Megan McClean’s Post

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Associate Professor at University of Wisconsin-Madison

Happy to share that work led by graduate students Zack Harmer and Jaron Thompson, with significant contributions from David Cole, is now out in the journal ACS Synthetic Biology. This work developed out of a productive collaboration with the research groups of Victor M Zavala and Ophelia Venturelli. Optogenetics leverages genetically encoded light-sensitive proteins to precisely control cellular behavior in response to light. An emerging limitation of optogenetics is that available wavelengths to stimulate these systems are limited, with many systems responding to blue light. In this work, we leverage Zack Harmer's previously published high-throughput optogenetics platform Lustro (https://lnkd.in/g6a5KrWA) and machine learning tools to enable multiplexed control over blue light sensitive optogenetic systems. We do this by leveraging the intensity and the dynamics of light delivery (rather than simply wavelength) as inputs to differentially control optogenetic split transcription factors. Lustro allowed rapid characterization of the response of 15 blue light-sensitive split transcription factors to different intensity, duty cycle, amplitude, and period of light delivery. From this initial characterization we were able to empirically identify pairs of split transcription factors that can be sequentially activated (where a first light program preferentially activates one optogenetic system and the second program activates both). We were also able to identify pairs of split transcription factors suitable for multiplexed control (where one system is more highly activated than another with one light condition, but under a different light condition, they switch and the second optogenetic system is more highly activated.) Using the high-throughput data that Lustro can generate, the Zavala lab built a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control. This work lays the foundation for more advanced synthetic biology driven by controllable optogenetic systems. For example, multiple synthetic biological circuit programs could be controlled using bespoke light induction programs. This has broad implications for biotechnology and bioengineering, by expanding how much biology can be controlled by a limited number of available inputs. This work was funded by the National Science Foundation, the National Institutes of Health, the Army Research Office, and the Burroughs Wellcome Fund.  More details can be found in the research article (https://lnkd.in/gkEkc3mM) or in the press release from the University of Wisconsin-Madison College of Engineering (https://lnkd.in/gXEMzMm7) #syntheticbiology #machinelearning #bioengineering #datascience

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