WHAT IS DNA?
WHAT IS DNA COMPUTING?
Many designs for minuscule computers aimed at harnessing the massive storage capacity of DNA have been proposed over the years. Earlier schemes have relied on a molecule known as ATP, which is a common source of energy for cellular reactions, as a fuel source. But in the new set up, a DNA molecule provides both the initial data and sufficient energy to complete the computation. Shapiro and his colleagues describe their DNA computer in a report published online this week by the Proceedings of the National Academy of Sciences.
Both models of the molecular computer are so-called automatons. Given an input string comprised of two different states, an automaton uses predetermined rules to arrive at an output value that answers a particular question. For example, it can determine whether a string containing only a’s and b’s has an even number of a’s, or if all the b’s are preceded by a’s. In the latest design, two DNA molecules bond together to perform the computational steps. An enzyme known as FokI acts as the computer’s hardware by cleaving a piece of the input molecule and releasing the energy stored in the bonds. This heat energy then powers the next computation. [The illustration above shows an input DNA molecule (green/blue), software DNA molecules (red/purple) and FokI (colored ribbons).] The authors report that a micro liter of solution could hold three trillion computers, which together would perform 66 billion operations a second.—Sarah Graham
WHY IS DNA COMPUTING USEFUL?
First and foremost, DNA computing is useful because it has a capacity lacked by all current electronics-based computers: its massively parallel nature. What does this mean, you ask? Well, essentially while DNA can only carry out computations slowly, DNA computers can perform a staggering number of calculations simultaneously; specifically, on the order of 10^9 calculations per mL of DNA per second! This capability of multiple cotemporal calculations immediately lends itself to several classes of problems which a modern electronic computer could never even approach solving. To give you an idea of the difference in time, a calculation that would take 10^22 modern computers working in parallel to complete in the span of one human’s life would take one DNA computer only 1 year to polish off!First and foremost, DNA computing is useful because it has a capacity lacked by all current electronics-based computers: its massively parallel nature. What does this mean, you ask? Well, essentially while DNA can only carry out computations slowly, DNA computers can perform a staggering number of calculations simultaneously; specifically, on the order of 10^9 calculations per mL of DNA per second! This capability of multiple cotemporal calculations immediately lends itself to several classes of problems which a modern electronic computer could never even approach solving. To give you an idea of the difference in time, a calculation that would take 10^22 modern computers working in parallel to complete in the span of one human’s life would take one DNA computer only 1 year to polish off!
OPERATIONS ON DNA
While a number of equivalent formalization exist, we follow the description. note that the types of operations available are result of the capabilities of molecular biology rather than the wishes of algorithms designers. Also note that this algorithms are performed in constant time on test tubes which for the sake of this discussion may be of arbitrary size this operations are:1) MERGE: this is the simple operations of combining the content of two test tube into one
2) ANNEAL: this is the process by which complementary strands of DNA are paired to form the famous double helix structure of Watson &crick. Annealing is achieved by cooling a DNA solution which encourage pairing. Adleman uses this to generate all legal paths through graphs.
3) MELT:-it is the inverse operation of anneling. By heating the contents of a test tube , double stranded DNA sequences are denatured , or separated into its two single stranded parts.
4) OPERATION BY LENGTH:-the contents of test tube can be separated by increasing length. this is achieved by gel electrophoresis , whereby longer strands travel more slowly through gel. This operation was used by Adleman in his solution to hp.
5) SEPEARATION BY SEQUENCE:- this operation allows one to remove from solution all the DNA strands that contain a desired sequence. This is performed by generating the strands whose complement is the desired sequence. This newly generated strands is attached to magnetic substance which is used to extracts the sequence after annealing.
6) COPYING:-copies are made from DNA strands in a test tube. The strands to be copied must have known sequences at both the beginning and end in order for this operation to be performed.
7) APPEND:-this process makes a DNA strand longer by adding a character or strands to the end of each sequence.
8) DETECT:-it is also possible to analyze test tube in order to determine whether or not it contain at least one strand of DNA.
HOW DNA COMPUTER WILL WORK?
THE GENE MACHINE:This article first appeared in Personal Computer World magazine, December 1996.
AS RESEARCHERS CONTINUE to look beyond silicon for the computers of the future, a US scientist has created a massively parallel computer in a single test-tube containing a few drops of liquid. His computer is DNA, the molecule of life
Leonard Adleman computer scientist at the University of Southern California, has devised a novel solution to a classic mathematical problem. In doing so, he has single-handedly laid the foundation for a new technology.
The diagram shows the test case used by Adleman. Here, there are seven towns, 1 to 7, and the arrows between towns show the interconnecting roads and the allowed direction of travel. The salesman must start in town 1 and finish in town 7. Such a simple case can be solved with a few minutes' trial and error (the answer is 1 -> 2 -> 3 -> 4 -> 5 -> 6 -> 7) but as the number of towns and their interconnections increase, the problem becomes very hard to solve indeed. In fact, the problem belongs to one of the hardest classes of
mathematical problems known, which require enormous computing power to attack. Adleman's breakthrough was to use DNA to solve the problem, and his approach was ingenious. This is how he did it.
