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Synthetic mixed-signal computation in living cells

Author

Listed:
  • Jacob R. Rubens

    (Synthetic Biology Group, MIT Synthetic Biology Center, Research Laboratory of Electronics, Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Microbiology Program, Massachusetts Institute of Technology)

  • Gianluca Selvaggio

    (Synthetic Biology Group, MIT Synthetic Biology Center, Research Laboratory of Electronics, Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Computational and System Biology Group, Centre for Neuroscience and Cell Biology, University of Coimbra)

  • Timothy K. Lu

    (Synthetic Biology Group, MIT Synthetic Biology Center, Research Laboratory of Electronics, Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Microbiology Program, Massachusetts Institute of Technology
    The Center for Microbiome Informatics and Therapeutics)

Abstract

Living cells implement complex computations on the continuous environmental signals that they encounter. These computations involve both analogue- and digital-like processing of signals to give rise to complex developmental programs, context-dependent behaviours and homeostatic activities. In contrast to natural biological systems, synthetic biological systems have largely focused on either digital or analogue computation separately. Here we integrate analogue and digital computation to implement complex hybrid synthetic genetic programs in living cells. We present a framework for building comparator gene circuits to digitize analogue inputs based on different thresholds. We then demonstrate that comparators can be predictably composed together to build band-pass filters, ternary logic systems and multi-level analogue-to-digital converters. In addition, we interface these analogue-to-digital circuits with other digital gene circuits to enable concentration-dependent logic. We expect that this hybrid computational paradigm will enable new industrial, diagnostic and therapeutic applications with engineered cells.

Suggested Citation

  • Jacob R. Rubens & Gianluca Selvaggio & Timothy K. Lu, 2016. "Synthetic mixed-signal computation in living cells," Nature Communications, Nature, vol. 7(1), pages 1-10, September.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms11658
    DOI: 10.1038/ncomms11658
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    Cited by:

    1. Yang Gao & Yuchen Zhou & Xudong Ji & Austin J. Graham & Christopher M. Dundas & Ismar E. Miniel Mahfoud & Bailey M. Tibbett & Benjamin Tan & Gina Partipilo & Ananth Dodabalapur & Jonathan Rivnay & Ben, 2024. "A hybrid transistor with transcriptionally controlled computation and plasticity," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Luna Rizik & Loai Danial & Mouna Habib & Ron Weiss & Ramez Daniel, 2022. "Synthetic neuromorphic computing in living cells," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    3. Yuanli Gao & Lei Wang & Baojun Wang, 2023. "Customizing cellular signal processing by synthetic multi-level regulatory circuits," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

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