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Pattern Formation by Spatially Organized Approximate Majority Reactions

  • Conference paper
Unconventional Computation and Natural Computation (UCNC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8553))

Abstract

Pattern formation is a topic of great interest in biology and nanotechnology. In this paper we investigate a system of spatially-organized reactions inspired by a well-known distributed algorithm for approximate majority voting, and demonstrate that this system can lead to pattern formation from a randomly initialized starting state. We also show that the approximate majority reaction scheme can preserve an existing pattern in the face of noise, and that exerting control over reaction rates can influence the generated pattern. This work has potential applications in the rational design of pattern-forming systems in DNA nanotechnology and synthetic biology.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of New Mexico, Albuquerque, NM, USA

    Matthew R. Lakin & Darko Stefanovic

  2. Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM, USA

    Darko Stefanovic

Authors
  1. Matthew R. Lakin
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  2. Darko Stefanovic
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Corresponding author

Correspondence to Matthew R. Lakin .

Editor information

Editors and Affiliations

  1. Santa Barbara College of Engineering, University of California, Santa Barbara, California, USA

    Oscar H. Ibarra

  2. University of Western Ontario, London, Ontario, Canada

    Lila Kari

  3. University of Western Ontario, London, Ontario, Canada

    Steffen Kopecki

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Lakin, M.R., Stefanovic, D. (2014). Pattern Formation by Spatially Organized Approximate Majority Reactions. In: Ibarra, O., Kari, L., Kopecki, S. (eds) Unconventional Computation and Natural Computation. UCNC 2014. Lecture Notes in Computer Science(), vol 8553. Springer, Cham. https://doi.org/10.1007/978-3-319-08123-6_21

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  • DOI: https://doi.org/10.1007/978-3-319-08123-6_21

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08122-9

  • Online ISBN: 978-3-319-08123-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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