fuzzy

controllers. Those methods have made the genetic operators

capable to operate at the gene level within the VDNA-

EA approach. Computer simulation demonstrated the

effectiveness of this method in designing automatically a classof TS fuzzy controllers. Neural networks also represent

other prospective candidates (Russo et al 1994, Farhat and

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Topical Review

Hernandez 1995) in making DNA computing more efficient.

Therefore, the design of artificial intelligence techniques in

DNA computing will serve to break the barrier of this brute-

force method and get a final solution from a very small

initial data pool, avoiding enumerating all candidate solutions

(Ezziane 2006).

5.2.2. Swarm intelligence. Apart from genetic algorithms

and other evolutionary algorithms that have promising

potential for a variety of problems such as automatic system

design for molecular nanotechnology (Hall 1997), another

emerging technique is swarm intelligence, which is inspired

by the collective intelligence in social animals such as birds,

ants, fish and termites. These social animals require no

leader. Their collective behaviours emerge from interactions

among individuals, in a process known as self-organization.

Each individual may not be intelligent, but together they

perform complex collaborative behaviours. Typical uses

of swarm intelligence are to assist the study of humansocial behaviour by observing other social animals and to

solve various optimization problems (Bonabeau et al 1999,

Eberhart et al 2001). There are three main types of swarm

intelligence techniques: models of bird flocking, the ant

colony optimization (ACO) algorithm, and the particle swarm

optimization (PSO) algorithm.

Besides being a model of the human social behaviour, the

particle swarm (Kennedy and Eberhart 1995) is closely related

to swarm intelligence. In theparticle swarm, there is no central

control: no one gives orders. Each particle is a simple agent

acting upon local information. Yet, the swarm as a whole

is able to perform tasks, whose degree of complexity is well

beyond the capabilities of the individual. The particle swarmshows signs of self-organization. The interactions among the

low-level components (particles) result in complex structures

at the global level (swarm) making it possible for it to perform

optimization of functions.

PSO was originally designed to simulate bird flocking

in order to learn more about the human social behaviour

(Kennedy and Eberhart 1995). However, the conventional

particle swarm optimization relies on social interaction among

particles through exchanging detailed information on position

and performance. In the physical world, this type of complex

communication is not always possible.

Recently, Kaewkamnerdpong and Bentley (2005) pro-

posed a new swarm algorithm, called the Perceptive ParticleSwarm Optimization (PPSO) algorithm. The PPSO algorithm

has extended the conventional PSO algorithm for applications

in the physical world. This extension takes into consideration

both the social interaction among particles and environmen-

tal interaction. The PPSO algorithm simulates the emerging

collective intelligence of social insects more closely than the

conventional PSO algorithm. The PPSO algorithm is designed

to handle real-world physical control problems including pro-

gramming or controlling agents of nanotechnology, for exam-

ple nanorobots or DNA computers.

6. Conclusion

The main benefit of using DNA computers to solve complex

problems is that different possible solutions are created all at

once and in a parallel fashion. Humans and most electronic

computers must attempt to solve the problem one process at

a time. DNA itself provides the added benefits of being a

cheap, energy-efficient resource. The increasing ability to

design complex molecules and systems makes these models

of computation increasingly of interest for nanotechnologyand biological engineering, as well as for the fundamental

understanding of biological processes.

Important events which have taken place in the field of

DNA computing initiated the possibility of exploiting the

massive parallelism, high storage density, and nanostructures

inherent in natural phenomena to solve computational

problems. Here indeed remain tremendous scientific,

engineering, and technological challenges to bring this

paradigm to full fruition, and thus make DNA computing a

competitive player in the landscape of practical computing

(Garzon and Deaton 1999).

The implementation of an intelligent system method such

as a GA in DNA computing presents an attractive alternativeto further evolutionary computation research by pushing

the analogy into a fully fledged in vivo implementation.

DNA computing is hence poised to enable feasible solutions

of previously infeasible search problems by using newly

available molecularbiological technology(Garzon and Deaton

1999). The DNA-based intelligent algorithms have potential

advantages in many complex practical problems.

The engineering and programming of biochemical

circuits, in vivo and in vitro, could transform industries that

use chemical and nanostructured materials. Information and

algorithms appear to be central to biological organization

and processes, from the storage and reproduction of genetic

information to the control of developmental processes to thesophisticated computations performed by the nervous system.

Much as human technology uses electronic microprocessors

to control electromechanical devices, biological organisms

use biochemical circuits to control molecular and chemical

events. The engineering and programming of biochemical

circuits would transform industries that use chemical and

nanostructured materials. Although the construction of

biochemical circuits has been explored theoretically since the

birth of molecular biology, the current practical experience

with the capabilities and possible programming of biochemical

algorithms is still in its infancy (Winfree 2003).

Bioelectronics is another sub-discipline that uses

biological molecules such as bacteriorhodopsin in electronicor photonic devices (Gupta et al 2001). It seeks to exploit

the growing technical ability to integrate biomolecules with

electronics to develop a broad range of functional devices.

An important research aspect is the development of the

communication interface between the biological materials and

electronic components. Bioelectronics research also seeks

to use biomolecules to perform the electronic functions that

semiconductor devices currently perform, thereby offering the

potential to increase computing-microchip density sufficiently

to continue Moores law down to the nanometre level.

DNA computing has expanded the notion of what is

computation. However, up to now a practical mathematical

problem that would justify the use of massive parallelismachieved by the DNA computations has not been developed.

Therefore, we might have to wait some time for DNA to

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Topical Review

replace the silicon in our computers. Future DNA computing

would provide exciting opportunities and open doors to

solve new research problems in combinatorics, complexity

theory and algorithms, intelligent manufacturing systems,

complex molecular diagnostics and molecular process control

(McCaskill 2000).For the long term, one can speculate about the prospect

for molecular computation. It seems likely that a single

molecule of DNA can be used to encode the instantaneous

description of a Turing machine and those currently available

protocols and enzymes could be used to induce successive

sequence modifications, which would correspond to the

execution of the machine. In the future, research in molecular

biology may provide improved techniques for manipulating

macromolecules. Research in chemistry may allow for

the development of synthetic designer enzymes. One can

imagine theeventualemergence ofa general-purposecomputer

consisting of nothing more than a single macromolecule

conjugated to a ribosome-like collection of enzymes that act

on it (Manca 1999).

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12/13

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Z Ezziane- DNA computing: applications and challenges

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