Biological Computer-Seminar report

Seminar Report’04 Biological Computers

INTRODUCTION

Biological computers have emerged as an interdisciplinary field that

draws together molecular biology, chemistry, computer science and mathematics.

The highly predictable hybridization chemistry of DNA, the ability to completely

control the length and content of oligonucleotides, and the wealth of enzymes

available for modification of the DNA, make the use of nucleic acids an

attractive candidate for all of these nanoscale applications

A ‘DNA computer’ has been used for the first time to find the only correct

answer from over a million possible solutions to a computational problem.

Leonard Adleman of the University of Southern California in the US and

colleagues used different strands of DNA to represent the 20 variables in their

problem, which could be the most complex task ever solved without a

conventional computer. The researchers believe that the complexity of the

structure of biological molecules could allow DNA computers to outperform

their electronic counterparts in future.

Scientists have previously used DNA computers to crack computational

problems with up to nine variables, which involves selecting the correct answer

from 512 possible solutions. But now Adleman’s team has shown that a similar

technique can solve a problem with 20 variables, which has 220 - or 1 048 576 –

possible solutions.

Adleman and colleagues chose an ‘exponential time’ problem, in which

each extra variable doubles the amount of computation needed. This is known as

an NP-complete problem, and is notoriously difficult to solve for a large number

of variables. Other NP-complete problems include the ‘travelling salesman’

problem – in which a salesman has to find the shortest route between a number

of cities – and the calculation of interactions between many atoms or molecules.

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Adleman and co-workers expressed their problem as a string of 24

‘clauses’, each of which specified a certain combination of ‘true’ and ‘false’ for

three of the 20 variables. The team then assigned two short strands of specially

encoded DNA to all 20 variables, representing ‘true’ and ‘false’ for each one.

In the experiment, each of the 24 clauses is represented by a gel-filled

glass cell. The strands of DNA corresponding to the variables – and their ‘true’

or ‘false’ state – in each clause were then placed in the cells.

Each of the possible 1,048,576 solutions were then represented by much

longer strands of specially encoded DNA, which Adleman’s team added to the

first cell. If a long strand had a ‘subsequence’ that complemented all three short

strands, it bound to them. But otherwise it passed through the cell.

To move on to the second clause of the formula, a fresh set of long strands

was sent into the second cell, which trapped any long strand with a

‘subsequence’ complementary to all three of its short strands. This process was

repeated until a complete set of long strands had been added to all 24 cells,

corresponding to the 24 clauses. The long strands captured in the cells were

collected at the end of the experiment, and these represented the solution to the

problem.

THE WORLD’S SMALLEST COMPUTER

The world’s smallest computer (around a trillion can fit in a drop of

water) might one day go on record again as the tiniest medical kit. Made entirely

of biological molecules, this computer was successfully programmed to identify

– in a test tube – changes in the balance of molecules in the body that indicate the

presence of certain cancers, to diagnose the type of cancer, and to react by

producing a drug molecule to fight the cancer cells.

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DOCTOR IN A CELL

In previous biological computers produced input, output and “software”

are all composed of DNA, the material of genes, while DNA-manipulating

enzymes are used as “hardware.” The newest version’s input apparatus is

designed to assess concentrations of specific RNA molecules, which may be

overproduced or under produced, depending on the type of cancer. Using pre-

programmed medical knowledge, the computer then makes its diagnosis based

on the detected RNA levels. In response to a cancer diagnosis, the output unit of

the computer can initiate the controlled release of a single-stranded DNA

molecule that is known to interfere with the cancer cell’s activities, causing it to

self-destruct.

In one series of test-tube experiments, the team programmed the computer

to identify RNA molecules that indicate the presence of prostate cancer and,

following a correct diagnosis, to release the short DNA strands designed to kill

cancer cells. Similarly, they were able to identify, in the test tube, the signs of

one form of lung cancer. One day in the future, they hope to create a “doctor in a

cell”, which will be able to operate inside a living body, spot disease and apply

the necessary treatment before external symptoms even appear.

