The use of living cells as microfactories for the production of desired biomolecules offers great promise, but the optimization of these processes is a high-dimensional problem. Genetic, metabolic, and environmental factors all influence the titer of the target molecule, leading to a vast design space. While important successes have been made in the field of industrial metabolic engineering, a rigorous framework to methodically explore the design space and identify truly optimal conditions is lacking.
Funding: $30K (2023)
Goal: Combine laboratory automation and machine learning to engineer a riboflavin-overproducing strain of the Gram-positive bacterium Streptococcus mutans.
Token Investors: Paul Jensen and Ryan Wyllie
Project ID: 1125