Massively parallel construction and profiling of genetic-encoded biosensors in yeast

Genetically encoded biosensors are valuable tools used in the precise engineering of metabolism. Although a large number of biosensors have been developed, the fine-tuning of their dose-response curves, which promotes the applications of biosensors in various scenarios, still remains challenging. To address this issue, we leverage DNA trackable assembly method and FACS-seq technology to set up a novel workflow for construction and comprehensive characterization of thousands of biosensors in a massively parallel manner.The FapR-fapO-based malonyl-CoA biosensor was used as a proof of concept to construct a trackable combinatorial library, containing 5184 combinations with 6 levels of transcription factor dosage, 4 different operator positions, and 216 possible UAS designs. By applying FACS-seq technique, the response curves of 2,632 biosensors out of 5184 combinations were successfully characterized to provide large-scale genotype-phenotype association data of the designed biosensors. Finally, machine learning algorithms were applied to predict the genotype-phenotype relationships of the uncharacterized combinations to generate a panoramic scanning map of the combinatorial space.Taken together, our pipe line provides a platform for the design, tuning and profiling of biosensor response curves, and shows a great potential to facilitate the rational design of the genetic circuits.