SynBioBeta Speaker
Bingyin (Brian) Wang
LinkZill
Dir. of Life Sciences BU
Bingyin (Brian) Wang, Ph.D, is a seasoned subject matter expert with over 15 years of specialized experience in genomics, with a core focus on Synthetic Biology alongside CRISPR genome editing and NGS technologies. Boasting a dual academic and commercial background across the US and China, he has excelled in cross-functional roles spanning sales, business development, product management, and application development, driving global business growth and operational excellence.
SynBioBeta 2026 Tickets are Live
Confirmed Speakers
Sessions Featuring
Bingyin (Brian)
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.
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
