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

Philip Le Roy1, Guadalupe Alvarez-Gonzalez1, Micaela Chacón1

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Genetically encoded biosensors are optimized using a high-throughput automation and computational approach. This method efficiently maps and samples the design space of biosensors for improved gene expression control in synthetic biology.

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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.