SynBioBeta Speaker
Nigel Mouncey
DOE Joint Genome Institute
Director
Dr. Mouncey serves as the Director of the U.S. Department of Energy’s Joint Genome Institute at Lawrence Berkeley National Laboratory in California, a role he has held since March 2017. Dr. Mouncey is responsible for the strategic and operational leadership of the JGI, a National User Facility, that serves more than 2500 users with large-scale and advanced genomics capabilities. He also leads JGI’s Secondary Metabolites Science Program that weaves together sequencing, genome mining, synthetic biology and metabolomics to discover novel secondary metabolites, their biosynthetic pathways and functional characterization. Prior to joining JGI, Dr. Mouncey served for almost 20 years in a variety of R&D leadership roles in the Industrial Biotechnology private sector working for Roche Vitamins, DSM and Dow Agrosciences. During his time in industry, he directed R&D teams that focused on the discovery, development and commercialization of novel production organisms and fermentation processes for vitamins, insecticides, fungicides, platform chemicals, cosmetics and new crop traits. Dr. Mouncey also served as President of the Society for Industrial Microbiology and Biotechnology 2022-2023. In 2024, he was elected as both a AAAS Fellow and a SIMB Fellow. Dr. Mouncey has 42 peer-reviewed articles and 13 granted patents.
SynBioBeta 2026 Tickets are Live
Confirmed Speakers
Sessions Featuring
Nigel
This Year
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Tools & Tech
Berkeley Lab: Leading the way in DOE-funded AI for Biosciences
Berkeley Lab is transforming and creating large-scale, multi-modal data to train AI models for usable predictions. The Lab is creating data lakehouses that allow programmatic access for querying across data types. This effort includes generating integrated, multi-omics and high-quality datasets that allow modeling of dynamic biological systems; this requires accurate annotation, curation, and accompanying metadata.
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Tools & Tech
Berkeley Lab: Leading the way in DOE-funded AI for Biosciences
Berkeley Lab is transforming and creating large-scale, multi-modal data to train AI models for usable predictions. The Lab is creating data lakehouses that allow programmatic access for querying across data types. This effort includes generating integrated, multi-omics and high-quality datasets that allow modeling of dynamic biological systems; this requires accurate annotation, curation, and accompanying metadata.
Session lineup still growing
Featuring
Speaker Coming Soon
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Human Health
From Cells to Patients: Solving the Scale Mismatch in Virtual Biology
Drug discovery often measures biology at the cell level while interventions work at the tissue, organ, or whole-patient scale. This mismatch can make accurate cell-level predictions irrelevant in the clinic. This session dives into strategies to bridge that gap: multiscale modeling that nests single-cell dynamics within organ-level simulations, spatial transcriptomics that preserve context, and surrogate models that translate cell-level outputs into clinical biomarkers. Speakers will ask: how do we ensure virtual biology reflects not just what cells do in isolation, but how biology behaves in the real complexity of patients?
Featuring
Speaker Coming Soon
