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SynBioBeta Speaker

George Peabody

Ginkgo Bioworks

Program Lead

Passionate about deploying synthetic biology to unlock industrial-scale biomanufacturing, George L. Peabody V earned his Ph.D. in Chemical Engineering from Texas A&M University and completed a postdoctoral fellowship at Oak Ridge National Laboratory, where he engineered non-model microorganisms for conversion of lignocellulosic feedstocks into valuable chemicals. As an Associate Director and Program Lead at Ginkgo Bioworks, he has led a diverse portfolio of strain engineering programs spanning small molecules, whole cell biocatalysts, and proteins in both yeast and bacteria — translating complex biological insights into scalable production systems and generating significant R&D value across multiple industrial partnerships.His recent work centers on adapting and running cell engineering R&D programs on Ginkgo's autonomous lab platform, pushing the boundaries of what is possible when biology meets automation and AI. This includes the development of end-to-end autonomous DNA-to-data workflows including engagement with AI-enabled protocol construction, launch, and evaluation. George believes that the convergence of AI, synthetic biology, and autonomous systems represents one of the most powerful levers available to unlock the power of biology in our fundamentally biological world.

Sessions Featuring

George

This Year

Breakout Session

4:30 PM

-

5:15 PM

Tools & Tech

AI Co-Scientists: From Pipettes to Protocols

Biology is entering an era where AI agents don’t just analyze data — they co-design, plan, and execute experiments. Multi-agent systems like CRISPR-GPT demonstrate how AI can act as a true lab co-pilot: decomposing complex genome editing projects into stepwise workflows, selecting tools, troubleshooting, and even drafting protocols that allow junior researchers to perform sophisticated edits on their first attempt . Beyond CRISPR, new systems like BioMARS integrate reasoning agents with robotics, while biotech companies are testing “AI lab assistants” that monitor and adjust experiments in real time. This session explores how multi-agent copilots are making biology more reproducible, democratizing complex workflows, and pushing the boundaries of lab autonomy. The central question: when AI can plan, troubleshoot, and validate experiments end-to-end, how should scientists and institutions govern this new power?

Breakout Session

4:30 PM

-

5:15 PM

Tools & Tech

AI Co-Scientists: From Pipettes to Protocols

Biology is entering an era where AI agents don’t just analyze data — they co-design, plan, and execute experiments. Multi-agent systems like CRISPR-GPT demonstrate how AI can act as a true lab co-pilot: decomposing complex genome editing projects into stepwise workflows, selecting tools, troubleshooting, and even drafting protocols that allow junior researchers to perform sophisticated edits on their first attempt . Beyond CRISPR, new systems like BioMARS integrate reasoning agents with robotics, while biotech companies are testing “AI lab assistants” that monitor and adjust experiments in real time. This session explores how multi-agent copilots are making biology more reproducible, democratizing complex workflows, and pushing the boundaries of lab autonomy. The central question: when AI can plan, troubleshoot, and validate experiments end-to-end, how should scientists and institutions govern this new power?

TBD

Session lineup still growing

Featuring

Speaker Coming Soon

Fireside Chat

12:00 AM

-

8:30 AM

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

Previous Speakers Include