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