SynBioBeta Speaker
Hector Garcia Martin
LBNL
Staff Scientist
Hector Garcia Martin was born in Bilbao, part of the Basque region in Spain. Hector studied physics and specialized in solid state physics at the University of the Basque Country. He obtained his Ph. D in condensed matter physics from the University of Illinois at Urbana-Champaign, where he studied Bose Einstein Condensates and scaling laws in ecology. His interest in using theoretical physics tools to make biology predictable led him to join the Department of Energy Joint Genome Institute, where he worked on studying microbial communities through metagenomics as a postdoctoral fellow. Pursuing the opportunity to improve predictive models in biology through synthetic biology, he became a group lead at Berkeley Lab in 2007, where he is part of the Joint BioEnergy Institute and the Agile BioFoundry programs. In his current role, he combines machine learning, mechanistic models, automation, microfluidics and genetic editing techniques to effectively guide the metabolic engineering process and provide some of the first examples in predictive synthetic biology.
SynBioBeta 2026 Tickets are Live
Confirmed Speakers
Sessions Featuring
Hector
This Year
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Tools & Tech
Self-Driving Labs, AI, and Automation: A Practical Guide to Getting Started
AI-enabled, self-driving labs are still emerging, but their foundations are already transforming how teams design, run, and interpret experiments. This session offers a practical guide for scientists and R&D leaders who want to understand what can be done today — from tightening design–test–learn loops to reducing manual error and capturing early benefits of autonomous experimentation. Rather than presenting an unrealized future, speakers will focus on practical, real-world steps that give organizations a competitive edge as SDL capabilities evolve and mature. Speakers will explore what’s working, what’s not, and how autonomous lab systems are reshaping protein engineering, pathway optimization, and therapeutic design.
Purchase Pass
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Tools & Tech
Self-Driving Labs, AI, and Automation: A Practical Guide to Getting Started
AI-enabled, self-driving labs are still emerging, but their foundations are already transforming how teams design, run, and interpret experiments. This session offers a practical guide for scientists and R&D leaders who want to understand what can be done today — from tightening design–test–learn loops to reducing manual error and capturing early benefits of autonomous experimentation. Rather than presenting an unrealized future, speakers will focus on practical, real-world steps that give organizations a competitive edge as SDL capabilities evolve and mature. Speakers will explore what’s working, what’s not, and how autonomous lab systems are reshaping protein engineering, pathway optimization, and therapeutic design.
Purchase Pass
Session lineup still growing
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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?
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Featuring
Speaker Coming Soon
