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Dynamic Multiplexed Control and Modeling of Optogenetic Systems Using the High-Throughput Optogenetic Platform,

Zachary P Harmer, Jaron C Thompson, David L Cole

    Biorxiv : the Preprint Server for Biology
    |January 8, 2024
    PubMed
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    Researchers developed a new optogenetics method using machine learning and a high-throughput platform to control cellular processes with blue light. This enables precise, multiplexed activation of light-sensitive systems in yeast.

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    Area of Science:

    • Synthetic biology
    • Optogenetics
    • Biotechnology

    Background:

    • Optogenetics offers precise control over cellular processes but is limited by the need for specific inducers, often blue light.
    • Existing systems struggle with simultaneous or sequential control of multiple optogenetic tools.

    Approach:

    • Developed an integrated framework combining the Lustro high-throughput optogenetics platform with machine learning tools.
    • Utilized Bayesian optimization for data-driven learning, uncertainty quantification, and experimental design.
    • Identified optimal light induction conditions for sequential and preferential activation of split transcription factors in *Saccharomyces cerevisiae*.

    Key Points:

    • Achieved multiplexed control over blue light-sensitive optogenetic systems.
    • Demonstrated sequential activation and switching between split transcription factors.
    • Enabled prediction of system behavior and identification of optimal control conditions.

    Conclusions:

    • This work overcomes inducer limitations in optogenetics by enabling precise, multiplexed control using light.
    • Lays the foundation for advanced synthetic biological circuits with designer light induction programs.
    • Has broad implications for biotechnology and bioengineering applications.