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

Genetically encoded biosensors are powerful tools for high-throughput information processing, enabling the transduction of environmental or chemical input signals into a variety of outputs. This allows dynamic sensing, direct control, and fine-tuned regulation of gene expression across a wide range of biotechnological applications, including enzyme optimization, strain development, and microbial process control. To make them fit for purpose, biosensor performance can be refined by modifying the stoichiometry of biosensor circuit components (e.g., transporters, input and output modules), and/or tuning associated host-biosensor intermolecular interactions (e.g., DNA-protein, protein-protein). However, here, the vast number of possible biosensor permutations creates a complex combinatorial design space, necessitating careful optimization of screening strategies to identify configurations that deliver the desired phenotypic performance. This complexity is further compounded by biosensor performance traits, such as tunability, which require effector titration analysis under monoclonal screening conditions. Consequently, the need to explore diverse sequences and experimental space makes fractional sampling methods particularly well-suited for this purpose. Underpinning this workflow are Design of Experiment (DoE) algorithms, which are well-positioned to allow efficient statistically-based structured mapping and fractional sampling of this combinatorial experimental design space. Reported within is a combined high-throughput automation and computational approach to efficiently sample the design space of allosteric transcription factor-based biosensors to afford distinct configurations with both digital and analogue dose-response curves. The protocol begins with the creation and automated selection of promoter and ribosome binding site libraries. These libraries, and their corresponding expression data, are transformed into structured dimensionless inputs, allowing computational mapping of the full experimental design space. Fractional sampling is then performed using a DoE algorithm and coupled with effector titration analysis using a high-throughput automation platform. This workflow provides an agnostic framework for the development and optimization of future biosensor systems and genetic circuits, providing a regulatory toolkit for the synthetic biology community.