ajith bio molecular computer
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INTRODUCTION
Molecular computing is an emerging field to which chemistry,
biophysics, molecular biology, electronic engineering, solid state physics
and computer science contribute to a large extent. It involves the encoding,
manipulation and retrieval of information at a macromolecular level in
contrast to the current techniques, which accomplish the above functions
via IC miniaturization of bulk devices. The biological systems have unique
abilities such as pattern recognition, learning, self-assembly and self-
reproduction as well as high speed and parallel information processing.
The aim of this article is to exploit these characteristics to build computing
systems, which have many advantages over their inorganic (Si,Ge)
counterparts.
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Who thought of this?
DNA computing began in 1994 when Leonard Adleman proved that
DNA computing was possible by finding a solution to a real- problem, a
Hamiltonian Path Problem, known to us as the Traveling Salesman Problem,
with a molecular computer. In theoretical terms, some scientists say the actual
beginnings of DNA computation should be attributed to Charles Bennett's
work.
Adleman, now considered the father of DNA computing, is a
professor at the University of Southern California and spawned the field with
his paper, "Molecular Computation of Solutions of Combinatorial Problems."
Since then, Adleman has demonstrated how the massive parallelism of a
trillion DNA strands can simultaneously attack different aspects of a
computation to crack even the toughest combinatorial problems.
Adleman's Traveling Salesman Problem:
The objective is to find a path from start to end going through all the
points only once. This problem is difficult for conventional computers to solve
because it is a "non-deterministic polynomial time problem" . These problems,
when they involve large numbers, are intractable with conventional
computers, but can be solved using massively parallel computers like DNA
computers. The Hamiltonian Path problem was chosen by Adleman because it
is known problem.
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The following algorithm solves the Hamiltonian Path problem:
1.Generate random paths through the graph.
2.Keep only those paths that begin with the start city (A) and conclude with the
end city (G).
3.If the graph has n cities, keep only those paths with n cities. (n=7)
4.Keep only those paths that enter all cities at least once.
5.Any remaining paths are solutions.
The key was using DNA to perform the five steps in the above algorithm.
Adleman's first step was to synthesize DNA strands of known
sequences, each strand 20 nucleotides long. He represented each of the six
vertices of the path by a separate strand, and further represented each edge
between two consecutive vertices, such as 1 to 2, by a DNA strand which
consisted of the last ten nucleotides of the strand representing vertex 1 plus
the first 10 nucleotides of the vertex 2 strand. Then, through the sheer amount
of DNA molecules (3x1013 copies for each edge in this experiment!) joining
together in all possible combinations, many random paths were generated.
Adleman used well-established techniques of molecular biology to weed out
the Hamiltonian path, the one that entered all vertices, starting at one and
ending at six. After generating the numerous random paths in the first step, he
used polymerase chain reaction (PCR) to amplify and keep only the paths that
began on vertex 1 and ended at vertex 6. The next two steps kept only those
strands that passed through six vertices, entering each vertex at least once. At
this point, any paths that remained would code for a Hamiltonian path, thus
solving the problem. (Adleman)
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How do they work?
DNA is the major information storage molecule in living cells,
and billions of years of evolution have tested and refined both this wonderful
informational molecule and highly specific enzymes that can either duplicate
the information in DNA molecules or transmit this information to other DNA
molecules.
Instead of using electrical impulses to represent bits of
information, the DNA computer uses the chemical properties of these
molecules by examining the patterns of combination or growth of the
molecules or strings. DNA can do this through the manufacture of enzymes,
which are biological catalysts that could be called the 'software' used to
execute the desired calculation.
DNA computers use deoxyribonucleic acids--A (adenine), C
(cytosine), G (guanine) and T (thymine)--as the memory units, and
recombinant DNA techniques already in existence carry out the fundamental
operations. In a DNA computer, computation takes place in test tubes or on a
glass slide coated in 24K gold. The input and output are both strands of DNA,
whose genetic sequences encode certain information. A program on a DNA
computer is executed as a series of biochemical operations, which have the
effect of synthesizing, extracting, modifying and cloning the DNA strands.
Their potential power underscores how nature could be capable of crunching
number better and faster than the most advanced silicon chips.
