Small-Molecule Biosensors for High-Throughput Screening and Control of Cellular Metabolism

Engineering living cells to perform new-to-nature functions require both understanding of multiple interacting genes and cellular processes, and levers by which they can be independently screened and controlled for optimal performance. Allosterically regulated transcription factors and membrane-bound receptors have proven widely applicable in synthetic biology as ligand-specific biosensors enabling real-time monitoring, selection and dynamic regulation of cellular metabolism.

This presentation will initially focus on methods for rational and evolution-guided engineering of biosensors with user-defined transfer functions and small-molecule specificities. Next, applications of biosensor for screening, selection and control of cellular metabolism in the eukaryote model chassis, baker’s yeast Saccharomyces cerevisiae, will be presented. This includes examples on high-throughput screening of biosynthetic pathways, selection based on conditional growth, and robust safe-guarding of refactored metabolic pathways from evolutionary drifting. Lastly, the power of using real-time biosensor read-outs from diverse cellular designs to train machine learning algorithms for predictive engineering of cellular metabolism will be presented.

Taken together, the results presented leverage the modular design protein-based biosensors to enable monitoring, control, and predictive engineering of cellular metabolism.