DNA polymerase programmed with a hairpin DNA incorporates a multiple-instruction architecture into molecular computing

Ken Komiya, Kensaku Sakamoto*, Atsushi Kameda, Masahito Yamamoto, Azuma Ohuchi, Daisuke Kiga, Shigeyuki Yokoyama, Masami Hagiya

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

Abstract

Parallelism is one of the major advantages of molecular computation. A large number of data encoded in DNA molecules can be processed simultaneously by molecular biology techniques, although only a single set of instructions has been implemented in a solution. We have developed a computing machine, called the "whiplash" machine, which is made of DNA polymerase and a hairpin DNA. This machine simulates a finite state machine, executing its own instructions encoded in the DNA moiety, and would thus be applicable to multiple-instruction operation in a solution. In the present study, we explored the feasibility of this novel type of parallelism by applying the whiplash machine in a computation of the directed Hamiltonian path problem. The possible paths in a given graph were represented with different instruction sets, which were then implemented separately by whiplash machines in a test tube. After an autonomous operation of the machines, only the machine that implemented the instruction set corresponding to the Hamiltonian path was recovered from the tube. On the basis of the efficiency of machine operation, which was experimentally determined, 1010 different instruction sets could be implemented simultaneously in a 1-ml solution.

Original languageEnglish
Pages (from-to)18-25
Number of pages8
JournalBioSystems
Volume83
Issue number1
DOIs
Publication statusPublished - 2006 Jan
Externally publishedYes

Keywords

  • DNA-based computing
  • Directed Hamiltonian path problem
  • Molecular state machine
  • Whiplash machine

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Applied Mathematics

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