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The only fundamental difference between conventional
computers and DNA computers is the capacity of memory units: electronic
computers have two positions (on or off), whereas DNA has four (C, G, A or
T). The study of bacteria has shown that restriction enzymes can be employed
to cut DNA at a specific word(W). Many restriction enzymes cut the two
strands of double-stranded DNA at different positions leaving overhangs of
single-stranded DNA. Two pieces of DNA may be rejoined if their terminal
overhangs are complementary. Complements are referred to as 'sticky ends'.
Using these operations, fragments of DNA may be inserted or deleted from
the DNA.
As stated earlier DNA represents information as a pattern of
molecules on a strand. Each strand represents one possible answer. In each
experiment, the DNA is tailored so that all conceivable answers to a particular
problem are included. Researchers then subject all the molecules to precise
chemical reactions that imitate the computational abilities of a traditional
computer. Because molecules that make up DNA bind together in predictable
ways, it gives a powerful "search" function. If the experiment works, the DNA
computer weeds out all the wrong answers, leaving one molecule or more
with the right answer. All these molecules can work together at once, so you
could theoretically have 10 trillion calculations going on at the same time in
very little space.
DNA computing is a field that holds the promise of ultra-
dense systems that pack megabytes of information into devices the size of a
silicon transistor. Each molecule of DNA is roughly equivalent to a little
computer chip. Conventional computers represent information in terms of 0's
and 1's, physically expressed in terms of the flow of electrons through logical
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circuits, whereas DNA computers represent information in terms of the
chemical units of DNA. Computing with an ordinary computer is done with a
program that instructs electrons to travel on particular paths; with a DNA
computer, computation requires synthesizing particular sequences of DNA
and letting them react in a test tube or on a glass plate. In a scheme devised by
Richard Lipton, the logical command "and" is performed by separating DNA
strands according to their sequences, and the command "or" is done by
pouring together DNA solutions containing specific sequences, merging.
By forcing DNA molecules to generate different chemical states,
which can then be examined to determine an answer to a problem by
combination of molecules into strands or the separation of strands, the answer
is obtained.
Most of the possible answers are incorrect, but one or a few
may be correct, and the computer's task is to check each of them and remove
the incorrect ones using restrictive enzymes. The DNA computer does that by
subjecting all of the strands simultaneously to a series of chemical reactions
that mimic the mathematical computations an electronic computer would
perform on each possible answer. When the chemical reactions are complete,
researchers analyze the strands to find the answer -- for instance, by locating
the longest or the shortest strand and decoding it to determine what answer it
represents.
Computers based on molecules like DNA will not have a
vonNeumann architecture, but instead function best in parallel processing
applications. They are considered promising for problems that can have
multiple computations going on at the same time. Say for instance, all
branches of a search tree could be searched at once in a molecular system
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while vonNeumann systems must explore each possible path in some
sequence.
Information is stored in DNA as CG or AT base pairs
with maximum information density of 2bits per DNA base location.
Information on a solid surface is stored in a NON-ADDRESSED array of
DNA words(W) of a fixed length (16 mers). DNA Words are linked together
to form large combinatorial sets of molecules.
DNA computers are massively parallel, while electronic computers would
require additional hardware, DNA computers just need more DNA. This could
make the DNA computer more efficient, as well as more easily
programmable.
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PROCESSING AND STORING OF
INFORMATION
Nucleic Acids are used because of density, efficiency
and speed. DNA molecules can store far more information than any existing
computer memory chip. This means that DNA computing is a far denser
packing of molecular information compared with silicon-based computers. A
single bacterium cell measures just a micron square - about the same size as a
single silicon transistor - but holds more than a megabyte of DNA memory
and has all the computational structures to sense and respond to its
environment. To try to put this in some understandable perspective, it has
been estimated that a gram of DNA can hold as much information as a trillion
CDs.
So DNA molecules would be like mega-memory. In
a biochemical reaction hundreds of trillions of DNA molecules can operate in
parallel. DNA computers could store a bit, 0 or 1, of data in one cubic nano
meter, one trillionth the size of the conventional computer's electronic storage.
