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. 1

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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

The Biological Bits and Bytes of DNA Computing 3

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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-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 4

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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 complexity of our machine code gets to the complexity of our DNA. 64 bit processors have twice the exponental complexity 32 bit processors have.

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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 using biomolecules, such a device may operate in the human body, interacting with its biochemical environment in a programcontrolled 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 only when the programmer directs the connections to occur. However, in a biological 7

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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-to2 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? When the number of cities gets to around one hundred it could take 11

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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. 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 12

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"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 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.'

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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 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 14

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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 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 15

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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 timeconsuming 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 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

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

ADVANTAGES 17

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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.

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The large supply of DNA makes it a cheap resource.

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Unlike the toxic materials used to make traditional microprocessors, DNA biochips can be made cleanly.

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

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DNA will make computers smaller than any computer that has come before them, while at the same time holding more data

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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

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Diagnosis of cancer

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Application in biological & pharmaceutical applns

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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,

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The fabrication of nanoscale objects that can be placed in intracellular locations for monitoring and modifying cell function,

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The replacement of silicon devices with nanoscale molecular-based computational systems, and

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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

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

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

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

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

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

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

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www.findarticles.com/cf_dls/ m0COW/1999_June_10/55056092/p1/article.jhtml

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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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