INDEX

1) INTRODUCTION TO BIOMOLECULAR COMPUTING 4

2) HAMILTON PATH PROBLEM 5

3) PROGRAMMING OF PROBLEM USING DNA 8

4) WORKING OF DNA ______________________________________________10

5) APPLICATION 17

6) EFFICIENCY___________________________________________________ 21

7) ADVANTAGES-DISADVANTAGES 22

8) FUTURE 24

9) CONCLUSION___________________________________________________ 29

FIGURES INDEX

2.1 Possible flight routes between seven cities________________6

2.2 Connecting Block___________________________________7

2.3 Double Helics_____________________________________ 7

2.4 Long Chain________________________________________7

2.5 Different Colour___________________________________ 7

3.1 Linking Pieces of DNA_______________________________ 9

3.2 Sequence of Sticky Pieces_____________________________9

4.1 Synthetic DNA_____________________________________11

4.2 Memory Unit Structure________________________________12

4.3 Complementary Coupling Nuclotide_______________________12

4.4 Coupling Rings Long Chain____________________________13

2

4.5 Complmentary Coupling_______________________________13

4.6 Double Helix Structure_______________________________13

4.7 Complementary Strands______________________________14

4.8 Split Doouble Stranded Enzymes_______________________14

4.9 Complementary Strands______________________________15

4.10 Copy Of Complementary Strands_____________________16

5.1 DNA MicroArray__________________________________20

3

1. INTRODUCTION TO BIO-MOLECULAR COMPUTING

Computer chip manufactures are furiously racing to make the next

microprocessor that will topple speed records. Sooner or later, though, this

competition is bound to hit a wall. Microprocessor made of silicon will

eventually reach their limits of speed and miniaturization. Chip makers need a

new material to produce faster computing speeds.

You won’t believe where scientists have found the new material they

need to build the next generation of microprocessors. Millions of natural

supercomputers exist inside living organisms, including your living body. They

are nothing else but Bio-Molecules itself. Especially DNA. DNA

(deoxyribonucleic acid) molecules, the material our genes are made of, have the

potential to perform calculations many faster than the world’s most powerful

human-built computers. The other Bio-Molecules like Nucleotides,

Nuclesoides, Saccharides, Lignin, Lipids, Amino acids…

What is a DNA Computer?

Research in the development of DNA computers is really only at its

beginning stages, so a specific answer isn't yet available. But the general sense

of such a computational device is to use the DNA molecule as a model for its

construction.

Although the feasibility of molecular computers remains in doubt, the

field has opened new horizons and important new research problems, both for

computer scientists and biologists. The computer scientist and mathematician

are looking for new models of computation to replace with acting in a test tube.

The massive parallelism of DNA strands may help to deal with

computational problems that are beyond the reach of ordinary digital computers

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-- not because the DNA strands are smarter, but because they can make many

tries at once. It's the parallel nature of the beast. For the biologist, the

unexpected results in DNA computing indicate that models of DNA computers

could be significant for the study of important biological problems such as

evolution. Also, the techniques of DNA manipulation developed for

computational purposes could also find applications in genetic engineering.

DNA computer can’t be still found at your local electronics store yet. The

technology is still in their development, and didn’t exist as concept before a

decade. In 1994, LEONARD ADELMAN introduced the idea of using DNA to

solve complex mathematical problems. Adelman, computer scientist at the

university of Southern California, came to the conclusion that DNA had

computational potential after reading the book “MOLECULAR BIOLOGY

OF THE GENE” written by JAMES WASTON, who co-discovered the

structure of DNA in 1953.In fact, DNA is more similar to computer. DNA is

very similar to a computer hard drive in how it stores permanent information

about your genes.

2. HAMILTON PATH PROBLEM

Adelman is often called the inventor of the DNA computers. His article in

a 1994 issue of Journal Science outlined how to use DNA to solve a well-

known mathematical problem, called the “Directed Hamilton Path problem”,

also known as the “Traveling Salesman Problem”. The goal of the problem is

to find the shortest route between a numbers of cities, going through each city

only once. As you add more cities the problem becomes more difficult.

