ajith bio molecular computer

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.

Biomolecular Computers Seminar Report 2004

Govt. Engg. College, Thrissur Dept.of Electronics & Communication

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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)



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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.



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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’



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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

ajith bio molecular computer

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