The neuron is a functional unit of the body's nervous system that transmits

electro-chemical impulses through the system. These electrochemical impulses

are the way information is exchanged in our bodies. Think of the neurons as

phone or network lines that make up the Internet and the electrochemical

impulses as e-mail, and you get idea. Just as e-mail is used to send messages

between people, electrochemical impulses are used to send message between

different body parts

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http://library.thinkquest.org/19314/glossary.htm#electro-chemical


The Biological Bits and Bytes of DNA Computing

Computers use 1 or 0 and DNA uses 1 ,2 ,3 and four. Or in the geneticists

jargon A C T G. We are built of proteins , each protein is assembled from

twenty different building blocks called amino acids. The order in which these

amino acids are assembled is obtained from the sequence contained in DNA. I

am told this leads to 64 different combinations.

DNA computers take advantage of DNA's physical

properties to store information and perform calculations. In a

traditional computer, data are represented by and stored as strings of zeros and

ones. With a DNA computer, a sequence of its four basic nucleotides — adenine,

cytosine, guanine, and thymine — is used to represent and store data on a strand

of DNA. Calculations in a traditional computer are performed by moving data

into a processing unit where binary operations are performed. Essentially, the

operations turn miniaturized circuits off or on corresponding to the zeros and

ones that represent the string of data. In contrast, a DNA computer uses the

recombinative properties of DNA to perform operations.

Guinness Book of World Records

The computer is listed in the 2004 Guinness Book of World Records as

the world's smallest biological computing device. Prof Shapiro's device is a

development of a biological computer that he first built in 2001. DNA is the

software of life: it carries huge quantities of information, programs the operating

system of every cell, controls the growth of the whole organism and even

supervises the making of the next generation. The first biological computers

were used to make mathematical calculations. They may not outperform silicon-

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based technology in the world of banking, aviation and databases, but the

Weizmann team realised that they might be agents of medical treatment.

They could be provided with specific `search and destroy' programmes,

administered as drugs, and delivered by the bloodstream to autonomously detect

disease in cells. They could even be used in late-stage cancer, to detect and

prevent secondary growths That is the dream. However, the biology in the latest

experiments was hugely simplified: the little machine identified cancer

molecules in a sterile saline solution in a laboratory under ideal conditions.

To actually track down and disable cancer cells in a human body, it would

have to survive the hurly-burly of proteins, lipids, polysaccharides and nucleic

acids, any of which could block or disable it. "There could be many reactions

with many other molecules that may be detrimental to either the computer or the

cell in which it operates,'' said Prof Shapiro.

COMPUTERS & BIOLOGICAL COMPLEXITY

The mathematics of DNA has a complexity of 2^64 and before you say

no, try to say 18446744073709551616 and mean it. The point of this is, the

makers of the chip that is at the heart of the common or garden PC are casting the

dies for the next generation of CPUs namely a 64 bit data bus. There are two

ways of looking at computer architecture. The simplest way to understand this ,

is to imagine a road and imagine how many cars you could get to travel along it

in an hour. To increase the traffic on the road you either widen it or make the

cars go faster or make smaller cars. The next generation of personal computers

will have a complexity matching our own. I say this because of genetic

algorithms. The fact that most of today's computers are linked up via the internet

makes it more likely that once change starts to happen, it will happen fast .

There is an awful similarity between DNA and machine code. The

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complexity of our machine code gets to the complexity of our DNA. 64 bit

processors have twice the exponental complexity 32 bit processors have.

This computer is able to run approximately one billion operations per

second with 99.8% accuracy. Astonishingly, one billion of these biological

computers could fit inside a drop of water!

Computing Device to Serve As Basis for Biological Computer

But their aim is to devise a new generation of fast and flexible computers

that can work out for themselves how to solve a problem, rather than having to

be told exactly what to do.

