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New Machine Learning Approach Could Accelerate Bioengineering

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.

A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data (open, DOI: 10.1038/s41540-018-0054-3) (DX)

Original Submission