Thursday, April 25, 2013
DNA COMPUTING
Introduction:
DNA or
Deoxyribo nucleic acid, represents information as a pattern of molecules on a
strand of DNA. Each strand represents
one possible answer. The machine's input, output
and software program are all DNA molecules.
The DNA computing is the
today’s real cutting edge technology.
Scientists are incorporating actual human genetic material into
microprocessors and using DNA in test tubes to solve sophisticated mathematical
problems.
Today’s
technology that takes DNA out of the test tube and puts it on a solid surface,
to the development of larger DNA computers capable of tackling the kinds of
complex problems that conventional computers now handle routinely.
Scientists
have taken DNA computing from the free-floating world of the test tube and
anchored it securely to a surface of glass and gold. In so doing, they have
taken a small but important step forward in the quest to harness the vast
potential of DNA to perform the same tasks that now require silicon and
miniature electronic circuits.
DNA technology is a nascent technology
that seeks to capitalize on the enormous informational capacity of DNA,
biological molecules that can store huge amounts of information and are able to
perform operations similar to a computer’s through the deployment of enzymes,
biological catalysts that act like software to execute desired operations.
In the Wisconsin University
The Structure of DNA:
The ladderlike double-helix
structure of DNA was discovered in 1953 by James Watson and Francis Crick. The
rungs of the "ladder" contain combinations of four bases (adenine,
thymine, cytosine, and guanine) held together by hydrogen bonds. These base
pairs are arranged along a sugar-phosphate backbone (the sides of the
"ladder").
What
is DNA Computing :
DNA computing is a new discipline involving
cross-disciplinary research between molecular biology and computer science and
involves both practical and theoretical work.
The theoretical research is mainly concerned with developing formal
models for biological phenomena, whilst the practical research involves the
realization of the theoretical work in the laboratory.
Instead of
retaining information as ones and zeros and using mathematical formula to solve
a problem, DNA computing uses data represented by a pattern of molecules
arranged on a strand of DNA.
Specific enzymes act like software to read, copy, and manipulate
the code in predictable ways.
The
area was initiated in 1994 by an article written by L.M.Adleman on ”Molecular
Computation of Solutions to Combinational Problems”. In this article Adleman show that it is
possible to solve a particular computational problem using standard techniques
from molecular biology. Since Adlemans
original experiment, researchers have developed several different models to
solve other mathematical and computational problems using molecular techniques.
Why
DNA Computing :
There
are two reasons for using molecular biology to solve computational problems.
1.
The
information density of DNA is much greater than that of silicon: 1 bit can be
stored in approximately one cubic nanometer.
Other storage media, such as videotapes can store 1 bit in
1,000,000,000,000 cubic nanometer.
2.
Operations
on DNA are massively parallel : a test tube of DNA can contain trillions of
strands. Each operation on a test tube
of DNA is carried out on all strands in the tube in parallel.
Explanation of
Molecular Computing with DNA :
Today DNA computing has become
one of the growth fields in the computational sciences.
The first toy problems solved by DNA computations were
Hamiltonian path problems, often called traveling-salesman problems. The
objective is to find the optimal path by which to visit a fixed number of
cities once each. The problem can be
solved with pencil and paper if only a small number of cities are involved, but
it explodes into a non-deterministic time problem (NP) when large number of
cities are considered. On conventional computers, NP problems quickly become
intractable because of the large number of possible paths that must be tested
and compared.
But DNA computers can use their massive
parallelism to find the optimal route among a large number of cities without
trying out every possible combination one at a time. Instead, massive numbers of short DNA
sequences representing each city are mixed together in solution. Each end of
each city sequence is sticky, so that they become stuck together in long
sequences representing every possible order in which cities could be visited.
Every possible route through the cities is generated at one
time, usually in less than an hour in a test tube. The next task is to filter out the DNA
sequences that start and end with the city of origin. Then the sequences with the correct number of
stops — one per city — are filtered out. Finally, the sequences that visit each city
only once are filtered out, yielding a set of optimal solutions.
Adleman performed agarose gel electrophoresis,
ligation reactions and polymerase chain reactions to carry out those steps with
real DNA sequences. He derived an
optimal solution to a seven-city traveling-salesman problem in approximately
one week. Unfortunately, you can solve
the same problem on a piece of paper in about an hour — or by a digital
computer in a few seconds.
But when the number of cities is increased to
just 70, the problem becomes intractable for even a 1,000-Mips supercomputer.
By contrast, the 70-city problem is a theoretical breeze for DNA computing,
because while a single DNA molecule performs at only .001 Mips, a test tube
full can perform at about 1 quadrillion Mips.
Adleman pointed out that the
medium was remarkable for the following reasons.
Ø Speed: The
above computation popped along at 10^14
operations/s; 100x faster than a fast supercomputer.
operations/s; 100x faster than a fast supercomputer.
Ø Energy Efficiency: Adleman
figured his computer was
running at 2x10^19 operations per joule. This represents 16x
more energy than the floor set by the second law of
thermodynamics. Computers built by humans waste about a billion
times more energy per operation.
running at 2x10^19 operations per joule. This represents 16x
more energy than the floor set by the second law of
thermodynamics. Computers built by humans waste about a billion
times more energy per operation.
