SynBioBeta Speaker
Benedetta Di Robilant
ProMIND Therapeutics
Co-founder & CEO
Dr. Benedetta di Robilant is a biotech entrepreneur and scientist with a PhD in Immunology and a Postdoctoral Fellowship at Stanford University, where she studied stem cells, aging, and age-related diseases including Down syndrome, Alzheimer’s disease, and fibrosis. She has experience translating academic discoveries into biotech ventures. Dr. di Robilant previously founded Dorian Therapeutics, a Stanford spinout acquired by Altos Labs and is now the co-founder and CEO of ProMIND Therapeutics, developing first-in-class therapies for neurodegenerative and neurodevelopmental disorders. Her work sits at the intersection of aging, neuroscience and translational medicine, with a focus on advancing fundamental discoveries toward meaningful patient impact.
SynBioBeta 2026 Tickets are Live
Confirmed Speakers
Sessions Featuring
Benedetta
This Year
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Longevity
Engineering Longevity: Reprogramming the Foundations of Aging
Aging is increasingly understood as a gradual loss of biological stability. DNA accumulates damage, protein homeostasis collapses, and cells drift away from youthful identities as regulatory networks lose their balance over time. These changes ripple across tissues and organs, driving many of the diseases associated with aging. Today, new tools in synthetic biology, artificial intelligence, and gene editing are revealing how these systems might be stabilized, repaired, or even reset. Researchers are engineering enhanced DNA repair mechanisms inspired by long-lived species, using AI to map the trajectories of cellular aging and uncover rejuvenating interventions, and developing therapies that restore protein metabolism to protect vulnerable tissues such as the brain. This session explores how scientists are moving beyond simply slowing aging to engineering the biological systems that maintain cellular integrity. By targeting the underlying mechanisms that govern genome stability, proteostasis, and cellular identity, researchers are laying the groundwork for a new generation of longevity therapeutics designed to restore function and resilience across the lifespan.
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Longevity
Engineering Longevity: Reprogramming the Foundations of Aging
Aging is increasingly understood as a gradual loss of biological stability. DNA accumulates damage, protein homeostasis collapses, and cells drift away from youthful identities as regulatory networks lose their balance over time. These changes ripple across tissues and organs, driving many of the diseases associated with aging. Today, new tools in synthetic biology, artificial intelligence, and gene editing are revealing how these systems might be stabilized, repaired, or even reset. Researchers are engineering enhanced DNA repair mechanisms inspired by long-lived species, using AI to map the trajectories of cellular aging and uncover rejuvenating interventions, and developing therapies that restore protein metabolism to protect vulnerable tissues such as the brain. This session explores how scientists are moving beyond simply slowing aging to engineering the biological systems that maintain cellular integrity. By targeting the underlying mechanisms that govern genome stability, proteostasis, and cellular identity, researchers are laying the groundwork for a new generation of longevity therapeutics designed to restore function and resilience across the lifespan.
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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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