Introduction to algorithms
Universal DNA tag systems: a combinatorial design scheme
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Strand design for biomolecular computation
Theoretical Computer Science - Natural computing
Stochastic Local Search Algorithms for DNA Word Design
DNA8 Revised Papers from the 8th International Workshop on DNA Based Computers: DNA Computing
Complexities for generalized models of self-assembly
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Linear constructions for DNA codes
Theoretical Computer Science
On codeword design in metric DNA spaces
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
Flexible word design and graph labeling
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
Deterministic polynomial-time algorithms for designing short DNA words
TAMC'10 Proceedings of the 7th annual conference on Theory and Applications of Models of Computation
Deterministic polynomial-time algorithms for designing short DNA words
Theoretical Computer Science
Improving the design of sequences for DNA computing: A multiobjective evolutionary approach
Applied Soft Computing
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We consider the problem of efficiently designing sets (codes) of equal-length DNA strings (words) that satisfy certain combinatorial constraints. This problem has numerous motivations including DNA self-assembly and DNA computing. Previous work has extended results from coding theory to obtain bounds on code size for new biologically motivated constraints and has applied heuristic local search and genetic algorithm techniques for code design. This article proposes a natural optimization formulation of the DNA code design problem in which the goal is to design n strings that satisfy a given set of constraints while minimizing the length of the strings. For multiple sets of constraints, we provide simple randomized algorithms that run in time polynomial in n and any given constraint parameters, and output strings of length within a constant factor of the optimal with high probability. To the best of our knowledge, this work is the first to consider this type of optimization problem in the context of DNA code design.