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Figure 2.1 shows a diagram of the Hamilton path problem. The objective

is to find a path from start to end going through all the points only once. This

problem is difficult for the conventional (serial logic) computers because they

try must try each path one at a time. It is like having a whole bunch of keys and

trying to see which fits into the lock. Conventional computers are very good at

math, but poor at “key into lock” problems. DNA based computers can try all

the keys at the same time (massively parallel) and thus are very good at key into

lock problems, but much slower at simple mathematical problems like

multiplication. The Hamilton path problem was chosen because every key-into-

lock problem can be solved as a Hamilton Path Problem.

Figure 2.1

Figure showing the possible flight routes between the seven cities.

The following algorithm solves the Hamilton Path Problem, regardless of

the type computers used.

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. Because the graph has 7 cities, keep only those paths with 7 cities.

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4. Keep only those paths that enter all cities at least once.

5. Any remaining paths are solutions.

The key to solving the problem was using DNA to perform the five steps in

solving the above algorithm.

These interconnecting blocks can be used to model DNA:

Figure 2.2DNA likes to form long double helices:

Figure 2.3

The two helices are joined by “bases”, which will be represented

by coloured blocks. Each base binds only to one other specific base. In our

example, we will say that each coloured block will bind only with the block

of same colour. For example, if we only had red coloured blocks, they

would form a long chain like this:

Figure 2.4

Any other colour will not bind with red:

Figure 2.5

3.PROGRAMMING OF THE PROBLEM USING DNA

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STEP 1: Create a unique DNA sequence for each city A through G. For

each path, for example, from A to B, creates a linking pieces of DNA that

matches the last half of A and first half of B:

Figure 3.1

Here the red block represents the city a, while the orange block represents

the city B. the half-red half-orange block connecting the two other blocks

represents the path from A to B.

In a test tube, all different pieces of DNA will randomly link with each

other, forming paths through the graph.

STEP 2: Because it is difficult to "remove" DNA from solution, the target

DNA, the DNA which started from A and ended at G was copied over and

over again until the test tube contained a lot of it relative to other random

sequences. This is essentially the same as removing all the other pieces.

Imagine a sock drawer which initially contains one or two coloured socks. If

you put in a hundred black socks, the chances are that all you will get if

you reach in is black socks.

STEP 3: Going by weight, the DNA sequences which were 7 "cities" long

were separated from the rest. A "sieve" was used which would allow

smaller pieces of DNA to pass quickly, while larger segments are slowed

down. the procedure used actually allows you to isolate the pieces which are

precisely 7 cities long from any shorter or longer paths.

STEP 4: To ensure that the remaining sequences went through each of

cities, “sticky” pieces of DNA attached to magnets were used to separate the

DNA. The magnets were used to ensure that the target DNA remained in the 8

test tube, while the unwanted DNA was washed away. First, the magnets

kept all the DNA which went through city A in the test tube, then B, then C,

and D, and so on. In the end, the only DNA which remained in the tube was

that which went through all seven cities.

Figure 3.2STEP 5: all that was left to sequences the DNA, revealing the path from

A to B to C to D to E to F to G.

4. WORKING OF DNA

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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.

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Figure 4.1The 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.

Figure 4.2

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

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theoretically have 10 trillion calculations going on at the same time in very little

space.

The hardware key to biological life is a very stable chemicals called

nucleic acid. It is the equivalent of silicon computer system. It can exist in the

form of chains of molecules known as nucleotides. There are only four different

nucleotides but each of them have a pair of complementary coupling points due

to an element of oxygen on one side and a phosphate site on the other. The

oxygen atom of any of the four nucleotides can bind to the phosphate site of any

other.