This computer may serve as a model in constructing a programmable

computer of subcellular size, that may be able to operate in the human body and

interact with the body's biochemical environment, thus having far-reaching

biological and pharmaceutical applications. Such a computer could sense

anomalous biochemical changes in the tissue and decide, based on its program,

what drug to synthesize and release in order to correct the anomaly.

The theoretical Turing machine consists of a potentially infinite tape

divided into cells, each of which can hold one symbol, a read/write head, and a

control unit which can be in one of a finite number of states. The operation of the

machine is governed by a finite set of rules that constitute its "software

program." In each cycle the machine reads the symbol in the cell located under

the read/write head, writes a new symbol in the cell, moves the read/write head

one cell to the left or to the right, and changes the control state, all according to

its program rules. A Future Interactive Biological Computer The computer

design may allow it to respond to the availability and to the relative

concentrations of specific molecules in its environment, and to construct

program-defined polymers, releasing them into the environment. If implemented

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using biomolecules, such a device may operate in the human body, interacting

with its biochemical environment in a program- controlled manner.

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COMPUTING WITH LEECHES

Neurons are the body's wires that transmit signals in the brain and

throughout the nervous system. Putting neurons into semiconductor circuits

could create the basis for a new breed of computer-brainlike systems that finally

live up to their name. Like the brain, neurosilicon computers might find solutions

on their own, with no need for programmers to write explicit step-by-step

instructions.

The researchers joined the neurons and linked them to a personal

computer, which sent signals representing different numbers to each cell. Using

principles of chaos theory, Ditto selectively stimulated the two neurons. From

the chatterbox traffic that followed, the PC extracted the correct answer to a

simple addition problem.

Neurons only have to be directed toward the answer and they will work

out their own way of solving the problem, Ditto says. This is the first time

invertebrate brain cells have used chaos to do arithmetic, let alone communicate

the results to humans.

What’s more, computer simulations by Ditto and Sudeshna Sinha at the

Institute of Mathematical Sciences in Madras, India, show that larger clusters of

neurons should also be able to do multiplication and Boolean logic operations,

the underlying principle of digital computers.

The Leechulator

A group of scientists from Emory University and Georgia Tech made a

calculator (called the "leech-ulator") with neurons taken from leeches. In

normal silicon computers, connections are made between the computer's chips

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http://www.physics.gatech.edu/chaos/research/neuron.html

http://www.physics.gatech.edu/chaos/tutorial/history3.htm


only when the programmer directs the connections to occur. However, in a

biological computer the neurons are able to connect on their own and are often

said to be "thinking" by making connections with their neighbors, possibly

increasing computational power. Since the processing power of the silicon chip

is close to being maximized, the next generation of computer technology may

rely on the use of biological computing. A billion operations per second is

impressive indeed, but when the prospect of massive neural connectivity is

considered, the speed is almost unfathomable.

The device the team has built can "think for itself" because the leech

neurons are able to form their own connections from one to another. Normal

silicon computers only make the connections they are told to by the programmer.

This flexibility means the biological computer works out it own way of

solving the problem. "With the neurons, we only have to direct them towards the

answer and they get it themselves," This approach to computing is particularly

suited to pattern recognition tasks like reading handwriting, which would take

enormous amounts of power to do well on a conventional computer.

The neurons are harnessed in a Petri dish by inserting micro-electrodes

into them. Each neuron has its own electrical activity and responds in its own

way to an electrical stimulus.

These features can be used to make each neuron represent a number.

Calculations are then performed by linking up the individual neurons. Leech

neurons are used because they have been extensively studied and are well

understood. Though much simpler, the neuron computer works in a similar way

to the human brain. Professor Ditto says a robot brain is his long-term aim,

noting that conventional supercomputers are far too big for a robot to carry

around

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Each neuron's electrical activity corresponds to a number

The "leech-ulator" demonstrates that the ability of neurons to make local

connections might be an advantage on which artificial intelligence could

capitalize.