Thus a DNA computer could store massive quantities of information in the
space a standard computer would use to store much less. A pound of DNA
could contain more computer memory than all the electronic computers ever
made. It would be about twice as fast as the fastest supercomputer, performing
more than 2,000 instructions per second. DNA computers also require
miniscule amounts of energy to perform. "We're interested in scale up. We
believe that ... we can see scaling up within a few years by a factor of a trillion
or more." (Lloyd Smith)
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Because the biochemical operations involved are subject to errors and are
often slow, rigorous tests of the accuracy and further technological
development are needed.
What about efficiency?
In both the solid-surface glass-plate approach and the
test tube approach, each DNA strand represents one possible answer to the
problem that the computer is trying to solve. The strands have been
synthesized by combining the building blocks of DNA, called nucleotides,
with one another, using techniques developed for biotechnology. The set of
DNA strands is manufactured so that all conceivable answers are included.
Because a set of strands is tailored to a specific problem, a new set would
have to be made for each new problem.
Most electronic computers operate linearly--they
manipulate one block of data after another--biochemical reactions are highly
in parallel: a single step of biochemical operations can be set up so that it
affects trillions of DNA strands. While a DNA computer takes much longer
than a normal computer to perform each individual calculation, it performs an
enormous number of operations at a time and requires less energy and space
than normal computers. 1000 litres of water could contain DNA with more
memory than all the computers ever made, and a pound of DNA would have
more computing power than all the computers ever made.
Obviously if you want to perform one calculation at a time, DNA computers
are not a viable option. When Adleman derived an optimal solution to a
seven-city traveling-salesman problem, it took approximately one week.
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Unfortunately, you can solve the same problem on a piece of paper in about
an hour - or by a digital computer in a few seconds. But when the number of
cities is increased to just 70, the problem becomes intractable for even a 1000-
Mips supercomputer.
What are the particular problems of error correction?
In real operations that molecular biologists do every
day in the lab, they don't perform many operations on the scale or amount of
DNA that is necessary for practical DNA computing. They seldom handle the
same number of steps that these computers will require, perhaps thousands,
and they are not called upon to operate with the accuracy that is required of a
computer.
The analysis of the errors that occur in these computers
is often very difficult because molecular biologist don't quantify their errors.
For example, when creating a recombinant DNA molecule that they would
like to put into a bacterium, molecular biologists don't require that a
recombinant DNA operation work for 100% of the molecules in a reaction, or
even 1%. A molecular biologist only wishes to get 'enough' correct
recombinant molecules to be able to transfer one bacterium that will yield one
colony of bacteria that have the desired characteristic.
The Restricted Model:
Since Adleman's original experiment, several
methods to reduce error and improve efficiency have been developed. The
problems with implementing a DNA computer can be separated into two
types:
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Physical obstructions: difficulties with large scale systems and coping with
errors
Logical obstructions: concerning the versatility of molecular computers and
their capacity to efficiently accommodate a wide variety of computational
problems.
The Restricted model of DNA computing solves
several physical problems scientists had with the Unrestricted model. The
Restricted model simplifies the physical obstructions in exchange for some
additional logical considerations. The purpose of this restructuring is to
simplify biochemical operations and reduce the errors.
The Restricted model of DNA computing in test tubes is simplified to:
Separate: isolate a subset of DNA from a sample
Merge: pour two test tubes into one to perform union
Detect: Confirm presence/absence of DNA in a given test tube
Despite these restrictions, this model can still solve Hamiltonian Path
problems. (Adleman)
Error control can also be achieved mainly through logical operations, such as
running all DNA samples showing positive results a second time to reduce
false positives. Some molecular proposals, such as using DNA with a peptide
backbone for stability, have also been recommended.
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DNA Computing on a chip
The DNA computing was proposed as a means of
solving a class of computational problems where in the computing time grows
exponentially with the problem size. ‘Traveling sales man’ or ‘Hamilton path
problem’ is an example. One technique for solving such problems involves the
immobilization and manipulation of combinatorial mixtures of DNA on a
support. These problems include the search for a solution that simultaneously
satisfies a number of clauses, each composed of a number of variables
connected by OR statements. These can be solved by a reasonable amount of
time by using brute force search made possible by the parallel nature of DNA
computing techniques. Here, space (amount of DNA needed) is exchanged for
time (number of biochemical steps to be used) to achieve a small
computational time. The whole process consists of the following steps
1) A set of DNA molecules (Watson strands) encoding
all candidate solutions to the computational problems of interest is synthesized
and attached to a surface via a reactive functional group.
