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Dynamics of Self-replicating DNA-Tile Patterns

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Mining Intelligence and Knowledge Exploration (MIKE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10089))

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Abstract

DNA tiles serve as molecular components for the self-assembly of programmable 2-dimensional patterns at the nanoscale. To produce identical copies of a pre-assembled DNA tile pattern, we use a theoretical framework of non enzymatic cross-coupled self-replication system based on tile self-assembly model. This paper presents a kinetic modelling of the pattern self-replication and analyses the influence of physicochemical parameters of tile self-assembly process over the reliability and replication gain. We demonstrate that the tile assembly errors, introduced in tile patterns during their assembly, set a limit over the size of a tile pattern that can be replicated exponentially and reliably.

R. Prasath—A part of this was carried out when the author was in Indian Institute of Information Technology (IIIT) Sricity, India.

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Notes

  1. 1.

    Macroscopic kinetic rate refers to an approximate kinetic rate for a terminal assembly process, as discussed in  [4].

  2. 2.

    z-transform is a linear operator that is applied to convert non-linear difference equations of time (i) domain into linear equations of frequency domain.

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Authors and Affiliations

  1. Department of Computer and Information Science, The Norwegian University of Science and Technology, Sem Sælands Vei 7–9, 7491, Trondheim, Norway

    Vinay Kumar Gautam & Rajendra Prasath

Authors
  1. Vinay Kumar Gautam
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  2. Rajendra Prasath
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Correspondence to Vinay Kumar Gautam .

Editor information

Editors and Affiliations

  1. Norwegian University of Science and Technology, Trondheim, Norway

    Rajendra Prasath

  2. Center for Computing Research, CIC, National Polytechnic Institute, IPN, Mexico City, Distrito Federal, Mexico

    Alexander Gelbukh

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Gautam, V.K., Prasath, R. (2017). Dynamics of Self-replicating DNA-Tile Patterns. In: Prasath, R., Gelbukh, A. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2016. Lecture Notes in Computer Science(), vol 10089. Springer, Cham. https://doi.org/10.1007/978-3-319-58130-9_3

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  • DOI: https://doi.org/10.1007/978-3-319-58130-9_3

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  • Online ISBN: 978-3-319-58130-9

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