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Accurate prediction of genetic circuit behavior requires multidimensional characterization of parts

View ORCID ProfileGalen Dods, View ORCID ProfileMariana Gómez-Schiavon, View ORCID ProfileHana El-Samad, View ORCID ProfileAndrew H. Ng
doi: https://doi.org/10.1101/2020.05.30.122077
Galen Dods
1Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
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Mariana Gómez-Schiavon
1Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
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Hana El-Samad
1Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
2Chan–Zuckerberg Biohub, San Francisco, CA, USA
3Cell Design Institute, University of California, San Francisco, CA, USA
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  • For correspondence: andrew.ng@ucsf.edu hana.el-samad@ucsf.edu
Andrew H. Ng
3Cell Design Institute, University of California, San Francisco, CA, USA
4Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
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  • For correspondence: andrew.ng@ucsf.edu hana.el-samad@ucsf.edu
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Abstract

Mathematical models can aid the design of genetic circuits, but may yield inaccurate results if individual parts are not modeled at the appropriate resolution. To illustrate the importance of this concept, we study transcriptional cascades consisting of two inducible synthetic transcription factors connected in series. Despite the simplicity of this design, we find that accurate prediction of circuit behavior requires mapping the dose responses of each circuit component along the dimensions of both its expression level and its inducer concentration. With such multidimensional characterizations, we were able to computationally explore the behavior of 16 different circuit designs. We experimentally verified a subset of these predictions and found substantial agreement. This method of biological part characterization enables the use of models to identify (un)desired circuit behaviors prior to experimental implementation, thus shortening the design-build-test cycle for more complex circuits.

Competing Interest Statement

The authors have declared no competing interest.

  • Abbreviations

    iSynTF
    inducible synthetic transcription factor
    YFP
    yellow fluorescent protein
    GEM
    Gal4 DNA binding domain, estradiol ligand binding domain, Msn2 activating domain
    Z3PM
    Z3 DNA binding domain, progesterone ligand binding domain, Msn2 activating domain
    Z4EM
    Z4 DNA binding domain, estradiol ligand binding domain, Msn2 activating domain
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    Posted May 31, 2020.
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    Accurate prediction of genetic circuit behavior requires multidimensional characterization of parts
    Galen Dods, Mariana Gómez-Schiavon, Hana El-Samad, Andrew H. Ng
    bioRxiv 2020.05.30.122077; doi: https://doi.org/10.1101/2020.05.30.122077
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    Accurate prediction of genetic circuit behavior requires multidimensional characterization of parts
    Galen Dods, Mariana Gómez-Schiavon, Hana El-Samad, Andrew H. Ng
    bioRxiv 2020.05.30.122077; doi: https://doi.org/10.1101/2020.05.30.122077

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