Living computer: interconnected leech neurons can add up

Bill Ditto views his computer wetware

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LIVING COMPUTERS VS. TODAY'S HARDWARE

At this stage, Ditto says, it's just too early to tell if biological computers

will have inherent limitations. But he is optimistic that biosilicon systems can

tackle anything today's hardware can, plus sensory-based computing that only

biological "wetware" does with ease, such as understanding human language.

"We may ultimately be able to do it all with electronics and silicon," Ditto

said."Right now though we are using the living computer portion because we

don't quite understand how these neurons behave well enough to recreate them in

silicon. So instead we are using actual living tissue, which we know can do the

job and do it very quickly."One of the most enticing aspects of a living computer

is its ability to recover, much like the human body, and improve its performance

on its own.

Another aspect of the living computer would involve its integration into

biological systems such as the human body. A bio-computer, interfacing into the

nervous system, could help people control robotic limbs or improve poor vision,

hearing or touch, Ditto said.

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DNA COMPUTING

Hamilton path problem

In the DNA computing approach to this problem, each city was

represented by a unique strand of DNA that stretched 20 nucleotides long. Each

possible route between any two cities was represented by another 20-nucleotide-

long strand. This strand connecting cities was also related to the nucleotide

sequence of each city connected by that route. For example, the route between

City 1 and City 2 consisted of two sets of 10 nucleotides; the first 10 nucleotides

on the strand's route complemented the last 10 nucleotides of City 1, while the

second 10 nucleotides on the strand's route complemented the first 10

nucleotides of City 2.

In this way, if strands for cities 1 and 2 came in proximity to the route 1-

to-2 strand, the three strands would bind. To solve the traveling salesman

problem, strands representing all seven cities and strands representing all

possible routes between any two cities were placed in a test tube. The end result

was a series of longer, recombined strands. The correct answer was contained in

a strand that started with City 1, ended with City 7, contained the strands of all

seven cities, and had no one city represented more than one time in this longer

strand. When the experiment was first done in 1994, the complexity of finding

the strand with the correct answer was considered a downside to DNA

computing.

DNA works in nature is a form of Turing Machine and such a machine

can be used to solve computational problems. He therefore devised a way of

applying DNA manipulation techniques to the "Hamilton Path Problem" - for

several cities, some of which are connected by non-stop flights, does a path exist

to travel from A to B which passes through every other city once and only once?

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When the number of cities gets to around one hundred it could take

hundreds of years of conventional computer time to solve the problem, even with

the most advanced parallel processing available.

Adleman developed a method of manipulating DNA which, in effect,

conducts trillions of computations in parallel. Essentially he coded each city and

each possible flight as a sequence of 4 components. For example he coded one

city as GCAG and another as TCGG.

The incredible thing is that once the DNA sequences had been created he

simply "just added water" to initiate the "computation": The DNA strands then

began their highly efficient process of creating new sequences based on the input

sequences.

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If an "answer" to the problem for a given set of inputs existed then it

should amongst these trillions of sequences. The next (difficult) step was to

isolate the "answer" sequences. To do this Adleman used a range of DNA tools.

For example, one technique can test for the correct start and end sequences,

indicating that the strand has a solution for the start and end cities. Another step

involved selecting only those strands which have the correct length, based on the

total number of cities in the problem (remembering that each city is visited

once).

Finally another technique was used to determine if the sequence for each city

was included in the strand. If any strands were left after these processes then:

a solution to the problem existed, and

the answer(s) would be in the sequence(s) on the remaining strands.

His attempt at solving a seven-city, 14 flight map took seven days of lab work.

This particular problem can be manually solved in a few minutes but the key point

about Adleman's work is that it will work on a much larger scale, when manual or

conventional computing techniques become overwhelmed. "The DNA computer

provides enormous parallelism... in one fiftieth of a teaspoon of solution approximately

10 to the power 14 DNA 'flight numbers' were simultaneously concatenated in about

one second".

This approach to computing is particularly suited to pattern recognition

tasks like reading handwriting, which would take enormous amounts of power to

do well on a conventional computer.