2) A ‘mark’ operation is carried out in which
supplementary strands for all possible Watson strands satisfying the first
clause are added. These supplementary strands are called Crick Strands. These
Crick strands stick to corresponding Watson strand creating double stranded
DNA.
3) After this, an enzyme is added which destroys all
surface bond oligonucleotides present in single strand form – destroy
operation.
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4) The surface is then regenerated for the next cycle of
operation by removing all hybridized compliments in an ‘unmark’
operation.This cycle of mark, destroy and unmark, will be repeated as many
times as the number of clauses (say, N). At the end of N cycles, only those
strands, which are solutions to the problem, remain. The identities of these
solutions are determined in a ‘read out’ operation by polymerase chain
reaction (PCR) followed by amplification and hybridization to an addressed
array.
The various steps for performing DNA computing is shown below.
Consider for example, a four variable, four clause 3-SAT problem given as
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(x OR y’ OR z) AND (w OR x’ OR z) AND (x’ OR y,) AND (w OR z)
The above mentioned problem can be solved in four
cycles of the ‘mark’, ‘destroy’, and ‘unmark’ operation as shown in figure
below. The four binary variables w, x, y and z and the possible solution set
consists of the decoded values of all combinations of these variables. Thus the
universal set consists of strands of numbered 0 to 15. For example: 1010(w=1,
x=0, y=1 and z=0) will represent the number 10 strand. The same convention
is followed for all possible answers. Using the procedure mentioned above,
the solutions are isolated one by one. For example: after step one, two strands
are eliminated as 2(0010) and 10(1010) do not satisfy the first clause.
Proceeding in similar fashion, one gets the final solution set as 1(0001),
3(0011), 8(1000), 9(1001),11(1011), 12(1100), 13(1101).
Not counting the number of steps required to produce the
DNA molecules in the first place, the algorithm takes only (3k+1) steps
(where k is the number of clauses for a brute force evaluation of all possible
answers). This is a remarkable improvement over the best conventional
computer algorithm. For example, a 3-SAT problem with 30 clauses and 50
variables could be solved in approximately 1.6 million steps by an ordinary
algorithm, but in only 91 steps by the DNA computer.
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Enzyme Based Logic Gates (ENLOGS)
These molecular switches use enzymatic activity to perform
operations that correspond to simple logic operations such as AND, OR, etc.
The classical analogy to explain the switching is the lock and key model.
Since the enzymes are the facilitating components, it is convenient to think of
each type of enzyme as a specific key and the substrates as locks. Fitting the
key into the lock is a simple switching event. The main crux of the problem is
to exploit the micro scale mechanisms to solve macro scale problems.
figure 1 : basic module of enzymatic switch
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The transduction and integration step, the input signals
impinging on the module are transduced to physiochemical presentations with
shape features, which enzyme can recognize. This is followed by the
recognition effect association step in which the read out enzymes recognize
the transduced features and take an action ,thus linking these features to a
molecular scale output. In the amplification step, the molecular level output is
amplified to a macroscopic output.
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PROTEIN BASED OPTICAL COMPUTING MEMORIES
Much research has gone in developing high-speed
optics random access memory based on bacteriorhodopsin. Bacteriorhodopsin
is a purple coloured pigment occurring in the cell membrane of
Halobacterium halobium. It utilizes solar energy to move protons across the
membrane, resulting in difference in the proton levels. Now it is known that
under a high proton concentration, the formation of ATP takes place and this
ATP is used to catalyse a reaction. By measuring the rate of reaction, one can
create a logic gate. On being cooled to sufficiently low temperatures, a
nanometer-sized section of the bR molecule will kink out of shape when
struck by a green laser. But, most importantly, the altered bR molecules can
be made to snap back to their original form, if hit by a red laser. Hence, bR
can act as the basis for a molecular binary switch. This can be used to make
large optical memories with access time below two nano seconds. Currently,
access times of 20ns have been achieved, the major limitation being the speed
at which optical beams can be positioned to read or write single bits. Such bR
based molecular storage devices could potentially store as much as 480
gigabytes of data in just five cubic centimeters, that can be read, written, or
erased in as little as five pico seconds using present laser diode technology.
