Bio-molecular Computing
.CHAPTER 1
INTRODUCTION
Computer chip manufactures are furiously racing to make the next microprocessor that will topple speed records. Sooner or later, though, this competition is bound to hit a wall. Microprocessor made of silicon will eventually reach their limits of speed and miniaturization. Chip makers need a new material to produce faster computing speeds.
You won’t believe where scientists have found the new material they need to build the next generation of microprocessors. Millions of natural supercomputers exist inside living organisms, including your living body. They are nothing else but Bio-Molecules itself. Especially DNA. DNA (deoxyribonucleic acid) molecules, the material our genes are made of, have the potential to perform calculations many faster than the world’s most powerful human-built computers. The other Bio-Molecules like Nucleotides, Nucleotides, Saccharides, Lignin, Lipids, Amino acids…
1.1 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 biologial 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.
CHAPTER 2
HAMILTON PATH PROBLEM
Adelman is often called the inventor of the DNA computers. His article in a 1994 issue of Journal Science outlined how to use DNA to solve a well-known mathematical problem, called the “Directed Hamilton Path problem”, also known as the “Traveling Salesman Problem”. The goal of the problem is to find the shortest route between a numbers of cities, going through each city only once. As you add more cities the problem becomes more difficult.
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.
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.
2.1 Solving the problem using DNA
The key to solving the problem using DNA to perform the five steps in solving the above algorithm.
These interconnecting blocks can be used to model DNA:
DNA likes to form long double helices:
The two helices are joined by “bases”, which will be represented by colored blocks. Each base binds only to one other specific base. In our example, we will say that each colored block will bind only with the block of same color. For example, if we only had red colored blocks, they would form a long chain like this:
Any other color will not bind with red
CHAPTER 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:
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 page 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 colored 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.
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.
CHAPTER 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.
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.
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) hainDetermination of expression level (abundance) of genes.
5.3 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.