Thesis-PDF - IAP/TU Wien

Investigated structures are e.g. genetic material, membranes or enzymes. The
power behind wet nanotechnology derives from the actual existence of functioning
nanomachines - living systems. No proof of concept is needed, these structures
interact already successfully on the nanometer-scale.
2.2.3 Computational Nanotechnology
Computational Nanotechnology is a prerequisite for designing and testing complex
nanostructures. The predictive and analytical power of simulations is an important
ingredient to success in nanotechnology. For example, in order to know what an
enzyme will be able to do, its 3D-structure must be determined. How its shape
emerges from a sequence of amino acids is one goal of protein folding simulations.
To predict this is highly desirable as it helps to understand how enzymes work,
what defects are caused by misfolding or even for designing functional enzymes 4 .
At the same time such efforts are very resource intensive. 5
Computational nanotechnology may help to combine wet and dry nanotechnology,
selecting from their strengths while avoiding their weaknesses. It can also
help to reduce the development times of such systems.
It is thinkable that advances made in nanotechnology do not only lay the
basis for new discoveries but will also contribute to the pace of advancement in
nanotechnology itself. E.g. the development of nanoelectronics yielding higher
computing power should have considerable effect on the ability to better describe
and develop the next generation of nanoelectronics. 6
4 Such protein molecular machines are quite contrary to the ideas of diamond/carbon/metallic
molecular manufacturing, which is predicated on rigidity/stability and absence of corrosive solvents.
A cooled down protein machine becomes at a certain point too rigid to work. Such a
machine works because because its non-bonding interactions are weak and a warm, wet environment
where Brownian motion is omnipresent and aids the internal logic of conformational
change of the molecule.
5 The computational power required is sometimes so high that with today’s supercomputers
these are hardly treatable problems. For the protein folding problem there exist initiatives to
involve the public. One example is the Folding@Home project, where a Stanford research group
works on the theory and simulations of how proteins, RNA, and nanoscale synthetic polymers
fold. The Folding@Home project uses distributed computing, i.e. private personal computers
that are given instructions over the Internet work on pieces of a simulation in parallel. Everyone
with an internet connection can take part by downloading a client program that will collect
simulation assignments and run them whenever the computer processor would otherwise be in
an idle state. Special algorithms are used in order to break up calculations, distribute the
workload and then combine them again in order to act like a single, very fast computer. The
project is currently using about 2 million processors with a total computational power of 920
Teraflops (1 Teraflop designates a trillion floating point operations per second). In comparison,
the most performant supercomputer as of today, the "BlueGene/L", only reaches about 300
teraflops. ([20], [21], [22])
6 A bold idea advocated by some researchers (e.g. R. Kurzweil, see [23]) is that our ability to
build tools that enhance our ability to build tools follows the trend of a generalized Moore’s law.
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