A new proof-of-concept study details how an automated system driven by artificial intelligence can design, build, test and learn complex biochemical pathways to efficiently produce lycopene, a red pigment found in tomatoes and commonly used as a food coloring, opening the door to a wide range of biosynthetic applications, researchers report. The results of the study, which combined a fully automated robotic platform called the Illinois Biological Foundry for Advanced Biomanufacturing with AI to achieve biomanufacturing, are published in the journal Nature Communications. "Biofoundries are factories that mimic the foundries that build semiconductors, but are designed for biological systems instead of electrical systems," said Huimin Zhao (BSD leader/CABBI/MMG), a University of Illinois chemical and biomolecular engineering professor who led the research. However, because biology offers many pathways to chemical production, the researchers assert that a system driven by AI and capable of choosing from thousands of experimental iterations is required for true automation. Previous biofoundry efforts have produced a wide variety of products such as chemicals, fuels, and engineered cells and proteins, the researchers said, but those studies were not performed in a fully automated manner.
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.
In contrast to current chemical manufacturing methods, characteristics inherent to bioconversion processes--such as the ability to operate at mild temperatures and pressures and achieve high carbon- and energy-conversion efficiencies in single-unit operations--result in more streamlined and less technologically complex processes. These characteristics enable flexible, smaller-scale, and capital expenditure–efficient operation that can both support and benefit from a large number of facilities, according to the economies of unit number model. For example, the capital expenditure entry-level cost of corn-grain ethanol facilities, the most widely developed current example of a bioconversion process, has substantially decreased as the number of plants has increased over the past few decades. This has facilitated rapid, small-scale, and widespread deployment resulting in a more than 10-fold increase in U.S. ethanol production from 1995 to 2015. Advances in metabolic engineering, synthetic biology, genomics, and industrial process design have pushed industrial biomanufacturing closer to more widespread adoption.
At one point or another, you will probably have opened the draw in the fridge to find a gunky red mess of a tomato past its prime. But a new study has found rotten or damaged fruit could soon be rescued from the bin and used to generate electricity. Researchers from the US have shown the waste crop could potentially be used in biological-based fuel cell to provide green energy. A study has found rotten or damaged fruit could soon be rescued from the bin and used to generate electricity. The fruit unfit to be sold or processed because of spoilage or damage is generally consigned to the rubbish tip.
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.