Letter

Complex cellular logic computation using ribocomputing devices

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Published online:

Abstract

Synthetic biology aims to develop engineering-driven approaches to the programming of cellular functions that could yield transformative technologies1. Synthetic gene circuits that combine DNA, protein, and RNA components have demonstrated a range of functions such as bistability2, oscillation3,4, feedback5,6, and logic capabilities7,8,9,10,11,12,13,14,15. However, it remains challenging to scale up these circuits owing to the limited number of designable, orthogonal, high-performance parts, the empirical and often tedious composition rules, and the requirements for substantial resources for encoding and operation. Here, we report a strategy for constructing RNA-only nanodevices to evaluate complex logic in living cells. Our ‘ribocomputing’ systems are composed of de-novo-designed parts and operate through predictable and designable base-pairing rules, allowing the effective in silico design of computing devices with prescribed configurations and functions in complex cellular environments. These devices operate at the post-transcriptional level and use an extended RNA transcript to co-localize all circuit sensing, computation, signal transduction, and output elements in the same self-assembled molecular complex, which reduces diffusion-mediated signal losses, lowers metabolic cost, and improves circuit reliability. We demonstrate that ribocomputing devices in Escherichia coli can evaluate two-input logic with a dynamic range up to 900-fold and scale them to four-input AND, six-input OR, and a complex 12-input expression (A1 AND A2 AND NOT A1*) OR (B1 AND B2 AND NOT B2*) OR (C1 AND C2) OR (D1 AND D2) OR (E1 AND E2). Successful operation of ribocomputing devices based on programmable RNA interactions suggests that systems employing the same design principles could be implemented in other host organisms or in extracellular settings.

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Acknowledgements

This work was supported by NIH Director’s New Innovator and Transformative Research Awards (1DP2OD007292, 1R01EB018659), an ONR Young Investigator Program Award (N000141110914) and grants (N000141010827, N000141310593, N000141410610, N000141612410), NSF CAREER and Expedition in Computing Awards (CCF1054898, CCF1317291) and grants (CCF1162459, ERASynBio 1540214), and Wyss Institute Molecular Robotics Initiative support to P.Y.; a DARPA Living Foundries grant (HR001112C0061) to P.A.S., P.Y., and J.J.C.; an ONR MURI Program grant, a DTRA grant (HDTRA1-15-1-0040), and Paul G. Allen Frontiers Group funds to J.J.C.; and an Arizona Biomedical Research Commission New Investigator Award, an Alfred P. Sloan Research Fellowship (FG-2017-9108), and Arizona State University funds to A.A.G. J.K. acknowledges a Wyss Institute Director’s Cross-Platform Fellowship.

Author information

Author notes

    • Alexander A. Green
    •  & Jongmin Kim

    These authors contributed equally to this work.

Affiliations

  1. Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts 02115, USA

    • Alexander A. Green
    • , Jongmin Kim
    • , Pamela A. Silver
    • , James J. Collins
    •  & Peng Yin
  2. Biodesign Center for Molecular Design and Biomimetics, Biodesign Institute and School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, USA

    • Alexander A. Green
    •  & Duo Ma
  3. Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, USA

    • Jongmin Kim
    • , Pamela A. Silver
    •  & Peng Yin
  4. Institute for Medical Engineering and Science, Department of Biological Engineering, and Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

    • James J. Collins
  5. Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA

    • James J. Collins

Authors

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  2. Search for Jongmin Kim in:

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Contributions

A.A.G. conceived the study, designed and performed the experiments, analysed the data, supervised D.M. and wrote the paper. J.K. conceived the study, designed and performed the experiments, analysed the data and wrote the paper. D.M. performed experiments and analysed the data. P.A.S. supervised the study. J.J.C. supervised the study. P.Y. conceived and supervised the study, interpreted the data, and wrote the paper. All authors reviewed and approved the manuscript.

Competing interests

A provisional US patent has been filed based on this work. P.Y. is the co-founder of Ultivue Inc. and NuProbe Global.

Corresponding authors

Correspondence to Alexander A. Green or Peng Yin.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains Supplementary Methods and Data, a Supplementary Discussion and Supplementary Table 10, a comparison of toehold switch design parameters.

Excel files

  1. 1.

    Supplementary Table 1

    This file contains the major conserved sequences Used.

  2. 2.

    Supplementary Table 2

    Sequences and Output Characteristics of AND-Computing Toehold Switches.

  3. 3.

    Supplementary Table 3

    Sequences for two-input OR gate circuit.

  4. 4.

    Supplementary Table 4

    Sequences for AND gate circuits.

  5. 5.

    Supplementary Table 5

    Sequences for A AND (NOT B) circuit.

  6. 6.

    Supplementary Table 6

    Sequences for the six-input OR gate circuit.

  7. 7.

    Supplementary Table 7

    Sequences for four- and five-input OR gate circuits constructed from AND-computing toehold switches.

  8. 8.

    Supplementary Table 8

    Sequences used for 11-input dual gate circuit shown in Extended Data Fig. 7e-g.

  9. 9.

    Supplementary Table 9

    Sequences for disjunctive normal form (DNF) circuits.

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