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New Machine Learning Approach Could Accelerate Bioengineering


Lawrence Berkeley National Laboratory researchers Zak Costello (left) and Hector Garcia Martin.

Lawrence Berkeley National Laboratory researchers have developed a predictive machine learning method to speed the design of biofuel-generating microbes.

Credit: Marilyn Chung/Berkeley Lab

A team at the Lawrence Berkeley National Laboratory (Berkeley Lab) has developed a predictive machine learning method to speed the design of biofuel-generating microbes.

Their algorithm is fed data about proteins and metabolites in a biofuel-producing microbial pathway of chemical reactions, then applies data from earlier experiments to determine the pathway's behavior.

With this method, the team has predicted how much biofuel can be generated by pathways introduced to E. coli bacterial cells.

"This approach could expedite the time it takes to design new biomolecules," says Berkeley Lab's Hector Garcia Martin. "A project that today takes 10 years and a team of experts could someday be handled by a summer student."

From Berkeley Lab News Center
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