The Metabolic Engineering Conference (ME16), taking place June 15–19, 2025, in the heart of Copenhagen, is the premier event for the field, offering a dynamic platform to explore cutting-edge methodologies, connect with top experts across industry and academia, and spark meaningful collaboration. In anticipation of this exciting gathering, we caught up with ME16 keynote speaker Nelson Barton, Executive Vice President and Chief Technology Officer at Geno. In this interview, he shares how Geno’s model-driven strategy and use of AI/ML are accelerating the path to cost-competitive, sustainable biomanufacturing—along with the trends he’s most excited to see shaping the future of metabolic engineering.
ME16 is known for bringing together top experts in metabolic engineering. From your perspective, what makes this conference a must-attend event, and what key discussions or innovations are you most looking forward to?
This conference brings together the leaders in the field. At Geno, our approach is “model, predict, engineer, scale,” with modeling serving as the foundation for everything we do. I’m especially looking forward to hearing about advances in metabolic modeling and to having conversations around how researchers are using—or planning to use—AI and machine learning to make even better predictions.
One of Geno’s key achievements is creating sustainable chemicals without requiring a market premium for sustainability. What strategies have been most effective in ensuring cost competitiveness and sustainable production?
Geno targets drop-in replacements for existing chemicals, so we already understand the production costs of best-available technology using fossil feedstocks. By using genome-scale and process models as inputs to our technoeconomic models, we can evaluate whether we’ll be cost competitive, assuming we hit the targets we believe are achievable for titer, rate, fermentation yield, and overall process yield.
Your talk at ME16 will highlight how Geno is leveraging AI and machine learning to shorten development timelines and reduce costs in biomanufacturing. Can you share an example of how these technologies are transforming the way high-yield microbial strains are developed?
We've already demonstrated we can develop cost-competitive, large-scale biomanufacturing processes, but we need to be able to do this faster and cheaper. To that end, we’re developing AI/ML tools to reduce both Design-Assemble-Test-Analyze (DATA) cycle times and the number of DATA cycles required to reach commercial performance targets for our commercial production strains. These tools have been developed to increase efficiency in a number of key areas, including enzyme engineering, pathway tuning, and data analysis.
Where do you see the future of industrial metabolic engineering heading, and what emerging trends are you most excited about?
The metabolic engineering community continues to develop and adopt tools that make it possible to commercialize a broader range of cost-competitive, scalable biomanufacturing processes. Newer tools like AI and machine learning will help us develop these processes more quickly and cheaply. The better we get at 1) developing production organisms with phenotypes customized and optimized for the most cost-competitive production processes, and 2) doing so at competitive development costs, the sooner biomanufacturing can become the preferred approach to producing chemicals and materials.
Learn more about the ME16 conference and register today.
About IMES
The International Metabolic Engineering Society promotes the use of metabolic engineering — the optimization of the genetic and regulatory processes within cells — as an enabling science for bio-based production of advanced materials, pharmaceuticals, food ingredients, chemicals, and fuels. One of its venues for collaboration and information exchange is the biannual Metabolic Engineering Conference, where practitioners share knowledge and discuss current developments made in the field.