BIO Seminar: Michael Krogh Jensen

Development and application of small-molecule biosensors for high-throughput screening and control of cellular metabolism

Speaker: Group Leader Michael Krogh Jensen, DTU

Host: Professor Karen Skriver, Section for Biomolecular Sciences

Abstract
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. Bacterial allosteric transcription factors (aTFs) constitute large protein families that sense a variety of small molecules in order for bacteria to adapt to environmental and cellular cues. Recently, aTFs have proven widely applicable for biotechnology and synthetic biology as ligand-specific biosensors of cell metabolism. However, both the biosensor specificity and the correlation between ligand concentration and biosensor output signal, also known as the transfer function, often needs to be optimized before meeting application needs in broader host ranges.
This presentation will initially focus on methods for rational and evolution-guided engineering of aTFs with user-defined transfer functions and small-molecule specificities. This includes characterization and development of transcriptional regulatory elements, as well as toggled selection regimes of aTF mutant libraries. Next, the application of aTFs 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 redox state, and robust safe-guarding of cells from evolutionary drifting. Lastly, ongoing research using real-time biosensor read-outs to train machine learning algorithms for predictive engineering of cellular metabolism will be presented.
Taken together, the results presented leverage the modular design and evolvability of aTF-based biosensors to enable monitoring, control, and predictive engineering of cellular metabolism.