DNA comprises two intertwined molecular strands, each of which is a long chain of alternating phosphates and sugars. Attached to each sugar is a molecular group called a 'base', and there are four different kinds, known as A, C, G and T. It is the particular sequence of bases along a strand that forms the genetic code for life. An A base on one strand attacts a T base on the other strand, and a C base attracts a G base. These attractions pull the two strands together into the familiar 'double helix' shape discovered by Watson and Crick in 1953.
Adleman represented each city, and each road between two cities, with a specially engineered strand of DNA exactly 20 bases long. The sequence of bases in each strand was carefully designed such that strands could link with each other to spell out possible routes. Take, for example, cities 6 and 2. The strand of DNA representing the road from 6 to 2 would stick to the end of the strand representing city 6, and the beginning of the strand for city 2, but not to any part of any strands for other cities.
To solve the problem of finding a route between cities 1 and 7, Adleman mixed together in his test-tube a million million copies of all the possible strands for the cities and their interconnections, and allowed them to link up with each other. Next, he used standard biochemical techniques to isolate particular strands. First, he isolated only those linked-up strands which started with the code for 1, and ended with 7. Then, he isolated only those strands which coded a route through seven cities, knowing that these strands must be exactly 140 (7 x 20) bases long. Longer or shorter strands were rejected. Finally, he kept only those strands containing city 1, and of these he kept only those containing city 2, and so on. After seven days of intensive laboratory work, Adleman's test-tube contained the answer to the problem, subsequently visible as a series of dark bands on a DNA sequencing gel.
On the face of it, it might hardly seem worth the bother, especially as Adleman already knew the answer before he started the experiment. But this was much more than a curious laboratory stunt. During the initial 'linking-up' stage of the process, Adleman's test-tube computer effectively performed an astonishing 10^14 calculations. And it did so with the consumption of only a tiny amount of energy, and in a tiny physical space.
This was the first time that the combinatorial power of DNA had ever been exploited for computation, and Adleman's work has sparked a flurry of activity. The first researcher to take the idea further was Richard Lipton of Princeton University, who showed how to use DNA to solve another important puzzle in computer science: the 'satifiability' problem, routinely faced by designers of logic circuits. Here, the goal is to find the solutions to problems in Boolean logic. For example, given an _expression such as
( (a = 1) OR (b = 1) OR (c = 0) ) AND ( (b = 0) OR (c = 1) )
the problem is to find which (if any) binary values of a, b, and c satify the _expression. Like the travelling salesman problem, simple instances are easy to solve by trial and error, but as the number of variables and constraints increase, the computation time mushrooms exponentially and the problem soon becomes intractable. With DNA strands, however, huge numbers of potential solutions can be evaluated and discarded in parallel, until the correct solution, if there is one, remains.
Perhaps the most exciting proposal is Lipton's scheme for using DNA to code arbitrary binary numbers, which opens up the possibility of DNA-based solutions to a wider range of problems, such as matrix manipulation, factoring, dynamic linear programming and algebraic symbol processing. Since the methods of DNA computing are quite different from traditional step-by-step algorithms, perhaps we shall see the development of hybrid machines, part silicon and part DNA Another promising application is to provide pure data storage: to encode one bit of data using DNA would occupy approximately 1 cubic nanometre, which means a test-tube ought to comfortably accommodate several hundred million gigabytes. Think what you could do with a bathful.
Research into DNA computing is taking off in a big way. This year the 2nd Annual Workshop on DNA-based computing was held at Princeton University, and there is already a new scientific journal devoted to the subject. However, like many ideas for computers based on technologies other than silicon, although the DNA computer looks great on paper, the practical biochemical and engineering challenges are immense. DNA manipulations involve fearsomely complicated lab protocols, and are highly prone to contamination and error. Some scientists also warn of the potential ecological horrors of flushing discarded DNA computers down the drain.
But apart from the technological excitement, all this talk about using DNA for computing has got the philosophers hopping too. Are the processes inside our own cells essentially performing computations to which human life is the answer? If this is so, the philosophers ask, then what is the question?
SILICON COMPUTERS
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When it comes to analyzing biomolecules such as proteins or DNA, the tried-and-true test tube can only take you so far. It is for this reason that many scientists are working to create so-called labs-on-chips and other nanoscale devices, specially engineered to sort, measure and count the various molecules of life. Such inventions might make possible, among other things, far more accurate genetic and diagnostic tests. Harold Craighead, director of the Cornell Nanobiotechnology Center, described some of his own prototypes yesterday at the annual meeting of the American Association for the Advancement of Science (AAAS) in San Francisco
The Craighead laboratory has worked on silicon-based alternatives to the organic gels used in DNA electrophoresis. The traditional technique relies on an electric field to pull fragments of DNA through the gel's tiny pores. Fragments of different lengths move at different speeds and ultimately collect in separate bands that can be photographed using fluorescent or radioactive tags. In Craighead's devices, silicon structures with tiny pores—or forests of either columns or flat vanes—replace the gel. These nano-sieves need not sort only DNA molecules but could also separate proteins, carbohydrates or lipids. "This will expand the methods for analyzing very small amounts of biochemicals," Craighead says, "and create new abilities unanticipated by the test-tube methods. --Kristin Leutwyler
TURNING DNA INTO LOGIC GATES
The idea of making DNA perform computational tricks is hardly new. These data-packed molecules are in many ways perfect for the job. One very public test of their potential came in 1994, when Leonard Adleman of the University of Southern California showed that DNA could tackle the famous "traveling salesman" problem. And now Nadrian C. Seeman and his colleagues at New York University have found a clever new way to tease DNA strands into mimicking exclusive OR (XOR) logic gates
APPLICATIONS
MASSIVELY PARALLEL PROCESSINGThe primary advantage offered by most proposed models of DNA based computation is the ability to handle millions of operations in parallel. The massively parallel processing capabilities of DNA computers may give them the potential to find tractable solutions to otherwise intractable problems, as well as potentially speeding up large, but otherwise solvable, polynomial time problems requiring relatively few operations.
