Journal article
ACS synthetic biology, 2016
APA
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Magaraci, M. S., Bermudez, J. G., Yogish, D., Pak, D. H., Mollov, V., Tycko, J., … Chow, B. (2016). Toolbox for Exploring Modular Gene Regulation in Synthetic Biology Training. ACS Synthetic Biology.
Chicago/Turabian
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Magaraci, Michael S., Jessica G Bermudez, Deeksha Yogish, D. H. Pak, Viktor Mollov, Josh Tycko, D. Issadore, Sevile G. Mannickarottu, and B. Chow. “Toolbox for Exploring Modular Gene Regulation in Synthetic Biology Training.” ACS synthetic biology (2016).
MLA
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Magaraci, Michael S., et al. “Toolbox for Exploring Modular Gene Regulation in Synthetic Biology Training.” ACS Synthetic Biology, 2016.
BibTeX Click to copy
@article{michael2016a,
title = {Toolbox for Exploring Modular Gene Regulation in Synthetic Biology Training.},
year = {2016},
journal = {ACS synthetic biology},
author = {Magaraci, Michael S. and Bermudez, Jessica G and Yogish, Deeksha and Pak, D. H. and Mollov, Viktor and Tycko, Josh and Issadore, D. and Mannickarottu, Sevile G. and Chow, B.}
}
We report a toolbox for exploring the modular tuning of genetic circuits, which has been specifically optimized for widespread deployment in STEM environments through a combination of bacterial strain engineering and distributable hardware development. The transfer functions of 16 genetic switches, programmed to express a GFP reporter under the regulation of the (acyl-homoserine lactone) AHL-sensitive luxR transcriptional activator, can be parametrically tuned by adjusting high/low degrees of transcriptional, translational, and post-translational processing. Strains were optimized to facilitate daily large-scale preparation and reliable performance at room temperature in order to eliminate the need for temperature controlled apparatuses, which are both cost-limiting and space-constraining. The custom-designed, automated, and web-enabled fluorescence documentation system allows time-lapse imaging of AHL-induced GFP expression on bacterial plates with real-time remote data access, thereby requiring trainees to only be present for experimental setup. When coupled with mathematical models in agreement with empirical data, this toolbox expands the scalability and scope of reliable synthetic biology experiments for STEM training.