Molecular computing is an emerging field to which chemistry, biophysics,
molecular biology, electronic engineering, solid state physics and computer science
contribute to a large extent. It involves the encoding, manipulation and retrieval
of information at a macromolecular level in contrast to the current techniques,
which accomplish the above functions via IC miniaturization of bulk devices. The
biological systems have unique abilities such as pattern recognition, learning,
self-assembly and self-reproduction as well as high speed and parallel information
processing. The aim of this article is to exploit these characteristics to build
computing systems, which have many advantages over their inorganic (Si,Ge) counterparts.
computing began in 1994 when Leonard Adleman proved thatDNA computing was possible
by finding a solution to a real- problem, a Hamiltonian Path Problem, known to
us as the Traveling Salesman Problem,with a molecular computer. In theoretical
terms, some scientists say the actual beginnings of DNA computation should be
attributed to Charles Bennett's work. Adleman, now considered the father of DNA
computing, is a professor at the University of Southern California and spawned
the field with his paper, "Molecular Computation of Solutions of Combinatorial
Problems." Since then, Adleman has demonstrated how the massive parallelism
of a trillion DNA strands can simultaneously attack different aspects of a computation
to crack even the toughest combinatorial problems.
Traveling Salesman Problem:
The objective is to find a path from start
to end going through all the points only once. This problem is difficult for conventional
computers to solve because it is a "non-deterministic polynomial time problem"
. These problems, when they involve large numbers, are intractable with conventional
computers, but can be solved using massively parallel computers like DNA computers.
The Hamiltonian Path problem was chosen by Adleman because it is known problem.
following algorithm solves the Hamiltonian Path problem:
paths through the graph.
2.Keep only those paths that begin with the start
city (A) and conclude with the
end city (G).
3.If the graph has n cities,
keep only those paths with n cities. (n=7)
4.Keep only those paths that enter
all cities at least once.
5.Any remaining paths are solutions.
key was using DNA to perform the five steps in the above algorithm. Adleman's
first step was to synthesize DNA strands of known sequences, each strand 20 nucleotides
long. He represented each of the six vertices of the path by a separate strand,
and further represented each edge between two consecutive vertices, such as 1
to 2, by a DNA strand which consisted of the last ten nucleotides of the strand
representing vertex 1 plus the first 10 nucleotides of the vertex 2 strand. Then,
through the sheer amount of DNA molecules (3x1013 copies for each edge in this
experiment!) joining together in all possible combinations, many random paths
were generated. Adleman used well-established techniques of molecular biology
to weed out the Hamiltonian path, the one that entered all vertices, starting
at one and ending at six. A
fter generating the numerous random paths in the first
step, he used polymerase chain reaction (PCR) to amplify and keep only the paths
that began on vertex 1 and ended at vertex 6. The next two steps kept only those
strands that passed through six vertices, entering each vertex at least once.
At this point, any paths that remained would code for a Hamiltonian path, thus
solving the problem.
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