In the United States (US), Lygos, Inc., a vertically integrated provider of safe and sustainable specialty ingredients, has announced that it is working with the US Department of Energy Bioenergy Technologies Office’s (BETO) Agile BioFoundry (ABF) consortium to generate the largest multi-omics dataset for guiding the development of organic acids. Over the course of the project, scientists will produce more than 500 000 data points from a series of experiments.
Lygos has created a fully integrated biological engineering platform focused on organic acid specialty ingredients, and health and wellness ingredients with funding from the US Department of Energy (DOE), US Department of Agriculture (USDA), National Science Foundation, and National Institutes of Health amongst other institutional and strategic investors.
The ABF consortium develops and deploys technologies that enable commercially relevant biomanufacturing, including using advanced machine learning methods in non-model microbes. ABF is working with Lygos to generate the largest multi-omics dataset by using its artificial neural networks to train machine learning algorithms and provide actionable recommendations to help optimize strain performance, increase operational efficiencies and enhance production guiding the development of organic acids.
The Agile BioFoundry’s objective is to develop and deploy technologies that enable commercially-relevant biomanufacturing, including using advanced machine learning methods in non-model microbes. We’re pleased to be able to showcase our success with industry partners like Lygos, a leader in the bioeconomy, said Dr Jay Fitzgerald, Chief Scientist at US DOE BETO.
Together, Lygos and ABF aim to demonstrate a high-throughput engineering cycle that incorporates multi-omics analysis and machine learning with industry-leading cycle times. The project leverages Lygos’ expertise in designing, building, and cultivating its organic acid yeast strains, as well as its malonic acid biosynthetic pathway.
Over the course of the project, scientists will produce more than 500 000 data points from a series of experiments. ABF and Lygos will demonstrate a high-throughput engineering cycle that incorporates multi-omics analysis and machine learning with industry-leading cycle times.
The multi-omics analysis is performed at Lawrence Berkeley National Laboratory, Pacific Northwest National Laboratory (PNNL), and Sandia National Laboratories. Scientists are applying these machine learning techniques to analyze the datasets and make predictions on how to increase malonic acid production in subsequent cycles. The teams plan on making the workflow and dataset available to the public later this year.
Lygos is the first and only company to demonstrate the production of a bio-based analog of malonic acid that provides identical performance without the incumbent toxic production process. This project with the ABF is another validation of our commitment to leveraging the latest advances in high-throughput analytics and data science. The combination of this multi-omics dataset and these new machine learning capabilities will help unlock new opportunities to further improve the performance and production of our safe and sustainable organic acids and health and wellness ingredients, said Dr Eric Steen, CEO of Lygos.