In contrast to such a bR based system, today’s
semiconductor systems are thousand times slower, and would require
enclosures the size of a home refrigerator to hold an equivalent amount of
data. Also, unlike 2-D semiconductors, bR devices are naturally 3-D in
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geometry. As such, bR’s unique architecture might herald a new era of
multidimensional holographical computing.
Another use of bR can be in the field of developing
electronic ink for computer displays. It can be made to change its optical
properties, especially its colour, when acted on by electric field-a property
called as electrochromism. Certain mutant halabacteria produce
bacteriorhodopsin, that potentially exhibits very strong electrochromism, in
which the colour changes from blue to pale yellow. These can thus be used as
electrically addressable pigments for use in computer displays. A bR is
sandwiched between glass plates that contain arrays of large number of
electrodes. A page of text or a colour image is written electrically on the
protein film by applying the corresponding array of voltages on the electrodes,
similar to the technology used in liquid crystal displays for laptop computers.
The difference is that electronic ink get its colour from reflecting ambient
light , whre as laptop displays use internal light sources which are a drain on
the batteries. Thus, the battery life time problem in portable computers can be
overcome.
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MOLECULAR AGAINST CONVENTIONAL
COMPUTING
The molecular-based systems have several advantages
over the silicon systems
Design and Fabrication
The costs to design and build a 64 mega-bit memory chip run into
billions of dollars and these costs would raise higher for larger memory-sized
chips. In contrast, some bio molecular systems like bR offer the promise of
being economically grown in a vat and can quickly be harvested in a normal
environment which is controlled via ordinary chemistry or use of shelf laser
diodes.
Quantum Effects They are introduced due to very small size of solid-state devices.
This is important when the feature size reduces to a point where one is dealing
with individual atoms. The quantum effects like unwanted tunneling of
electrons pose a great difficulty. These effects can be nullified using an
average output through redundant circuits making the fabrication costlier. In
contrast, in the molecular-based systems , billions of atoms can be stuffed into
smallest patches of material, which can carry or encode identical information
with reasonable accuracy despite quantum effects due to the natural
redundancy inherent in these systems.
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Thermal Build-up Semiconductor designers are always trying to shrink circuit line
widths in order to increase overall processor speed. But this causes massive
thermal dissipation problems. The tightly spaced electronic switches generate
huge amounts of heat, which has to be dissipated at a high speed. Such
problems will not arise in bio molecular devices.
Holographic associative Memories At present, serial memories dominate computer architectures.
Associative memories, which are used by molecular-based systems, take an
input and independent of the central processor, scan the entire memory for the
data block that matches exactly or with some tolerance and finally return the
data block or it’s memory address, which satisfies the matching criteria. Such
memories have significant potential in optical computer architectures,
optically coupled neural network computers, robotic vision hardware and
generic pattern systems.
3D Optical Memories A major disadvantage of the present computer chips is the 2-D
memory storage capacity .The 3-D addressing capability derives from the
ability to adjust the location of the irradiated volume in the three dimensions.
The other advantages of the molecular computing systems are : a higher
degree of integration, considerable lower switching energies, enhanced
stability of the circuits in presence of radiation, inherent precision and high
speed signal processing.
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Where is the work being done? What is the future?
The year 2000 has brought a renewed interest in
biomolecular computing, and money from the National Science Foundation
and U.S. Military has followed. The most logical applications will be in
Biology, Chemistry, Medicine, and the Military where scientists deal with
enormous amounts of data. There has been an amazing growth of knowledge
about how to compute with molecules and a wealth of theoretical models with
steadily accumulating laboratory experience, all of which serve to present
more challenges than solutions.
Researchers from Stanford and Princeton
universities, Richard J. Lipton, a computer-science professor at Princeton
University, Daniel Boneh, an assistant professor of computer science at
Stanford University, and Christopher T. Dunworth, a computer-science
doctoral student at Princeton, have outlined a way for a DNA computer to
crack messages coded with the U.S. government's own Data Encryption
Standard, which is used to protect a wide range of data, including telephone
conversations on classified topics and data transmissions between banks and
the Federal Reserve.