Figure 4.3

Nucleotides have an affinity to stick together due to a chemical formation at

each side which provides a physical coupling supplemented by

electromagnetic attraction

This ability of the nucleotides to bind side by side allows them to form

long chains without it mattering which type of nucleotide is binding to which

other type (see figure 3.2).

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Figure 4.4

DNA can form digital strings of information because the four nucleotides

cannot be linked to each other in any order

A long string of these four bases can thus contain a massive amount of

information. The nucleotides also have another pair of complementary

coupling sites which, from a hardware point of view, give DNA other very

important characteristics. They allow each nucleotide to link up to a third

nucleotide. These extra binding sites are not universal coupling points like the

chain building coupling sites, these binding sites allow only specific pairs of

nucleotides to bond - A will bind with T and T with A; C will bind with G and

G with C. These specific coupling pairs are illustrated in figure 3.3.

Figure 4.5

Nucleotides have additional binding sites which attract specific

complementary nucleotides to bind to them

13

This extra bonding site allows the formation of double strand, with one

strand being the complement of other. This forms the famous "double helix"

structure which carries genetic code.

Figure 4.6The complementary coupling sites allow strings of DNA to form into double

strands with one strand being the complement of the other. The two

strands form into a double helix

The first advantage of the double strand structure is the increased stability

it provides. Although nucleotide bonding is quite secure there is so much

jostling in the environment of a cell that individual nucleotides can get

displaced.

A double stranded structure of complementary strands allows

damaged sections of the strand to be repaired by referring to the

complement nucleotides

Figure 4.7

Enzymes can split a double stranded DNA into a two chain of nucleotides

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Figure 4.8

The splitting process forms two separate chains with one the complement of

the other

Figure 4.9

The two halves of a DNA section which has been split down the middle

can each rapidly build an additional complementary strand. These results in the

splitting operation producing two copies of the original (see figure 3.8).

Figure 4.10

The splitting of strands and then regrowing the complementary

strands results in the original strand being copied and is exactly analogous

to the way in which binary data is copied within a computer program.

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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 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 16

be searched at once in a molecular system 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.

5.APPLICATIONS

DNA2DNA Applications

Another area of DNA computation exists where conventional computers

clearly have no current capacity to compete is the concept of DNA2DNA

computations as suggested 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 re-coding 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:

DNA sequencing;

DNA fingerprinting;

17

DNA mutation detection or population screening;

Other fundamental operations on DNA.

In the case of DNA mutation detection, the strand being operated on

would already be partially known and therefore fewer steps would need to be

taken to re-code the DNA into a redundant form applicable for computational

form.

There are other models of DNA computation that suggest that DNA

might be used to detect and evaluate other chemical and biochemical

substances. It is suggested that nucleic acid structures, could play an important

role in molecular computation. Various shapes of folded nucleic acid can be

used to detect the presence of drugs, proteins, or other molecules.

Engineered riboenzymes could be used as operators to affect rewrite rules

and to detect the presence of such non-nucleic acid molecules. Using these

structures and operators to sense levels of substances, it would then be possible

to compute an output readable using proposed biosensors that detect

fluorescence or polarization. These biosensors could potentially allow

communication between molecular sensory computers and conventional

electronic computers.

Implications to Biology, Chemistry, and Medicine

While the development of DNA computational methods may have many

directly applicable applications, the biggest contribution of research in this area

may be much more fundamental and will likely fuel many indirect benefits. In

many papers, it is stressed that high levels of collaboration between academic

disciplines will be essential to affect progress in DNA computing. Such 18

collaboration may very well lead to the development of a DNA computer with

practical advantages over a conventional computer but has an even greater

likelihood of contributing to an increased understanding of DNA and other

biological mechanisms. The need for additional precision could affect progress

in biomolecular techniques by placing demands on biochemists and their tools

that might not otherwise be considered.

A particular area within the natural and applied sciences that may benefit

from advances in - DNA computation is combinatorial chemistry.

