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Optimizing Tile Set Size While Preserving Proofreading with a DNA Self-assembly Compiler

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Book cover DNA Computing and Molecular Programming (DNA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11145))

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Abstract

Algorithmic DNA tile systems have the potential to allow the construction by self-assembly of large structures with complex nanometer-scale details out of relatively few monomer types, but are constrained by errors in growth and the limited sequence space of orthogonal DNA sticky ends that program tile interactions. We present a tile set optimization technique that, through analysis of algorithmic growth equivalence, potentially sensitive error pathways, and potential lattice defects, can significantly reduce the size of tile systems while preserving proofreading behavior that is essential for obtaining low error rates. Applied to systems implementing multiple algorithms that are far beyond the size of currently feasible implementations, the optimization technique results in systems that are comparable in size to already-implemented experimental systems.

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Acknowledgments

We thank Chigozie Nri, Philip Petersen, Lulu Qian, and Grigory Tikhomirov for discussions and collaboration on physical implementations and the Alhambra compiler, and Robert Johnson and William Poole for discussions on aTAM equivalence. This work was partially supported by the Evans Foundation and National Science Foundation award CCF-1317694.

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Authors and Affiliations

  1. Evans Foundation, Pasadena, CA, USA

    Constantine G. Evans

  2. California Institute of Technology, Pasadena, CA, USA

    Constantine G. Evans & Erik Winfree

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  1. Constantine G. Evans
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  2. Erik Winfree
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Correspondence to Constantine G. Evans .

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Editors and Affiliations

  1. University of California, Davis, CA, USA

    David Doty

  2. Technical University Munich, Garching, Germany

    Hendrik Dietz

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Evans, C.G., Winfree, E. (2018). Optimizing Tile Set Size While Preserving Proofreading with a DNA Self-assembly Compiler. In: Doty, D., Dietz, H. (eds) DNA Computing and Molecular Programming. DNA 2018. Lecture Notes in Computer Science(), vol 11145. Springer, Cham. https://doi.org/10.1007/978-3-030-00030-1_3

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  • DOI: https://doi.org/10.1007/978-3-030-00030-1_3

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  • Online ISBN: 978-3-030-00030-1

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