DNA sequence design using templates
New Generation Computing
Stochastic Local Search Algorithms for DNA Word Design
DNA8 Revised Papers from the 8th International Workshop on DNA Based Computers: DNA Computing
Statistical thermodynamic analysis and designof DNA-based computers
Natural Computing: an international journal
Hybrid randomised neighbourhoods improve stochastic local search for DNA code design
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Complexity of graph self-assembly in accretive systems and self-destructible systems
DNA'05 Proceedings of the 11th international conference on DNA Computing
In search of optimal codes for DNA computing
DNA'06 Proceedings of the 12th international conference on DNA Computing
DNA sequence design by dynamic neighborhood searches
DNA'06 Proceedings of the 12th international conference on DNA Computing
A probabilistic model of the DNA conformational change
DNA'06 Proceedings of the 12th international conference on DNA Computing
DNA'06 Proceedings of the 12th international conference on DNA Computing
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We present a local search based algorithm designing DNA short-sequence sets satisfying thermodynamical constraints about minimum free energy (MFE) criteria. In DNA12, Kawashimo et al. propose a dynamic neighborhood search algorithm for the sequence design under hamming distance based constraints, where an efficient search is achieved by dynamically controlling the neighborhood structures. Different from the hamming distance based constraints, the thermodynamical constraints are generally difficult to handle in local-search type algorithms. This is because they require a large number of evaluations of MFE to find an improved solution, but the definition of MFE itself contains time-consuming computation. In this paper, we introduce techniques to reduce such time-consuming evaluations of MFE, by which the proposed dynamic neighborhood search strategy become applicable to the thermodynamical constraints in practice. In computational experiments, our algorithm succeeded in generating better sequence sets for many constraints than exiting methods.