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DNA COMPUTING
Samira T P
Shruthi N
samiratp@gmail.com
shruthi.nag@gmail.com
SIT, Tumkur
Abstract
DNA computing is a nascent technology
that seeks to capitalize on the enormous
informational capacity of DNA, biological
molecules that can store huge amounts of
information and are able to perform operations
similar to a computer's through the deployment of
enzymes, biological catalysts that act like
software to execute desired operations.
The appeal of DNA computing lies in the
fact that DNA molecules can store far more
information than any existing conventional
computer chip. It has been estimated that a gram
of dried DNA can hold as much information as a
trillion CDs. Moreover, in a biochemical reaction
taking place in a tiny surface area, hundreds of
trillions of DNA molecules can operate in concert,
creating a parallel processing system that mimics
the ability of the most powerful supercomputer.
The chips that drive conventional
computers represent information as a series of
electrical impulses using ones and zeros.
Mathematical formulas are used to manipulate
that binary code to arrive at an answer. DNA
computing, on the other hand, depends on
information represented as a pattern of molecules
arranged on a strand of DNA. Certain enzymes
are capable of reading that code, copying and
manipulating it in predictable ways.
The speed of computations is such that, a
DNA computer can solve a problem (travelling
salesman problem with 5 lakh cities) in 5 seconds
compared 5 years as taken by the world’s fastest
super computer(KRAY 4). This paper illustrates
the computing capability of DNA molecules by
taking ‘travelling salesman problem with 5 cities’
1. Introduction
The Moore’s law as stated by Gordon
Moore, the founder of Intel states that the number
of electronic devices on a microprocessor doubles
every 18 months and it has been so till date.
However many experts have predicted that
Moore’s law will soon reach its end due to
limitations on speed and miniaturization.
This brought about the need of a new material that
could be used to design computing systems and
scientists have found the material inside our own
body.
The DNA.
DNA (deoxyribonucleic-acid) molecules, the
material our genes are made of have the potential
to perform calculations many times faster than the
world’s most powerful human-built computers.
DNA might one day be integrated into a computer
chip to create a so-called biochip that will push
computers even faster. DNA molecules have
already been harnessed to perform complex
mathematical problems. While still in their
infancy, DNA computers will be capable of
storing billions of times more data than your
personal computer.
DNA computing is an offspring of
nanotechnology where genetic material is used to
create nano-computers that might take the place of
silicon-based computers in the next decade. DNA
is used by genes to remember the intricate
patterns of life will be used in DNA computers to
store data. DNA performs massive parallel
processing which makes DNA computers faster
than the fastest super computer. DNA computers
solve highly complex problems within a blink of
the eye, while a super computer may take months.
The main benefit of using DNA computers to
solve complex problems is that different possible
solutions are created all at once which is due to
parallel processing capability of DNA by which it
can perform trillions of calculations at a time.
The storage capacity of DNA is
tremendous. A single gram occupies a volume of
1 cm3 and can hold as much information as a
trillion compact discs, approximately 750
terabytes. DNA computers use a different
methodology to compute solutions; it uses
molecular automations and preset reactions to
arrive at the solution. The solutions are obtained
by the links that the DNA generates amongst
them. The wrong solutions are eliminated and the
correct one is retained. DNA computers have
already surpassed the super computers in their
abilities and are the future of computing.
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The success of the Adleman DNA computer
proves that DNA can be used to calculate
complex mathematical problems. However,
this early DNA computer is far from
challenging silicon-based computers in terms
of speed. The Adleman DNA computer
created a group of possible answers very
quickly, but it took days for Adleman to
narrow down the possibilities. Another
drawback of his DNA computer is that it
requires human assistance. The goal of the
DNA computing field is to create a device that
can work independent of human involvement.
Nevertheless, his work is significant for a number
of reasons.
 It illustrates the possibilities of using
DNA to solve a class of problems that is
difficult or impossible to solve using
traditional computing methods.
 It's an example of computation at a
molecular level, potentially a size limit
that may never be reached by the
semiconductor industry.
 It demonstrates unique aspects of DNA
as a data structure
 It demonstrates that computing with
DNA can work in a massively parallel
fashion.
