| Project Detail |
The ability of cells to sense and respond to signals is an essential requirement of life. Genetically encoded biosensors meet this need by detecting, for example, chemicals and triggering gene expression in response. This concept is used across the life sciences to sense molecules in basic research, diagnostics and treatment. Crucially, biosensors can be used to isolate and engineer microbes that sustainably produce value-added chemicals and thus play a key role in the transition to a circular economy. However, native biosensors are mostly unfit for synthetic applications in terms of molecules and concentrations they respond to. Moreover, little is known about the relationship between biosensor sequence and resulting function, which prohibits rational biosensor engineering and enforces tedious, often unsuccessful trial-and-error approaches.
I propose to build a pipeline for the rational engineering of biosensors with tailored sensory properties to overcome these limitations. Building upon an ultrahigh-throughput DNA-recording technique we have recently invented, we will generate hitherto inaccessible datasets linking over 10^8 transcriptional and translational biosensor sequences with their sensory properties and use these data to train deep learning models that infer biosensor function directly from sequence. This will enable straightforward biosensor design, which we will capitalize on to build a versatile biosensing platform to specifically detect and discriminate molecules from three metabolic compound classes with high potential for bio-based production. Finally, we will apply designed biosensors to engineer new enzymes for CO2-fixation and build dynamic metabolic controllers to obtain superior bacterial strains for the production of flavors and pharmaceuticals. Our novel, data-driven approach will break new grounds in biosensor engineering through synergies between synthetic biology and artificial intelligence paving the way to novel, sustainable bioprocesses. |
