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Published January 2018 | Published + Supplemental Material
Journal Article Open

Probabilistic switching circuits in DNA

Abstract

A natural feature of molecular systems is their inherent stochastic behavior. A fundamental challenge related to the programming of molecular information processing systems is to develop a circuit architecture that controls the stochastic states of individual molecular events. Here we present a systematic implementation of probabilistic switching circuits, using DNA strand displacement reactions. Exploiting the intrinsic stochasticity of molecular interactions, we developed a simple, unbiased DNA switch: An input signal strand binds to the switch and releases an output signal strand with probability one-half. Using this unbiased switch as a molecular building block, we designed DNA circuits that convert an input signal to an output signal with any desired probability. Further, this probability can be switched between 2^n different values by simply varying the presence or absence of n distinct DNA molecules. We demonstrated several DNA circuits that have multiple layers and feedback, including a circuit that converts an input strand to an output strand with eight different probabilities, controlled by the combination of three DNA molecules. These circuits combine the advantages of digital and analog computation: They allow a small number of distinct input molecules to control a diverse signal range of output molecules, while keeping the inputs robust to noise and the outputs at precise values. Moreover, arbitrarily complex circuit behaviors can be implemented with just a single type of molecular building block.

Additional Information

© 2018 National Academy of Sciences. This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND). Edited by David Baker, University of Washington, Seattle, WA, and approved December 22, 2017 (received for review September 10, 2017). Published ahead of print January 16, 2018. We thank D. Y. Zhang and E. Winfree for discussions. D.W., J.B., and L.Q. were supported by an NSF Expedition in Computing grant (0832824). L.Q. was also supported by a Career Award at the Scientific Interface from the Burroughs Wellcome Fund (1010684) and a Faculty Early Career Development Award from the NSF (1351081). Author contributions: J.B. and L.Q. designed research; D.W. and L.Q. performed research; D.W. and L.Q. analyzed data; and D.W., J.B., and L.Q. wrote the paper. The authors declare no conflict of interest. This article is a PNAS Direct Submission. This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1715926115/-/DCSupplemental.

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Supplemental Material - pnas.1715926115.sapp.pdf

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Created:
August 21, 2023
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October 18, 2023