Classical DNA computing techniques have already been theoretically applied to a real life problem: breaking the Data Encryption Standard (DES). Although this problem has already been solved using conventional techniques in a much shorter time than proposed by the DNA methods, the DNA models are much more flexible, potent, and cost effective.
STORAGE AND ASSOCIATIVE MEMORY
DNA might also be used to mirror, and even improve upon, the associative capabilities of the human brain. Baum proposed a method for making a large content addressable memory using DNA. A truly 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. Rather, the use of this technique would replicate what is thought by many to be a key factor in human intelligence.
DNA2DNA APPLICATIONS
Another area of DNA computation exists where conventional computers clearly have no current capacity to compete. 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.
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. 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 effect progress in biomolecular techniques by placing demands on bio-chemists and their tools that might not otherwise be considered.
MEDICAL APPLICATIONS
The work is still test-tube-based but it could lead to "nano-clinics" which remain in the body, sensing illnesses and then treating them automatically.
The devices are so small that roughly a trillion of them can fit into a microlitre (a millionth of a litre).
The research is led by Ehud Shapiro from the Weizmann Institute in Rehovot and is published in the journal Nature.
"The devices are made of biological molecules - DNA; synthetic DNA molecules which we produced to our design, and a naturally occurring enzyme which cuts DNA," Professor Shapiro told BBC News.
Biological 'computer'
They look like chains consisting of three main segments. The first segment senses levels of substances which are produced by cancerous cells. It functions like a computer running through a simple algorithm.
One algorithm which the team tested is intended to diagnose prostate cancer.
It says that if levels of two messenger RNA molecules (PPAP2B and GSTP1) are lower than usual, and levels of two others (PIM1 and HPN) are elevated, there must be prostate cancer cells in the vicinity.
Smart medicine
So far these devices have only been trialled in test-tube solutions, and several decades of further work are needed before research could begin in humans.
But one day nano-scale devices like these could be used inside our bodies to protect against or treat cancers and other diseases.
"The best way to think about it is as a smart drug," suggested Professor Shapiro.
"Today, we bombard the body with drugs that go everywhere and operate everywhere and at any time.
"And what we designed is a smart drug that has some conditions encoded for its release; and it will be released and activated only at the right time and at the right location when a disease is diagnosed."
Advantages & drawbacks
Advantages1) Memory capacity
Its memory is compact. One cubic centimeter of DNA could store 10^21 bits of information, where as conventional computers can store 10^14 bits.
2) Information capacity:
Each DNA molecules encodes 400 bits of information which is 100000 billion times as much as one gigabyte hard disk can store.
3) Low cost
Its very cheap since every living organism is made up of DNA.
4) Speed
DNA performs 1000 more operations per second than the fastest supercomputers.
5) Energy efficiency
DNA computers can perform 2*2019 operation while super computer can do 1010operation making it 1010 times less efficient.
6) Storage efficiency:
DNA molecules are very small, much smaller than computer chips, which makes a lot of them easy to fit in little space.
7) Parallel execution:
All molecules work together at once simultaneously. 10 trillion calculations could be executed at the same time.
8) Parallel processing:
Gives DNA computers the ability to revolution of tracable problems.
9) Simpler & more accessible
The new research in journals nature reports the development of novel surface chemistry that greatly simplifies the complex and repetitive steps previously used in rudimentary DNA computers. It takes DNA outs of test tube and puts it on the solid surface making the technology simpler more accessible and more amenable to the development of larger DNA computers.
Drawbacks
- One problem is that the algorithm proposed so far uses really slow molecular biological operation. Each primitive operation in the DNA computer takes hours. That’s clock rate may be 10 to 11 times slower than your 100 MHz Pentium. That’s why u don’t have to worry about your computer replacing your computers. it will never response quickly enough on simple problems but we are hoping its make up for its slothfulness with pure parallel power massive memory on some problems to be fit or run in a silicon machine. If all goes as expected a dna computer will take millions of years on the powerful super Computer in, say six months.
- This process takes hours when you run them with small test tube of dna.scale up to vast amount of DNA we are talking about and they may slow down dramatically.
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.
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