Concentration-Based Polynomial Calculations on Nicked DNA

Tonglin Chen, Marc Riedel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we introduce a novel scheme for computing polynomial functions on a substrate of nicked DNA. We first discuss a fractional encoding of data, based on the concentration of nicked double DNA strands. Then we show how to perform multiplication on this representation. Next we describe the read-out process, effected by releasing single strands. We show how to perform simple mathematical operations such as addition and subtraction, as well as how to scale constant values using probabilistic switches. We also describe two complex operations: calculating a vector dot product and computing a general polynomial function. We conclude by discussing potential applications of our scheme, practical challenges, and future research directions.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8836-8840
Number of pages5
ISBN (Electronic)9781509066315
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: May 4 2020May 8 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
CountrySpain
CityBarcelona
Period5/4/205/8/20

Bibliographical note

Funding Information:
This work was funded by DARPA Grant #W911NF-18-2-0032. We thank David Soloveichik, Olgica Milenkovic and Boya Wang for helpful discussions.

Publisher Copyright:
© 2020 IEEE.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • DNA Computing
  • DNA Strand Displacement
  • Stochastic Computing

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