When a message is encrypted according to the
standard, the coding relies on one of 72 quadrillion "keys," or encoding
instructions. A message coded in this way is hard to crack, because there is no
way to know which specific key was used. Testing all possible keys on an
electronic computer would take an enormous amount of time, but a DNA
computer could test all of the keys at the same time, find the right one, and
pass it to a human code-breaker for use in translating the message. A highly
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automated version of a DNA computer might be able to produce the answer in
as little as two hours.
"About 15 groups are actively doing DNA computer research worldwide, and
most of these groups are looking for the right architectural features for doing
such molecular computations. DNA is just one molecule with which these
computers could eventually be made," (Adleman).
A massive study is due soon from Duke University's
John H. Reif, Professor of Computer Science and involves Princeton, NYU, U
of Penn, U of Delaware, Mt. Sinai, U of Wisconsin, U of Southern Calif,
Binghamton U, U of Rochester. The key tasks are experimental
demonstrations, nano-construction of new 3-D structures, applications, and
software tools. The most important task of the project is small to moderate
scale prototype demonstrations and implementations. This involves word(W)
design and register design for storage and retrieval of logical and numeric
information in massively parallel memories, where the registers encode large
amounts of binary data within distinct DNA strands. They are using word
designs and other methods to improve error resiliency. The experimental
demonstrations of BMC include: massively parallel execution of basic
operations such as logical and arithmetic operations, and the sequential
chaining of these operations.
Future research is aimed at developing a device
that can read out answers more easily, perhaps on a traditional computer
screen, especially if there is more than a single answer. Answers now must be
gleaned from the results by the scientist.
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One-pot reactions is another dream of molecular
computation. Scientists would like to be able to throw a bunch of reactants
together and watch them self-assemble without any more purification,
separation, or poking and prodding other than something simple like heat
cycling. The problem is that setting up the kinds of chemical reactions that are
rich enough to support computation is hard to do without cross-talk between
them. Cells manage to decrease cross-talk by compartmentalizing chemical
reactions physically with membranes or virtually by segregating them in the
nanoenvironments of enzymes.(Wisz)
Biomolecular computation, may have its biggest
impact in completely different ways -- for example, enabling a computing
system to read and decode natural DNA directly. Such a computer also might
be able to perform DNA fingerprinting -- matching a sample of DNA, such as
that in blood found at a crime scene, with the person from whom it came.
'The DNA computer might be a cost-effective way to decode the genetic
material of humans and other living things, and it might be able to create wet-
data-bases of DNA for research purposes that would eliminate the time-
consuming task of translating DNA into a form that can be stored in an
electronic computer. That could be the killer application for biomolecular
computation.' (Reif)
While most research is taking place at
universities, some companies are probing the potential of DNA computers.
NEC Corp.'s Research Institute in Princeton, N.J., for instance, has several
scientists working on DNA computing. Hewlett-Packard Co., in Palo Alto,
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Calif., is keeping tabs on six to 10 major projects, including Smith's work at
the U of Wisconsin.
Current biomolecular computing technology is still far
from overtaking the silicon chip. DNA computing is an infant discipline
looking to find a way into real-world applications and a dream for scientists...
a dream to harness the enormous data-storing capacity of DNA. It does seem
to be the first example of true nanotechnology, forging a link between
computational science and life science. Solutions take multidisciplinary teams
employing molecular biologists, mathematicians, computer scientists,
biochemists, and material engineers.
Interest in these computers nearly ebbed in late
1999, but has been renewed in 2000. In my lifetime computers filled entire
rooms and had to be programmed by manually rewiring. Since that time,
computers have become much smaller and easier to use. It is possible that
DNA computers will become more common for solving very complex
problems, and those of us alive now will remember when many could not
imagine that they would ever be practical.
"Practical uses of the technology will come later. The business history of
computation is that the capability comes before significant applications.