Combinatorial chemistry involves the construction of enzymes, sequences of

RNA, and other molecules, for use in biomolecular engineering or medicine.

The combinatorial chemistry involves generating large sets of random RNA

sequences and searches for molecules with the desired properties. Advances in

either area could easily benefit the other field or even pave a way to combining

the two fields, producing both products and related computational results in

parallel.

Several papers also extend the use of biomolecular computing into

applications in the emerging science of nanotechnology, specifically nano-

fabrication, making use of both the small scale computational abilities of DNA

and the manufacturing abilities of RNA. Since both fields are still very

embryonic, the practical or even experimental implementation of this use is still

highly speculative but promising.

Applying the techniques of DNA2DNA computing could result in

improved laboratory interfaces capable of performing computations prior to

input into conventional computers and may lead to improved methods of

sequencing DNA by motivating the use of magnetic beads, optical scanners, and

other emerging techniques that may allow DNA to be read directly into an

electronic interface.

DNA MICROARRAY19

Figure 5.1

DNA microarray, or DNA chips are fabricated by high-speed robotics,

generally on glass but sometimes on nylon substrates, for which probes* with

known identity are used to determine complementary binding, thus allowing

massively parallel gene expression and gene discovery studies. An experiment

with a single DNA chip can provide researchers information on thousands of

genes simultaneously - a dramatic increase in throughout.

There are two major application forms for the DNA microarray

technology: 1) Identification of sequence (gene / gene mutation); and 2)

Determination of expression level (abundance) of genes.

6.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,

20

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 enourmous 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.

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.

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 back bone for stability, have also been recommended.

7. ADVANTAGES & DISADVANTAGES

ADVANTAGES21

There are several advantages to using DNA instead of silicon:

Perform millions of operations simultaneously;

Generate a complete set of potential solutions;

Conduct large parallel searches; and

Efficiently handle massive amounts of working memory.

As long as there are cellular organisms, there will always be a supply of

DNA.

The large supply of DNA makes it a cheap resource.

Unlike the toxic materials used to make traditional microprocessors,

DNA biochips can be made cleanly.

DNA computers are many times smaller than today's computers.

Storage and Associative Memory

DNA might also be used to mirror, and even improve upon, the

associative capabilities of the human brain. A 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. The use of this

technique would replicate what is thought by many to be a key factor in

human intelligence.

Baum has further speculated that a memory could be constructed where

only portions of the data are content addressable and associative, with other

information on an object compactly stored in addresses relative to the

22

associative portion of the entry. To save on operating costs and reduce error

frequency, this portion of the memory could be kept in double stranded form.

DISADVANTAGES

These models also have some of the following drawbacks:

Each stage of parallel operations requires time measured in hours or days,

with extensive human or mechanical intervention between steps.

Generating solution sets, even for some relatively simple problems, may

require impractically large amounts of memory.

Many empirical uncertainties, including those involving: actual error

rates, the generation of optimal encoding techniques, and the ability to

perform necessary bio-operations conveniently in vitro or in vivo.

8. FUTURE

DNA computing is -few years old (November 11, 1994), and for this

reason, it is too early for either great optimism of great pessimism. Early

computers such as ENIAC filled entire rooms, and had to be programmed by

punch cards. Since that time, computers have since become much smaller and

easier to use. DNA computers will become more common for solving very

complex problems; Just as DNA cloning and sequencing were once manual

tasks, DNA computers will also become automated.

23

In addition to the direct benefits of using DNA computers for performing

complex computations, some of the operations of DNA computers already have,

and perceivably more will be used in molecular and biochemical research.

DNA's Role in Computer Science

The study of DNA as both a natural storage medium, and as a tool of

computation, could shed new light on many areas of computer science. Despite

the high error rates encountered in DNA computing, in nature DNA has little

understood but resilient mechanisms for maintaining data integrity. By studying

genetic code for these properties, new principles of error correction may be

discovered, having, use within conventional electronic computation in addition

to the new biomolecular paradigms.

