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

Area of Science:

• Synthetic Biology
• Optogenetics
• Biotechnology

Background:

• Optogenetics offers precise control over cellular processes but is limited by inducer dependence and reliance on blue light.
• Existing optogenetic systems often struggle with multiplexed control, hindering the development of complex synthetic biological circuits.

Purpose of the Study:

• To overcome the limitations of current optogenetic systems by enabling multiplexed control over blue light-sensitive optogenetic tools.
• To develop a framework for precise, sequential, and preferential activation of multiple optogenetic systems in yeast.

Main Methods:

• Utilized Lustro, a high-throughput optogenetics platform, for generating extensive experimental data.
• Integrated machine learning, specifically a Bayesian optimization framework, with high-throughput data.
• Applied data-driven learning, uncertainty quantification, and experimental design for predicting system behavior.

Main Results:

• Identified specific light induction conditions for sequential and preferential activation of split transcription factors in *Saccharomyces cerevisiae*.
• Successfully demonstrated multiplexed control over blue light-sensitive optogenetic systems.
• Developed a predictive model for optimizing conditions in optogenetic circuit design.

Conclusions:

• The integrated framework of Lustro and machine learning enables advanced multiplexed control of optogenetics.
• This approach overcomes limitations in current optogenetic systems, paving the way for sophisticated synthetic biological circuits.
• The findings have broad implications for the fields of biotechnology and bioengineering, enabling designer light induction programs.