SynBioBeta Speaker
Gleb Kuznetsov
Manifold Bio
Co-founder & CEO
Gleb Kuznetsov is Co-Founder and CEO of Manifold Bio, a company combining AI protein design with generation of biological data at massive scale and physiological relevance to build organism-scale models of biology for design of therapeutics. He founded Manifold Bio after completing his PhD in the Harvard Biophysics program in the lab of George Church, where he developed new approaches for protein measurement and design by combining high-throughput biology, DNA sequencing, DNA synthesis, and machine learning technologies.
Sessions Featuring
Gleb
This Year
•
-
AIxBIO
Where Does Biology Compute? From Molecular Signals to Clinical Reality
As we move toward the “virtual cell” and ultimately the “virtual organism,” the AIxBIO ecosystem faces a fundamental challenge: where does biology actually compute? While our ability to measure molecular events has advanced dramatically, predicting how those signals translate into emergent, system-level outcomes remains a core bottleneck in programmable biology. This session brings together leaders across AI, synthetic biology, and medicine to explore the computational bottleneck, mapping where predictive power breaks down from molecules to cells to organisms. It will examine how to measure emergence at scale by generating causal, time-resolved, perturbation-rich datasets across diverse biological contexts, and how to close the reality gap with in vivo feedback, using next-generation sensors and real-world data to continuously calibrate and validate models in living systems.
•
-
AIxBIO
Where Does Biology Compute? From Molecular Signals to Clinical Reality
As we move toward the “virtual cell” and ultimately the “virtual organism,” the AIxBIO ecosystem faces a fundamental challenge: where does biology actually compute? While our ability to measure molecular events has advanced dramatically, predicting how those signals translate into emergent, system-level outcomes remains a core bottleneck in programmable biology. This session brings together leaders across AI, synthetic biology, and medicine to explore the computational bottleneck, mapping where predictive power breaks down from molecules to cells to organisms. It will examine how to measure emergence at scale by generating causal, time-resolved, perturbation-rich datasets across diverse biological contexts, and how to close the reality gap with in vivo feedback, using next-generation sensors and real-world data to continuously calibrate and validate models in living systems.
Session lineup still growing
Featuring
Speaker Coming Soon
•
-
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
