An interface for a computing model using methylation to allow precise population control by quantitative monitoring

  • Authors:

  • Ken Komiya;Noriko Hirayama;Masayuki Yamamura

  • Affiliations:

  • Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology;Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology;Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology

  • Venue:
  • DNA13'07 Proceedings of the 13th international conference on DNA computing
  • Year:
  • 2007

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Abstract

We developed an interface to enable feedback control for amethylation-based computing model, in which a bit string is represented by the methylated and unmethylated status of the specific locations on a DNA molecule. On construction of a reaction system for the computational purpose, it is problematic that an open loop system without feedback control is easy to lose the molecular variety required for computation. It is, thus, important for the methylation-based computing to achieve quantitative sensing for feedback control. Difference in methylation status can be converted into the sequence variation by the bisulfite reaction. As a consequence, distribution between methylated and unmethylated DNA molecules could be quantitatively monitored by combining the polymerase chain reaction (PCR) using methylation specific primers with quantitative PCR. In the present study, we experimentally investigated the feasibility of the proposed interface for controlling the population of a library of DNA registers that have distinct methylated patterns representing different bits. Result indicated that, quantitative measurement of population was successfully performed by discriminative amplification using the methylation-specific primer. This interface, which allows us to generate a homogenous or biased library as expectedly, would be useful for molecular evolutionary computation and molecular learning.