Methodology for the DNA-Guided Self Assembly of Novel Computing Circuitry
Chris Dwyer*, a, Russell Taylora, Richard Superfineb, Sean Washburnb, and Dorothy Eriec
aDepartment of Computer Science, University of North Carolina at Chapel Hill,
Chapel Hill, NC 27599 USA bDepartment of Physics & Astronomy, University of North Carolina at Chapel Hill cDepartment of Chemistry, University of North Carolina at Chapel Hill
We present a methodology for the self assembly of complex 3D lattice structures from nanorod-DNA precursors. Our method employs techniques developed across various scientific disciplines to form electrically active and stable nano-structures useful in conventional CMOS-like circuitry [1, 2, 3]. An important feature of our method is that the assembly complexity, or number of unique precursors required to form a structure, does not scale with the size of the lattice. We demonstrate this by describing several simple structures and their assembly procedure.
Although the structures we describe can be used to form almost any nano-scale structure, we explore their use in the fabrication of novel computing circuitry. We describe a type of machine that mixes assemble-time computation with run-time computation. The result is a general-purpose solution to a particular class of problems like the NP-complete problems typically addressed by classical DNA computing. The advantage of our technique over a classical DNA computing technique is that our machines embody the solutions to all problem instances for a given class. Further, the machine uses conventional CMOS circuitry to communicate with the outside world. Therefore, extracting a solution to a particular problem instance requires only fractions of a second compared to the extensive biochemical lab work and time required by most DNA computing techniques.
The bounds on the problem sizes these machines can solve are similar to those that restrict DNA computing; namely, the translation of NP-time to NP-space or NP-material cannot be overcome even for modestly practical problem sizes. We conclude by suggesting a type of approximation machine that may overcome the NP-space restriction and produce practical solutions to the weighted Hamiltonian path problem.
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Department of Computer Science, University of North Carolina at Chapel Hill
Chapel Hill, NC 27599 USA
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