UNIVAC, the first commercially produced electronic computer in 1951, was
not a success in the marketplace. It took businesses such as IBM to start
inventing their own computers and finding new uses for them. "(Wood)
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NEW DEVOLOPMENTS
The first applications were "brute force" solutions
in which random DNA molecules were generated, and then the correct
sequence was identified. The first problems solved by DNA computations
involved finding the optimal path by which a travelling salesman could visit a
fixed number of cities once each. Recent work showed how DNA can be
employed to carry out a fundamental computer operation, addition of two
numbers expressed in binary." (Bancroft)
In January 2000, the Lloyd Smith team at the
University of Wisconcin, showed that DNA computing can be simplified by
attaching the molecules to a surface and then using them to tackle real and
complex problems. This 'solid surface' chemistry greatly simplifies the
complex and repetitive steps previously used in rudimentary DNA computers.
It takes DNA out of the test tube and puts it on a solid surface, making the
technology simpler, more accessible and more amenable to the development
of larger DNA computers. This one breakthrough revitalized the research
community.
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The Wisconsin approach uses a gold-plated
square of glass as something like to a conventional memory chip. As many as
a trillion individual strands of DNA would be anchored to the glass, each
strand containing information being stored in the DNA computer. The new
surface chemistry provides an opportunity for harnessing DNA to make the
biggest non-conventional computer yet. (Smith)
To use a solid surface the scientists must
immobilize a complete combinatorial set of single strand DNA oligomers onto
a surface. The surface will facilitate sample handling and simplify reactant-
product separation. There will however be a loss of information density in this
two-dimensional world and slower enzyme movement. This is why Adleman's
research remains in test tubes.
The n301ers will recognize some of the lingo
here. Information is stored in a NON-ADDRESSED array of 'DNA Words' of
a fixed length, 16mers (8 bits per word=256 or 16mers) These words (W) will
be linked together to form large combinatorial sets of molecules. (2300 copies
of each DNA or 64mer in 1 cm^2). To get a readout: perform enzymatic DNA
computations to remove most of the words from the surface (Here are those
restrictive enzymes.), amplify the remaining DNA words (PCR), and then
identify the remaining words by detecting PCR products on single word
arrays.
How do they know what the mathmatical answer is? You might be interested
in seeing this chart. It's somehow familiar; and yet, it isn't. We can see 16
strands of DNA and their enzyme sequence as it corresponds to the binary
numbers that represent 0 through 15. (Smith)
Biomolecular Computers Seminar Report 2004
Govt. Engg. College, Thrissur Dept.of Electronics & Communication
27
Biomolecular Computers Seminar Report 2004
Govt. Engg. College, Thrissur Dept.of Electronics & Communication
28
CONCLUSION Biomolecular computers have the real potential
for solving problems of high computational complexities and therefore, many
problems are still associated with this field. The difficulty of devising an
interface is therefore the sensitive dependence on a biological environment,
susceptibility to degradation, senescence and infection, etc. Nevertheless, it
offers the best approach to human cognitive equivalence. But like any
radically new technology, there is a daunting learning and manufacturing
curve that must first be overcome before these molecular devices can find a
practical use in everyday life. They are still five to ten years away from
becoming a commercial reality.
Biomolecular Computers Seminar Report 2004
Govt. Engg. College, Thrissur Dept.of Electronics & Communication
29
REFERENCES
1. Michael Conrad. ‘Molecular Computing: The Lock – Key Paradigm.’ Computer, vol25, 1992, p 11.
2. ‘DNA Computing on a chip’ by Mitsunori Ogihara & Animesh Ray. 3. Q Liu, et al . ‘DNA Computing on Surfaces’.
4. T H Cormer, et al. ‘Introduction to Algorithms’
5. Robert Birge. ‘Protein based Optical Computing Memories.’
6. A L Lehninger, et al. ‘Principles of Biochemistry’
7. Kirstof Sienicky. ‘Molecular Electronics & Molecular Electronic Devices’
Biomolecular Computers Seminar Report 2004
Govt. Engg. College, Thrissur Dept.of Electronics & Communication
30
ACKNOWLEDGEMENT
I thank God Almighty for the successful completion of my seminar.
Sincere feelings of gratitude for Prof. K.P. Indira Devi, Head of the
Department, Electronics & Communication Engineering. I express my
heartfelt gratitude to co-ordinator Smt. Muneera C.P. for her valuable advice
and guidance. I would also like to express my gratitude to all my respected
teachers.
I would like to thank my dear friends, for their kind-hearted cooperation
and encouragement.
AJITH DEVADAS
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