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2. Fundamentals about dna structure
The DNA(deoxyribonucleic acid) is a
biological structure that forms the building block
of any living organism. The DNA has a double
helix structure and determines the genetics of the
living organisms. The data density of DNA is
impressive. Just like a string of binary data is
encoded with ones and zeros, a strand of DNA is
encoded with four bases, represented by the
letters A, T, C, and G. The bases (also known as
nucleotides) are spaced every 0.35 nanometers
along the DNA molecule, giving DNA an
remarkable data density of nearly 18 Mbits per
inch. In two dimensions, if you assume one base
per square nanometer, the data density is over
one million Gbits per square inch. Compare this
to the data density of a typical high performance
hard drive, which is about 7 Gbits per square
inch -- a factor of over 100,000 smaller.
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y
We can be confident that we have all possible
combinations including the correct one by using
an excess of DNA encodings. We now have a test
tube full of various lengths of DNA that encode
possible routes between cities. What we want are
routes that start with Mumbai and end with
Kolkata. To accomplish this we can use a
technique called Polymerase Chain Reaction
(PCR), which allows you to produce many copies
of a specific sequence of DNA. PCR is an
iterative process that cycles through a series of
copying events using an enzyme called
polymerase. Polymerase will copy a section of
single stranded DNA starting at the position of a
primer, a short piece of DNA complimentary to
one end of a section of the DNA that we are
interested in. By selecting primers that flank the
section of DNA we want to amplify, the
polymerase preferentially amplifies the DNA
between these primers, doubling the amount of
DNA containing this sequence. After many
iterations of PCR, the DNA we’re working on is
amplified exponentially. So to selectively amplify
the itineraries that start and stop with our cities of
interest, we use primers that are complimentary to
Mumbai and Kolkata. What we end up with after
PCR is a test tube full of double stranded DNA of
various lengths, encoding itineraries that start with
Mumbai and end with Kolkata.
STEP 2:Sort the DNA by length and select the
DNA whose length corresponds to 5 cities. Our
test tube is now filled with DNA encoded
itineraries that start with Mumbai and end with
Kolkata, where the number of cities in between
Mumbai and Kolkata varies. We now want to
select those itineraries that are five cities long. To
accomplish this we can use a technique called Gel
Electrophoresis, which is a common procedure
used to resolve the size of DNA. The basic
principle behind Gel Electrophoresis is to force
DNA through a gel matrix by using an electric
field. DNA is a negatively charged molecule
under most conditions, so if placed in an electric
field it will be attracted to the positive potential.
However since the charge density of DNA is
constant (charge per length) long pieces of DNA
move as fast as short pieces when suspended in a
fluid. This is why you use a gel matrix. The gel is
made up of a polymer that forms a meshwork of
linked strands.
The DNA now is forced to thread its way through
the tiny spaces between these strands, which
slows down the DNA at different rates depending
on its length. What we typically end up with after
running a gel is a series of DNA bands, with each
band corresponding to a certain length. We can
then simply cut out the band of interest to isolate
DNA of a specific length. Since we known that
each city is encoded with 6 base pairs of DNA,
knowing the length of the itinerary gives us the
number of cities. In this case we would isolate the
DNA that was 30 base pairs long (5 cities times 6
base pairs).
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So we now affinity purify fives times, using a
different city complement for each run. For
limiting the number of iterations we
can do successively before the
probability of producing an error
becomes greater.
example, for the first run we use Mumbai'beads (where the ' indicates compliment
strand) to fish out DNA sequences which
contain the encoding for Mumbai (which
should be all the DNA because of step 3),
the next run we use Bangalore'-beads, and
then
finally
Delhi'-beads,
Chennai'-beads,
Kolkata'-beads.
The
order
and
3.Applications of DNA Computing
isn’t
The primary advantage offered by most proposed
models of DNA based computation is the ability
to handle millions of operations in parallel. The
massively parallel processing capabilities of
DNA computers may give them the potential to
find tractable solutions to otherwise intractable
problems, as well as potentially speeding up
large, but otherwise solvable, polynomial time
problems requiring relatively few operations
important. If an itinerary is missing a city,
then it will not be "fished out" during one of
the runs and will be removed from the
candidate pool. What we are left with are the
are itineraries that start in Mumbai, visit
each city once, and end in Kolkata. This is
exactly what we are looking for. If the
answer exists we would retrieve it at this
step.
One possible way to find the result would be to
simply sequence the DNA strands. However,
since we already have the sequence of the city
encodings we can use an alternate method called
graduated PCR. Here we do a series of PCR
amplifications using the primer corresponding to
L.A., with a different primer for each city in
succession. By measuring the various lengths of
DNA for each PCR product we can piece
together the final sequence of cities in our
itinerary.