Ø Memory: DNA
stores memory at a density of about 1 bit
per cubic nanometer. This is about a trillion times more
efficient than that of videotape.
per cubic nanometer. This is about a trillion times more
efficient than that of videotape.
DNA
Factoids :
Ø
The
Length of DNA molecule, when extended, is
1.5 meters. If stretched out all of the
DNA in our cells, it would reach to the moon—6,000 times.
Ø
DNA
is the basic medium of storage for all living
cells. It has contained and transmitted
the data of life for billions of years.
It is the prototype of human made computers.
Ø Roughly 10 trillion DNA molecules could
fit into a space the size of a marble. Since
all these molecules can process data simultaneously, you could theoretically
have 10 trillion calculations going on in that small space at once. That's more
than the fastest existing supercomputer can handle (currently about 1 trillion
per second).
Advantages
:
A key advantage of DNA is its
microscopic size. “In a test tube
smaller than the joint on your finger, you can put billions and billions of DNA
strands”.
DNA stores a
massive amount of data in a small space. Its effective density is roughly
100,000 times greater than modern hard disks. And while a desktop PC
concentrates on doing one task at a time very quickly, billions of DNA
molecules in a jar will attack the same problem billions of times over.
The appeal of DNA computing lies in the fact
that DNA molecules can store far more information than any existing
conventional computer chip. It has been estimated that a gram of dried DNA can
hold as much information as a trillion CDs. Moreover, in a biochemical reaction
taking place in a tiny surface area, hundreds of trillions of DNA molecules can
operate in concert, creating a parallel processing system that mimics the
ability of the most powerful supercomputer.
The chips that
drive conventional computers represent information as a series of electrical
impulses using ones and zeros. Mathematical formulas are used to manipulate
that binary code to arrive at an answer. DNA computing, on the other hand,
depends on information represented as a pattern of molecules arranged on a
strand of DNA. Certain enzymes are capable of reading that code, copying and
manipulate.
But
why use DNA or RNA to solve problems when we already have fast, silicon-based
microprocessors? DNA processors use
cheap, clean, and readily available biomaterials (rather than the costly, and
often toxic materials that go into traditional microprocessors). DNA also stores more information in less
space, and because it computes via biochemical reactions (of which many can
take place simultaneously), DNA can handle massive parallel processing. In an era where the end of Moore’s Law is in sight, computer
scientists are looking for a way to take processors beyond the speed and size
limits of silicon microcircuitry. DNA
computing is one way to do this thing it in predictable ways.
More than 25 years ago, when
Intel was developing the first microprocessor, company cofounder Gordon Moore
predicted that the number of transistors on a microprocessor would double
approximately every 18 months. To date, Moore
An End to Disease :
Most
of the research on DNA processors is being done by biotech companies hoping to
cash in on recent breakthroughs in decoding the human genome. Scientists at
these companies have created microprocessor chips that contain fragments of DNA
in place of the usual electrical circuitry. These chips, which contain an array
of specific genetic information that corresponds to the data on a human gene,
are known as microarrays. Once they are
fed into a special, PC-like machine, scientists can compare the chip to real human
DNA to see how human DNA changes when it becomes cancerous or is afflicted with
a virus. Eventually, when scientists
have a more thorough understanding of which parts of the human genome control
specific functions, they will be able to use microarrays to determine an
individual's susceptibility to certain diseases or resistance to particular
drugs. (Biotech companies are patenting
their microarrays, and plan to sell them to doctors and scientists.)
Disadvantages:
One of the practical difficulties that
arise in implementing a DNA computer is controlling the error rate at each
computational step. Unlike their logical
counter parts, biological operations(bio-ops) produce incorrect results from
time-to-time.
The error rates typically range from
10^-5 to 0.05.
Theoretical
& Practical Computing :
Starting
from Observing the structure and dynamics of DNA of theoretical research began
to propose formal models(This means models with rules for performing
theoretical operations) from DNA computers.
Once a model has been created it is important see what kind of problems
can be solved using it.
The practical side of DNA computing has progressed at a
much slower rule, due mainly to the fact that the laboratory work is very time
consuming and error prone. However the
practical research is now beginning to pickup speed.
DNA computing is an interdisciplinary field where
biologists, computer scientists, physics, mathematics, chemists, etc. find a
lot of interesting problems which can be applied to both theoretical and
practical areas of DNA computing.
If any one want to begin to work on DNA computing should
have a basic idea of what they want to do. i.e., in practical or theoretical
side? If they prefer the practical one,
then they must be more oriented to Chemistry, biochemistry, Computer Science
etc. If they prefer the theoretical side then they must be oriented to Computer
Science, Mathematics etc.
CONCLUSION
:
The
development of Biotechnology can definitely lead to the development of DNA computing. Today DNA computing is one of the nascent
technologies. The DNA computing can
replace the existing conventional microprocessors because of its cheap, clean,
and readily available biomaterials.
