Scientists from the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a way to use machine learning to dramatically accelerate the design of microbes that produce biofuel.
Their computer algorithm starts with abundant data about the proteins and metabolites in a biofuel-producing microbial pathway, but no information about how the pathway actually works. It then uses data from previous experiments to learn how the pathway will behave. The scientists used the technique to automatically predict the amount of biofuel produced by pathways that have been added to E. coli bacterial cells.
The new approach is much faster than the current way to predict the behavior of pathways, and promises to speed up the development of biomolecules for many applications in addition to commercially viable biofuels, such as drugs that fight antibiotic-resistant infections and crops that withstand drought.
Machine learning approaches, such as the technique recently developed by Berkeley Lab scientists, are hamstrung by a lack of large quantities of quality data. New automation capabilities at JBEI and the Agile BioFoundry will be able to produce these data in a systematic fashion. This video shows a liquid handler coupled with an automated fermentation platform at JBEI, which takes samples automatically to produce data for the machine learning algorithms. Fore rest of the story, click here: Lawrence Berkeley National Laboratory
