Héctor García Martin and Zak Costello, scientists affiliated with the DOE Agile BioFoundry, the Joint BioEnergy Institute (JBEI), and the Biological Systems and Engineering Division 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 research is published online in Nature Partner Journal Systems Biology and Applications. Read the Berkeley Lab News Center feature story.