Adleman's experiment solved a seven city
problem, but there are two major shortcomings
preventing a large scaling up of his computation.
They are:
1. As the complexity of the problem
increases exponentially, the amount of
DNA to be used also increases
exponentially. This places some
restrictions on the number of cities that
can be solved.
2. Since thee operations are not
deterministic but stochastically driven,
each step contains statistical errors,
Massively Parallel Processing
DNA computers derive their potential advantage
over conventional computers from their ability
to:
 Perform
millions
of
operations
simultaneously;
 Generate a complete set of potential
solutions;
 Conduct large parallel searches; and
 Efficiently handle massive amounts of
working memory.
A working DNA based computer might hold an
advantage over conventional computers when
applied to decomposable problems, those
problems that are able to be divided into
separate, non-sequential tasks, because they can
hold so much data in memory and conduct so
many operations at once
4.Solving NP-Complete and Hard
Computational Problems
After Adleman's and Lipton's initial results,
much of the work on DNA computing has
continued to focus on solving NP-complete
and other hard computational problems.
Problems in NP-complete are such that
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there is no polynomial time solution known
to
exist
using
conventional
computer
algorithms. That is, as the complexity of
these problems increase, the time required
to solve them increases at an exponential
rate. These problems are also said to be
intractable, but, if within the domain of NPcomplete a tractable solution can be found
to one of these problems then it can also be
used to solve for all other problems in the
set.
Adleman's original and subsequent works
demonstrated a tractable solution to the directed
Hamiltonian path problem, similar to the more
familiar traveling salesperson problem. It has
since been shown that a direct polynomial time
solution can be obtained for any NP-complete
problem using similar techniques of DNA
computation .
The ability to obtain tractable solutions to NPcomplete and other hard computational problems
has many implications to real life problems,
particularly
in
business
planning
and
management science. Many of the cost
optimization problems faced by managers are in
NP-complete are currently solved using heuristic
methods and other approximations. These
problems include scheduling, routing, and
optimal use of raw materials and correspond to
problems already solved, either theoretically or
experimentally, using DNA computation.
Storage and Associative Memory
DNA might also be used to mirror, and even
improve upon, the associative capabilities of the
human brain. Baum proposed a method for
making a large content addressable memory
using DNA. A truly content addressable memory
occurs when a data entry can be directly
retrieved from storage by entering an input that
most closely resembles it over other entries in
memory. This input may be very incomplete,
with a number of wildcards, and in an associative
memory might even contain bits that do not
actually occur within the closest match. This
contrasts with a conventional computer memory,
where the specific address of a word must be
known to retrieve it. Rather, the use of this
technique would replicate what is thought by
many to be a key factor in human intelligence.
Baum's models for a DNA associative memory
are quite simple, and build from the techniques
used in other areas of DNA computation. Storing
a word could be done by assigning a specific
DNA subsequence to each component value pair
and building a fixed length word from these
subsequences. To then retrieve the word closest
to the input, one would introduce marked
complementary subsequences into the storage
medium and chose the molecule that has the
most matches to this input. This technique could
be further refined to more closely approximate
the brain by appending words to only store
attributes that an object has, rather than wasting
space using '0's to represent attributes that an
object does not have.
5.DNA2DNA Applications
Another area of DNA computation exists where
conventional computers clearly have no current
capacity to compete. This is the concept of
DNA2DNA computations as suggested in [12]
and identified as a potential killer app.
DNA2DNA computations involve the use of
DNA computers to perform operations on
unknown pieces of DNA without having to
sequence them first. This is achieved by recoding and amplifying unknown strands into a
redundant form so that they can be operated on
according to techniques similar to those used in
the sticker model of DNA computation. Many of
the errors inherent in other models of DNA
computing can hopefully be ignored in
DNA2DNA computing because there will be
such a high number of original strands available
for operations.
The potential applications of re-coding natural
DNA into a computable form are many and
include:
 DNA sequencing;
 DNA fingerprinting;
 DNA mutation detection or population
screening; and
 Other fundamental operations on DNA.
Bibliography
1] www.aj.com
2] www.advancements.org/dnacomputations
3] “Molecular Biology and its Technological
Implications” – Richard Moore
4] “DNA Sequencing, Mapping and Algorithms”
– Douglas MacLean
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5] “DNA Computations and the first DNA
Computer” - Aldeman
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