Dynamic neighborhood searches for thermodynamically designing DNA sequence

Suguru Kawashimo, Hirotaka Ono, Kunihiko Sadakane, Masafumi Yamashita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationDNA Computing - 13th International Meeting on DNA Computing, DNA13, Revised Selected Papers
Number of pages10
Publication statusPublished - 2008
Event13th International Meeting on DNA Computing, DNA13 - Memphis, TN, United States
Duration: Jun 4 2007Jun 8 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4848 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other13th International Meeting on DNA Computing, DNA13
Country/TerritoryUnited States
CityMemphis, TN

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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