SynBioBeta Speaker
Eduardo Abeliuk
TeselaGen
Founder & CEO
Eduardo Abeliuk is the Founder and CEO of TeselaGen Biotechnology, a company building an AI-powered operating system for biological R&D. His work focuses on applying machine learning and agentic AI to accelerate the Design-Build-Test-Learn (DBTL) cycle, enabling scientists to design, execute, and optimize biological systems more efficiently.His expertise sits at the intersection of synthetic biology, computational biology, and artificial intelligence. He has led the development of platforms that integrate DNA design, laboratory automation, and data-driven modeling to improve pathway engineering, strain optimization, and biomanufacturing workflows. His current focus is on deploying autonomous AI agents that can design experiments, analyze results, and recommend next steps, shifting from passive tools to systems that actively execute scientific work.Eduardo has worked closely with leading biofoundries, national laboratories, and industrial biotech organizations, contributing to large-scale efforts in sustainable chemicals, biofuels, and next-generation biomanufacturing. He is also an advisor to the Agile BioFoundry and has been involved in initiatives aligned with the U.S. Department of Energy’s efforts to advance bioindustrial innovation.His research has been published in scientific journals, holds multiple U.S. patents in computational biology and artificial intelligence, has co-founded and advised several technology companies . Eduardo is particularly interested in how AI, especially agentic systems, can transform scientific discovery and product development from a largely manual, iterative process into a more autonomous, scalable, and predictive discipline, enabling faster innovation across academia, biopharma, and industrial biotechnology. He holds an M.S. in Bioengineering and a Ph.D. in Electrical Engineering from Stanford University.
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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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