Working principle

'The living cell contains incredible molecular machines that manipulate

information-encoding molecules such as DNA and RNA in ways that are

fundamentally very similar to computation,' says Prof. Shapiro of the Institute's

Computer Science and Applied Mathematics Department and the Biological

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Chemistry Department. 'Since we don't know how to effectively modify these

machines or create new ones just yet, the trick is to find naturally existing

machines that, when combined, can be steered to actually compute.'

Turing machine

A finite automaton that detects whether a list of 0's and 1's has an even

number of 1's. Benenson came up with a solution using DNA molecules and two

naturally occurring DNA-manipulating enzymes: Fok-I and Ligase. Operating

much like a biological editing kit, Fok-I functions as a chemical scissors,

cleaving DNA in a specific pattern, whereas the Ligase enzyme seals DNA

molecules together.

As the lab work progressed, Shapiro and his team realized that the

automaton they built could be programmed to perform different tasks by

selecting different subsets of the molecules realizing the eight possible rules of

operation controlling the performance of a two-state, two-symbol finite

automaton. The software molecules, together with two 'output display' molecules

used to visualize the final result of the computation, can be used to create a total

of 765 software programs. Several of these programs were tested in the lab,

including the 'even 1's checker' and the '0's before 1's' test mentioned above, as

well as programs that check whether a list of 0's and 1's has at least (or at most)

one 0, and whether it both starts with a 0 and ends with a 1.

The nanocomputer created by Shapiro's team uses the four DNA bases

known as A, G, C and T, to encode the input data as well as the program rules

underlying the computer 'software.' Both input and software molecules are

designed to have one DNA strand longer than the other, resulting in a single-

strand overhang called a 'sticky end.' Two molecules with complementary sticky

ends can temporarily stick to each other (a process known as hybridization),

allowing DNA Ligase to permanently seal them into one molecule. The sticky

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end of the input molecule encodes the current symbol and the current state of the

computation, whereas the sticky end of each 'software' molecule is designed to

detect a particular state-symbol combination. A two-state, two-symbol

automaton has four such combinations. For each combination the nanocomputer

has two possible next moves, to remain in the same state or to change to the

other state, allowing eight software molecules to cover all possibilities.

So how does such a computer work? Let's say the computer wants to

know whether the number of times a "B" DNA type appears in a DNA section is

odd or even. The DNA section - the "input" - is inserted into a chemical solution

along with enzymes to be used as the computer's "hardware". Other DNA

sections are added, which act as "software".

A software section sticks to the input with the help of an enzyme. If the

tip of the input is "B", the input will be labeled as having an odd number of "B"

sequences. Another enzyme then cuts the section and reveals the next sequence.

Each time "B" appears, the label on the input changes from "even" to "odd" and

back again. Once the computer has dissected the entire input, it can determine

whether the sequence appeared an even or odd number of times according to the

last label - the "output".

The computer can also do other calculations, such as checking whether

the "B" sequence appears at least once, or at most once, by inserting different

types of software DNAs.

Output Display

In each processing step the input molecule hybridizes with a software

molecule that has a complementary sticky end, allowing Ligase to seal them

together using two ATP molecules as energy. Then comes Fok-I, detecting a

special site in the software molecule known as the recognition site. It cleaves the

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input molecule in a location determined by the software molecule, thus exposing

a sticky end that encodes the next input symbol and the next state of the

computation. Once the last input symbol is processed, a sticky end encoding the

final state of the computation is exposed and detected, again by hybridization and

ligation, by one of two 'output display' molecules. The resulting molecule, which

reports the output of the computation, is made visible to the human eye in a

process known as gel electrophoresis.

DNA'S LIMITLESS POTENTIAL

Though they are simple, the bio-computers can perform certain functions

very quickly. For example, scientists said they might be able to speed up the

time-consuming study of DNA by forming the basis of computers capable of

screening DNA libraries in parallel, without sequencing each molecule as is

required now. DNA has vast memory capacity -- the trick is to exploit its nearly

limitless potential.

