SynBioBeta Speaker
Tommaso Biancalani
Lila Sciences
SVP
Tommaso Biancalani is Senior Vice President of Generative Biology at Lila Sciences, where he focuses on building frontier AI models to streamline biological discovery. Prior to LILA, Tommaso led AI for biology at Genentech, where his department spearheaded the development and application of machine learning to various stages of the drug discovery pipeline, including target identification, hit finding, and lead optimization. Earlier in his career, Tommaso was a group leader at the Broad Institute, focusing on deep learning for genomics. Tommaso holds a PhD from the University of Manchester and completed postdoctoral fellowships at MIT and the Carl R. Woese Institute for Genomic Biology.
SynBioBeta 2026 Tickets are Live
Confirmed Speakers
Sessions Featuring
Tommaso
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.
Get a Ticket
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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.
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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
