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Programmable Mixed-Signal Biocomputers in Mammalian Cells

Justin H. Letendre, Benjamin H. Weinberg, Marisa Mendes, Jeffery M. Marano, K. J. William Benman, Rachel Petherbridge, Kamila Drezek, Samantha E. Koplik, Alexandra Piñeiro, Wilson W. Wong
doi: https://doi.org/10.1101/2022.06.07.495130
Justin H. Letendre
1Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA 02215
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Benjamin H. Weinberg
1Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA 02215
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Marisa Mendes
1Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA 02215
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Jeffery M. Marano
1Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA 02215
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K. J. William Benman
1Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA 02215
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Rachel Petherbridge
1Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA 02215
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Kamila Drezek
1Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA 02215
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Samantha E. Koplik
1Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA 02215
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Alexandra Piñeiro
1Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA 02215
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Wilson W. Wong
1Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA 02215
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  • For correspondence: wilwong@bu.edu
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Abstract

Living cells perform sophisticated computations that guide them toward discrete states. Synthetic genetic circuits are powerful tools for programing these computations, where transcription-regulatory networks and DNA recombination are the two dominant paradigms for implementing these systems. While each strategy exhibits unique strengths and weaknesses, integrating both into one seamless design framework would enable advanced gene circuit designs intractable with either approach alone. Here, we present Computation via Recombinase Assisted Transcriptional Effectors (CREATE), which leverages site-specific recombination to perform robust logic on discreet computational layers and programmable transcription factors that connect these layers, allowing individual calculations to contribute toward larger operations. We demonstrate the functionality of CREATE by producing sophisticated circuits using a simple plug- and-play framework, including 189 2-input-3-output circuits, modular digital-to-analog signal converters, a 2-bit multiplier circuit, and a digital and analog mixed-signal generator. This work establishes CREATE as a versatile platform for programming complex signal processing systems capable of high-fidelity logic computation and tunable control over circuit output levels.

One-Sentence Summary We present a minimal and robust genetic circuit platform for programming cells with sophisticated signal processing capabilities.

Competing Interest Statement

WWW is a co-founder and shareholder of Senti Biosciences. BHW is a current employee of Tessera Therapeutics. JHL is a current employee of Strand Therapeutics. All other authors declare no competing interests.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 07, 2022.
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Programmable Mixed-Signal Biocomputers in Mammalian Cells
Justin H. Letendre, Benjamin H. Weinberg, Marisa Mendes, Jeffery M. Marano, K. J. William Benman, Rachel Petherbridge, Kamila Drezek, Samantha E. Koplik, Alexandra Piñeiro, Wilson W. Wong
bioRxiv 2022.06.07.495130; doi: https://doi.org/10.1101/2022.06.07.495130
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Programmable Mixed-Signal Biocomputers in Mammalian Cells
Justin H. Letendre, Benjamin H. Weinberg, Marisa Mendes, Jeffery M. Marano, K. J. William Benman, Rachel Petherbridge, Kamila Drezek, Samantha E. Koplik, Alexandra Piñeiro, Wilson W. Wong
bioRxiv 2022.06.07.495130; doi: https://doi.org/10.1101/2022.06.07.495130

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