"The living cell contains incredible molecular machines that manipulate

information-encoding molecules such as DNA and RNA in ways that are

fundamentally very similar to computation," Shapiro said. "Since we don't know

how to effectively modify these machines or create new ones just yet, the trick is

to find naturally existing machines that, when combined, can be steered to

actually compute," he added.

Molecular-Level Treatment

The bio-computer uses two naturally occurring enzymes that can

manipulate DNA as its "hardware." The "software" and "hardware" molecules,

when mixed in a solution, operate in harmony on what is known as the "input"

molecule. What results is a simple mathematical computing machine known as a

"finite automaton." It then can be programmed to perform simple tasks by

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choosing different "software" molecules to be mixed in solution. "Such machines

might analyze natural DNA, human or otherwise, in the lab within a few years,"

Shapiro said. "The time when such machines can actually operate within the

human body, programmed with medical knowledge so that they can effect some

medical treatment at the molecular level, is at least a few decades away

Computing Comes to Life

The idea of a bacterial computer is not in itself quite so outlandish as it

may seem on first acquaintance. In principle, computing machines can be made

out of almost anything, and there is no reason that lipid sacs of proteins and

nucleic acids should not also qualify as computer building blocks. From the lofty

and austere perspective of computer science, an agar plate coated with

microscopic bacteria is not much different from a silicon wafer etched with

microscopic transistors. If the components can store and manipulate information

in a few basic ways, they can compute.

Can biocomputer engineers cope with all the distinctive failure modes of

living organisms—disease, predation, parasitism, senescence, death? (In this

context the threat of a computer virus is more than a metaphor!) It's fair to say

that practical applications of biological computers are a long way off. And yet

skeptics might keep in mind that the historical record of domestications is a vast

catalogue of unlikely-seeming successes

Biological 'computer' fits inside a drop of water

Scientists in Jerusalem have created a 'biological computer' small enough

to fit inside a drop of water. It uses enzymes as hardware and DNA molecules as

software. The nanocomputer contains a trillion living cells and it is hoped such a

device may one day act as an automatic doctor inside patients. The device's

creators say the trillion cells, acting together, can perform a billion operations per

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second, with 99.8% accuracy. The trillion cells require less than a billionth of a

watt of power to operate.

ADVANTAGES

There are several advantages to using DNA instead of silicon:

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.

DNA will make computers smaller than any computer that has come

before them, while at the same time holding more data

Unlike conventional computers, DNA computers perform calculations

parallel to other calculations. Conventional computers operate linearly,

taking on tasks one at a time. It is parallel computing that allows DNA to

solve complex mathematical problems in hours, whereas it might take

electrical computers hundreds of

Diagnosis of cancer

Application in biological & pharmaceutical applns

Recoverability

There are a lot of things that are always going to happen,accidents,

different conditions, something that's going to fool your computer program, but a

biological computer can actually heal itself, adapt and get better at what it does

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DISADVANTAGES

Biological computers (literally) have a few bugs that need to be worked

out before they start appearing regularly in mail order catalogues. For one thing,

while they can do rudimentary sort of calculation, input/output is exceedingly

slow. That slimy blob would take a long time to do something as simple as

balance your checkbook, and it would need regular feedings as well.

Silicon computers can switch between calculations. But you would have

to construct a biological computer anew for each problem.

The electronics behind your computer chips run at almost the speed of

light. Transistors are limited by "gating time," which is how long it takes the gate

to open and close when you apply voltage. The gates of transistors composing

chips now on the market are 130 nanometers (really small), which make them

fast and power efficient.

But a biological computer is limited by diffusion, a relatively slow

process. Plus, cells need a medium in which to grow. That biological computer

could be a gooey mess.

