Asynchronous Signal Passing for Tile Self-assembly: Fuel Efficient Computation and Efficient Assembly of Shapes

  • Jennifer E. Padilla
  • Matthew J. Patitz
  • Raul Pena
  • Robert T. Schweller
  • Nadrian C. Seeman
  • Robert Sheline
  • Scott M. Summers
  • Xingsi Zhong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7956)


In this paper we demonstrate the power of a model of tile self-assembly based on active glues which can dynamically change state. We formulate the Signal-passing Tile Assembly Model (STAM), based on the model of Padilla, et al.[1] to be asynchronous, allowing any action of turning a glue on or off, attaching a new tile, or breaking apart an assembly to happen in any order. Within this highly generalized model we provide three new solutions to tile self-assembly problems that have been addressed within the abstract Tile Assembly Model and its variants, showing that signal passing tiles allow for substantial improvement across multiple complexity metrics. Our first result utilizes a recursive assembly process to achieve tile-type efficient assembly of linear structures, using provably fewer tile types than what is possible in standard tile assembly models. Our second system of signal-passing tiles simulates any Turing machine with high fuel efficiency by using only a constant number of tiles per computation step. Our third system assembles the discrete Sierpinski triangle, demonstrating that this pattern can be strictly self-assembled within the STAM. This result is of particular interest in that it is known that this pattern cannot self-assemble within a number of well studied tile self-assembly models. Notably, all of our constructions are at temperature 1, further demonstrating that signal-passing confers the power to bypass many restrictions found in standard tile assembly models.


Turing Machine Tile Type Universal Turing Machine Tape Head Tile Assembly Model 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jennifer E. Padilla
    • 1
  • Matthew J. Patitz
    • 2
  • Raul Pena
    • 3
  • Robert T. Schweller
    • 3
  • Nadrian C. Seeman
    • 1
  • Robert Sheline
    • 3
  • Scott M. Summers
    • 4
  • Xingsi Zhong
    • 3
  1. 1.Department of ChemistryNew York UniversityNew YorkUSA
  2. 2.Department of Computer Science and Computer EngineeringUniversity of ArkansasFayettevilleUSA
  3. 3.Department of Computer ScienceUniversity of Texas – Pan AmericanEdinburgUSA
  4. 4.Department of Computer Science and Software EngineeringUniversity of Wisconsin – PlattevillePlattevilleUSA

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