Biofuel


Newly Developed Machine Learning Approach Could Accelerate Bioengineering

#artificialintelligence

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 behaviour 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.


New machine learning approach could accelerate bioengineering

#artificialintelligence

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. The research is published May 29 in the journal npj Systems Biology and Applications.


Scientists Use Machine Learning to Speed Up Biofuel Production

#artificialintelligence

Researchers from the U.S. Department of Energy's Lawrence Berkeley National Laboratory have created a new method using machine learning to accelerate the design of microbes that produce biofuel. To speed up the production of biofuels, the scientists developed a computer algorithm that begins with abundant data about the proteins and metabolites in a biofuel-producing microbial. However, the algorithm does not contain information about how the pathway actually work and instead uses data from previous experiments to learn how the pathway will behave. This new technique enables scientists to automatically predict the amount of biofuel produced by pathways that have been added to E. coli bacterial cells. A pathway is a series of chemical reactions in a cell that produce a specific compound, which researchers have sought to find ways to re-engineer and import from one microbe to another.


New machine learning approach could accelerate bioengineering

#artificialintelligence

IMAGE: A new approach developed by Zak Costello (left) and Hector Garcia Martin brings the the speed and analytic power of machine learning to 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.


New machine learning approach could accelerate bioengineering

#artificialintelligence

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.


New Machine Learning Approach Could Accelerate Bioengineering

#artificialintelligence

A new approach developed by Zak Costello (left) and Hector Garcia Martin brings the the speed and analytic power of machine learning to 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.