With the advent of DNA computational paradigms, especially those

directed towards improved methods of solving NP-hard problems, will come an

increased understanding about the limitations of computation. DNA based

computers may postpone certain expected thermodynamic obstacles to

computation as well as exploring the limitations of Turing machines and

questioning theories of computation based on electronic and mechanical models.

Developments in parallel Processing algorithms to take advantage of the

massive parallelism of "classic" DNA based computation may serve as a

foundation for future developments in other parallel processing architectures

using more conventional electronic computers

DNA based computing may also be able to contribute to other

unconventional areas of computation. Through the use of its massive

parallelism and potentially non-deterministic mechanisms, DNA based models

of computation might be useful for simulating or modeling other emerging 24

computational paradigms, such as quantum computing, which may not be

feasible until much later. As well, the trials and tribulations experienced by

DNA computing researchers may very well be related to the future obstacles

expected to face other revolutionary ideas within computer science and

information systems.

Future Outlook

With so many different methods and models emerging from the current

research, DNA computing can be more accurately described as a collection of

new computing paradigms rather than a single focus. Each of these different

paradigms within biomolecular computing can be associated with different

potential applications that may prove to place them at an advantage over

conventional methods. Many of these models share certain features that lend

them to categorization by these potential advantages. However, there exist

enough similarities and congruencies that hybrid models will be possible, and

that advances made in both "classic" and "natural" areas of DNA computing

will be mutually beneficial to both areas of research. Advancements in DNA

computing may also serve to enhance understanding of both the natural and

computer sciences. For these reasons, and due to the many areas dependent on

each of computer science, mathematics, natural science, and engineering,

continued interdisciplinary collaboration is very important to any future

progress in all areas of this new field.

A "killer app" is yet to be found for DNA computation, but might exist

outside the bulk of current research, in the domain of DNA2DNA applications

and other more natural models and applications of manipulated DNA. This

direction is particularly interesting because it is an area in which DNA based

solutions are not only an improvement over existing techniques, but may prove

25

to be the only feasible way of directly solving such problems that involve the

direct interaction with biological matter.

On the "classical" front, problem specific computers may prove to be the

first practical use of DNA computation for several reasons. First, a problem

specific computer will be easier to design and implement, with less need for

functional complexity and flexibility. Secondly, DNA computing may prove to

be entirely inefficient for a wide range of problems, and directing efforts on

universal models may be diverting energy away from its true calling. Thirdly,

the types of hard computational problems that DNA based computers may be

able to effectively solve are of sufficient economic importance that a dedicated

processor would be financially reasonable. As well, these problems will be

likely to require extensive time they would preclude the need for a more

versatile and interactive system that may be able to be implemented with a

universal computing machine.

Even if the current difficulties found in translating theoretical DNA

computing models into real life are never sufficiently overcome, there is still

potential for other areas of development. Future applications might make use of

the error rates and instability of DNA based computation methods as a means of

simulating and predicting the emergent behavior of complex systems. This

could pertain to weather forecasting, economics, and lead to more a scientific

analysis of- social science and the humanities. Such a system might rely on

inducing increased error rates and mutation through exposure to radiation and

deliberately inefficient encoding schemes. Similarly, methods of DNA

computing might serve as the most obvious medium for use of evolutionary

programming for applications in design or expert systems. DNA computing

might also serve as a medium to implement a true fuzzy logic system.

26

9. CONCLUSION:

DNA computers will become more common for solving very

complex problems; Just as DNA cloning and sequencing were once manual

tasks, DNA computers will also become automatedes. Studying DNA

computers may also lead us to a better future enhancement.

With so many possible advantages over conventional techniques, DNA

computing has great potential for practical use. Future work in this field should

begin to incorporate cost-benefit analysis so that comparisons can be more

appropriately made with existing techniques and so that increased funding can

be obtained for this research that has the potential to benefit many circles of

science and Industry.

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