Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation...

Area of Science:

• Synthetic Biology
• Biotechnology
• Genetic Engineering

Background:

• Genetically encoded biosensors enable high-throughput signal processing for gene expression regulation.
• Optimizing biosensor performance involves modifying circuit components and host interactions.
• The combinatorial complexity of biosensor design necessitates efficient screening strategies.

Purpose of the Study:

• To develop a combined high-throughput automation and computational approach for efficient biosensor design space exploration.
• To optimize allosteric transcription factor-based biosensors for distinct digital and analogue dose-response curves.
• To provide an agnostic framework for the development of future biosensor systems and genetic circuits.

Main Methods:

• Creation and automated selection of promoter and ribosome binding site libraries.
• Transformation of library data into dimensionless inputs for computational mapping of the design space.
• Fractional sampling using Design of Experiments (DoE) algorithms coupled with effector titration analysis.

Main Results:

• Efficient sampling of the biosensor design space was achieved.
• Distinct biosensor configurations with digital and analogue dose-response curves were identified.
• A statistically-based, structured mapping of the combinatorial experimental design space was established.

Conclusions:

• The developed workflow offers an agnostic framework for biosensor and genetic circuit optimization.
• This approach provides a regulatory toolkit for the synthetic biology community.
• High-throughput automation and computational methods significantly enhance biosensor development efficiency.