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APPLICATIONS

There are currently several research disciplines driving towards the

creation and use of DNA nanostructures for both biological and non-biological

applications. These converging areas are:

The miniaturization of biosensors and biochips into the nanometer scale

regime,

The fabrication of nanoscale objects that can be placed in intracellular

locations for monitoring and modifying cell function,

The replacement of silicon devices with nanoscale molecular-based

computational systems, and

The application of biopolymers in the formation of novel nanostructured

materials with unique optical and selective transport properties.

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CONCLUSION

Biological software for computers of the future

Researchers from the Weizmann Institute in Rehovot and the Technion in

Haifa have developed a computer so tiny that a trillion of them could fit into a

laboratory test tube. The "computers" are biological molecules, using DNA for

software and enzymes for hardware, and can solve a billion mathematical

problems a second.

Such tiny devices could one day fit into cells and supervise

biological processes, or even synthesize drugs. DNA strands exist in

almost every body cell - they are biological software that tell each cell

and molecule what to do.

"If you look at the mechanism of a cell, a lot of what goes on inside

is computation. We don't need to teach the cell new tricks, we just need

to put the existing tricks in the right order," says Prof. Ehud Shapiro of

the Weizmann Institute, who headed the research.

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REFERENCES

www.news.bbc.co.uk/hi/english/ sci/tech/newsid_358000/358822.stm

www.ece.uiuc.edu/pubs/centhist/six/bcl.htm

www.sciencedaily.com/releases/1999/07/990702080524.htm

www.sciencedaily.com/releases/2004/04/040430052921.htm

www.cbhd.org/resources/biotech/tongen_2003-11-07.htm

www.hindu.com/seta/2004/ 05/20/stories/2004052000611600.htm

www.eurekalert.org/pub_releases/ 2004-04/wi-bcd042604.php

www.findarticles.com/cf_dls/

m0COW/1999_June_10/55056092/p1/article.jhtml

www.expeditionzone.com/story_detail.cfm

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ACKNOWLEDGEMENT

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ABSTRACT

New generation of fast and flexible computers that can work out for

themselves how to solve a problem, rather than having to be told exactly what to

do. Ordinary computers need absolutely correct information every time to come

to the right answer A biological computer will come to the correct answer based

on partial information, by filling in the gaps itself.

The device the team has built can "think for itself" because the leech

neurons are able to form their own connections from one to another. Normal

silicon computers only make the connections they are told to by the programmer.

This flexibility means the biological computer works out it own way of solving

the problem. "With the neurons, we only have to direct them towards the

answer and they get it themselves,"This approach to computing is

particularly suited to pattern recognition tasks like reading handwriting,

which would take enormous amounts of power to do well on a

conventional computer.

The method by which DNA works in nature is a form of Turing Machine

and such a machine can be used to solve computational problems. A way of

applying DNA manipulation techniques to the "Hamilton Path Problem" - for

several cities, some of which are connected by non-stop flights, does a path exist

to travel from A to B which passes through every other city once and only once?

When the number of cities gets to around one hundred it could take hundreds of

years of conventional computer time to solve the problem, even with the most

advanced parallel processing available.

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TABLE OF CONTENTS

INTRODUCTION

THE WORLD’S SMALLEST COMPUTER

DOCTOR IN A CELL

THE BIOLOGICAL BITS AND BYTES OF DNA COMPUTING

o Guinness Book of World Records

COMPUTERS & BIOLOGICAL COMPLEXITY

o Computing Device to Serve As Basis for Biological Computer

COMPUTING WITH LEECHES

o Leechulator

LIVING COMPUTERS VS. TODAY'S HARDWARE

DNA COMPUTING

o Hamilton path problem

o Working principle

o Turing machine

o Output Display

DNA'S LIMITLESS POTENTIAL

o Molecular-Level Treatment

o Computing Comes to Life

o Biological 'computer' fits inside a drop of water

ADVANTAGES

DISADVANTAGES

APPLICATIONS

CONCLUSION

o Biological software for computers of the future

REFERENCES

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Biological Computer-Seminar report

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