Professor Stefano Lonardi has received a new NSF grant of 1.5M over three years on "Data-driven engineering of the thermotolerant yeast Kluyveromyces marxianus". The project is in collaboration with Ian Wheeldon (Chem Env Engineering, UCR), Nancy Da Silva (UCI), and Argonne National Laboratory.

The abstract of the project:

"We propose to develop new data-driven approaches for engineering the stress-tolerant yeast Kluyveromyces marxianus for the synthesis of biobased chemicals and fuels. Innovative experimental and computational approaches will be combined to significantly advance the Design-Build-Test-Learn cycle for K. marxianus. A deep learning architecture will be used to design optimized sgRNA libraries to generate genetic diversity. The build stage of the cycle will leverage efficient CRISPR-Cas9 technologies for gene disruption and regulation. A biosensor-driven approach to testing will enable high throughput analysis of the effect of host genetics on the production malonyl-CoA. The datasets generated in the testing stage will be used as input to a new algorithm that will learn how to link genotypes to phenotypes."