SynBioBeta Speaker
Sharroll Bachas
Onava AI
Co-founder & CSO
Sharrol Bachas is a biophysicist, structural biologist, and protein engineer working at the intersection of AI and protein design. He is Co-founder and Chief Scientific Officer at Onava, where he is building a next-generation platform for therapeutic protein design—combining generative models, protein engineering, structural biology, and a proprietary high-throughput wet lab to rapidly iterate on antibodies, peptides, and miniproteins for design and optimization.
Prior to founding Onava, Sharrol led AI-driven antibody engineering programs at Absci, directed scaled AI–wet lab implementation of protein design workflows at EvolutionaryScale, and helped shape foundational infrastructure for integrating structural biology with machine learning in drug discovery. His work spans de novo design, structural grafting, display-based screening, and biophysics, with a strong emphasis on human-in-the-loop design cycles.
Sharrol earned his Ph.D. in Biophysics and Biophysical Chemistry from Johns Hopkins University School of Medicine, and completed his postdoctoral research at the NIH, focusing on structure–function relationships of proteins and RNAs using X-ray crystallography.
SynBioBeta 2026 Tickets are Live
Confirmed Speakers
Sessions Featuring
Sharroll
This Year
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Tools & Tech
Closing the Loop at 10ⁿ Scale: The Autonomous DBTL Stack
The Design–Build–Test–Learn (DBTL) cycle remains the core engine of biological engineering, yet its iteration speed still lags far behind software development. As AI systems begin to design, plan, and execute experiments, a new paradigm is emerging: DBTL as an autonomous, continuously optimizing system. Next-generation platforms combine AI-assisted rational design, high-throughput construction and perturbation, real-time data acquisition, and active learning to close the loop at unprecedented scale. Agent-powered lab-in-a-loop workflows, lab-on-a-chip systems, and advances at the silicon-to-carbon interface are enabling tighter integration between computation and biology, from semiconductor-enabled sensing to real-time feedback and decision-making. This session explores how autonomous DBTL stacks could unlock software-like iteration velocity in biology, redefine experimentation, and reshape the future of programmable discovery.
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Tools & Tech
Closing the Loop at 10ⁿ Scale: The Autonomous DBTL Stack
The Design–Build–Test–Learn (DBTL) cycle remains the core engine of biological engineering, yet its iteration speed still lags far behind software development. As AI systems begin to design, plan, and execute experiments, a new paradigm is emerging: DBTL as an autonomous, continuously optimizing system. Next-generation platforms combine AI-assisted rational design, high-throughput construction and perturbation, real-time data acquisition, and active learning to close the loop at unprecedented scale. Agent-powered lab-in-a-loop workflows, lab-on-a-chip systems, and advances at the silicon-to-carbon interface are enabling tighter integration between computation and biology, from semiconductor-enabled sensing to real-time feedback and decision-making. This session explores how autonomous DBTL stacks could unlock software-like iteration velocity in biology, redefine experimentation, and reshape the future of programmable discovery.
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Session lineup still growing
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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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