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 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.