SynBioBeta Speaker
Fay Lin
GEN
Sr Editor, Technology
Fay is a computational biologist turned science journalist. As Senior Editor, Technology at Genetic Engineering & Biotechnology News (GEN), Fay covers the intersection of artificial intelligence (AI) and biology with a particular focus on drug discovery and healthcare. Prior to joining GEN, she was a founding Senior Editor of GEN Biotechnology, GEN‘s sister peer review journal launched in 2022 publishing outstanding research and review articles across the biotech field. Fay’s work has been published across multiple media outlets for science communication and outreach, including Chemical & Engineering News (C&EN), BioTechniques, and Inside Higher Ed. She earned her PhD in Biochemistry from University of California, Los Angeles (UCLA), where she developed mathematical models to decipher cell signaling in immune response.
SynBioBeta 2026 Tickets are Live
Confirmed Speakers
Sessions Featuring
Fay
This Year
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AIxBIO
The Programmable Protein Era: How AI Rewrites the Rules of Biomolecules
Biologics and engineered proteins have traditionally evolved through cycles of intuition, screening, and incremental optimization. Today, AI is transforming proteins into programmable systems; governed by learnable patterns across activity, stability, expression, specificity, manufacturability, and environmental performance. This shift is unlocking a new generation of biomolecules, from next-generation therapeutics to sustainable enzymes and functional biological systems, that would have been impossible to design by hand. In this session, leaders from biopharma, industrial biotech, machine learning, and protein engineering will explore how multiparameter optimization, generative modeling, and closed-loop experimental validation are reshaping biomolecular design across domains. From clinical biologics to planetary-scale applications, we examine the shift from trial-and-error to predictive, constraint-driven design, and what it means for R&D timelines, scalability, and real-world impact.
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AIxBIO
The Programmable Protein Era: How AI Rewrites the Rules of Biomolecules
Biologics and engineered proteins have traditionally evolved through cycles of intuition, screening, and incremental optimization. Today, AI is transforming proteins into programmable systems; governed by learnable patterns across activity, stability, expression, specificity, manufacturability, and environmental performance. This shift is unlocking a new generation of biomolecules, from next-generation therapeutics to sustainable enzymes and functional biological systems, that would have been impossible to design by hand. In this session, leaders from biopharma, industrial biotech, machine learning, and protein engineering will explore how multiparameter optimization, generative modeling, and closed-loop experimental validation are reshaping biomolecular design across domains. From clinical biologics to planetary-scale applications, we examine the shift from trial-and-error to predictive, constraint-driven design, and what it means for R&D timelines, scalability, and real-world impact.
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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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Speaker Coming Soon
