BEYOND SILICON COMPUTING
AUTHORS:
JIGAR PUROHIT (03CE117) GUNJAN DHOLAKIYA(03CE116) INSTITUE: CHAROTAR INSTITUE OF TECHNOLOGY (CITC)
ABSTRACT
“Everything looks good when it is mounted on a chip.” To challenge the limits of speed and miniaturization scientists of silicon chip the biochip has been invented and made up of DNA (Deoxyribonucleic acid). The function of 0’s and 1’s are done by the specified combinations of the four nucleotides. Their speed is billion times faster. As its processing is parallel and not serial it can compute problem with large possibilities much faster. With application in various fields of genetics basically DNA recognition as well as chemistry development of medicines and detecting diseases it has more utilities.
INDEX 1)
INTRODUCTION TO DNA COMPUTER
4 2
2)
HAMILTON PATH PROBLEM
3)
WORKING OF DNA
5 7
4) PROGRAMMING OF PROBLEM USING DNA________________________ 9 5)
APPLICATION
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6) EFFICIENCY______________________________________________________17 7)
ADVANTAGES-DISADVANTAGES
8)
FUTURE 19
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9) CONCLUSION_____________________________________________________21
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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_______________________________ 8 3.2 Sequence of Sticky Pieces_____________________________9 4.1 Synthetic DNA_____________________________________10 4.2 Memory Unit Structure________________________________11 4.3 Complmentary Coupling Nuclotide_______________________11 4.4 Coupling Rings Long Chain____________________________ 12 4.5 Complmentary Coupling_______________________________12 4.6 Double Helix Structure_______________________________12 4.7 Complementary Strands______________________________13 4.8 Split Doouble Stranded Enzymes_______________________ 13 4.9 Complementary Strands______________________________13 4
4.10 Copy Of Complementary Strands_____________________ 14 5.1 DNA MicroArray___________________________________ 17
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1. INTRODUCTION TO DNA COMPUTER 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. 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.
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 -- 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 6
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. 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. 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 2.2 DNA likes to form long double helices: 7
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 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 8
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 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 3.2 STEP 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. 9
4. WORKING OF DNA 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 4.1 The only fundamental difference between conventional computers and DNA computers is the capacity of memory units: electronic computers have two positions (on or off), whereas DNA has four (C, G, A or T). The study of bacteria has shown that restriction enzymes can be employed to cut DNA at a specific word(W). Many restriction enzymes cut the two strands of double-stranded DNA at different positions leaving overhangs of single-stranded DNA. Two pieces of DNA may be rejoined if their terminal overhangs are complementary. Complements are referred to as 'sticky ends'. Using these operations, fragments of DNA may be inserted or deleted from the DNA.
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 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 11
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).
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.
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Figure 4.5 Nucleotides have additional binding sites which attract specific complementary nucleotides to bind to them 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 4.6 The 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. 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. 14
Most of the possible answers are incorrect, but one or a few may be correct, and the computer's task is to check each of them and remove the incorrect ones using restrictive enzymes. The DNA computer does that by subjecting all of the strands simultaneously to a series of chemical reactions that mimic the mathematical computations an electronic computer would perform on each possible answer. When the chemical reactions are complete, researchers analyze the strands to find the answer -- for instance, by locating the longest or the shortest strand and decoding it to determine what answer it represents. Computers based on molecules like DNA will not have a vonNeumann architecture, but instead function best in parallel processing applications. They are considered promising for problems that can have multiple computations going on at the same time. Say for instance, all branches of a search tree could be searched at once in a molecular system 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; • 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 15
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 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 MICROARRAY
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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, 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.
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7. ADVANTAGES & DISADVANTAGES ADVANTAGES 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 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. 18
• •
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 biooperations 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. 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 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 19
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 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.
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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 costbenefit 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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