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Solving constraint satisfaction problems with DNA computing
 Proceedings of the 8th Annual International Computing and Combinatorics Conference, COCOON'02, volume 2387 of Lecture Notes in Computer Science
, 2002
"... Abstract. We demonstrate how to solve constraint satisfaction problems (CSPs) with DNA computing. Assuming that DNA operations can be faulty, we estimate error probability of our algorithm. We show that for any kCSP, there is a polynomialtime DNA algorithm with bounded probability of error. Thus, ..."
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Abstract. We demonstrate how to solve constraint satisfaction problems (CSPs) with DNA computing. Assuming that DNA operations can be faulty, we estimate error probability of our algorithm. We show that for any kCSP, there is a polynomialtime DNA algorithm with bounded probability of error. Thus, kCSPs belong to a DNA analogue of BPP. 1
A Robust Dna Computation Model That Captures Pspace
"... One of the most serious problems in DNA computing is that basic DNA operations are faulty. Many DNA computation models use operations based on annealing and magneticbeads separation which sometimes produce undesirable results. Some models use reliable operations only but their computational powe ..."
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One of the most serious problems in DNA computing is that basic DNA operations are faulty. Many DNA computation models use operations based on annealing and magneticbeads separation which sometimes produce undesirable results. Some models use reliable operations only but their computational power is too weak. The purpose of this paper is to nd a good tradeo between the robustness of DNA operations and their computational power. We present a robust DNA computation model that can solve computationally hard problems. We prove that (i) this model can solve PSPACE complete problems, and (ii) any computational problem that can be solved with this model is in PSPACE.