SynBioBeta Speaker
Elise de Reus
Cradle
Co-founder
As co-founder of Cradle, Elise is motivated by the belief that industrial biotechnology holds enormous potential to produce essential products at lower environmental impact. Elise previously worked at Zymergen to develop high-throughput workflows for rapidly generating improved microbial strains, and contributed to strain engineering for microbial production of dairy protein at Perfect Day Foods. She holds an MSc in Life Science & Technology from TU Delft and grew quite fond of fungi during a PhD in fungal synthetic biology at the Technical University of Denmark.
SynBioBeta 2026 Tickets are Live
Confirmed Speakers
Sessions Featuring
Elise
This Year
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AIxBIO
The New Computational Biology Stack: Models, Compute, and Experimental Feedback
AI is transforming biology into a fully integrated computational discipline, where discovery depends on the seamless interaction between models, compute infrastructure, and experimental systems. As foundation models for proteins, genomes, and cellular systems mature, the challenge is no longer prediction alone. It is building a unified stack that connects generative design, large-scale computation, and rapid experimental feedback into continuous learning loops. This session explores how the next generation of computational biology platforms is emerging at the intersection of cloud computing, GPU-accelerated modeling, advanced simulation, and high-throughput experimental infrastructure. Leaders across AI, biotech, and technology will discuss how tightly integrated design-build-test-learn cycles are reshaping therapeutic discovery, enabling adaptive model refinement, and accelerating the transition from in silico hypotheses to real-world biological outcomes.
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AIxBIO
The New Computational Biology Stack: Models, Compute, and Experimental Feedback
AI is transforming biology into a fully integrated computational discipline, where discovery depends on the seamless interaction between models, compute infrastructure, and experimental systems. As foundation models for proteins, genomes, and cellular systems mature, the challenge is no longer prediction alone. It is building a unified stack that connects generative design, large-scale computation, and rapid experimental feedback into continuous learning loops. This session explores how the next generation of computational biology platforms is emerging at the intersection of cloud computing, GPU-accelerated modeling, advanced simulation, and high-throughput experimental infrastructure. Leaders across AI, biotech, and technology will discuss how tightly integrated design-build-test-learn cycles are reshaping therapeutic discovery, enabling adaptive model refinement, and accelerating the transition from in silico hypotheses to real-world biological outcomes.
Get a Ticket
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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.
Get a Ticket
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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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Featuring
Speaker Coming Soon
