SynBioBeta Speaker
Laura Luebbert
FutureHouse
Postdoctoral Fellow
Laura is an Eric and Wendy Schmidt Fellow and FutureHouse AI-for-Science Postdoctoral Fellow working with Prof. Pardis Sabeti at the Broad Institute of MIT and Harvard. She specializes in machine learning methods for infectious disease discovery and triage. During her PhD at Caltech, Laura developed gget, a widely adopted open-source toolkit for genomic and proteomic analysis with over 300k downloads worldwide, and created algorithms to discover hidden viral sequences in RNA sequencing data. Her primary interests lie in understanding the role of viruses in human health and disease, as well as building machine learning models to assist high-stakes clinical triage in West Africa.
SynBioBeta 2026 Tickets are Live
Confirmed Speakers
Sessions Featuring
Laura
This Year
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AIxBIO
Biology in Silico: Multi-Agent Simulations of Life
From tissues morphing in development to microbes competing in a bioreactor, biology is inherently emergent. Multi-agent simulations — from platforms like BioDynaMo, CompuCell3D, and BIO-LGCA — are now powerful enough to model billions of interacting agents, capturing diffusion, metabolism, migration, and signaling with physical fidelity. Synthetic biologists are using these frameworks to probe design limits before moving to the lab, asking questions like: How far can diffusion alone carry a signaling molecule? What metabolic bottlenecks emerge in crowded cells? And how do engineered traits play out at population scale? This session will spotlight how agent-based models are becoming essential design environments for synthetic biology, helping teams test hypotheses virtually, anticipate failure modes, and translate biology into an engineering discipline rooted in predictive, quantitative simulation.
Purchase Pass
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AIxBIO
Biology in Silico: Multi-Agent Simulations of Life
From tissues morphing in development to microbes competing in a bioreactor, biology is inherently emergent. Multi-agent simulations — from platforms like BioDynaMo, CompuCell3D, and BIO-LGCA — are now powerful enough to model billions of interacting agents, capturing diffusion, metabolism, migration, and signaling with physical fidelity. Synthetic biologists are using these frameworks to probe design limits before moving to the lab, asking questions like: How far can diffusion alone carry a signaling molecule? What metabolic bottlenecks emerge in crowded cells? And how do engineered traits play out at population scale? This session will spotlight how agent-based models are becoming essential design environments for synthetic biology, helping teams test hypotheses virtually, anticipate failure modes, and translate biology into an engineering discipline rooted in predictive, quantitative simulation.
Purchase Pass
Session lineup still growing
Purchase Pass
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
