Tufts in Translation: Predicting Glucose Responses: Building a Virtual Platform for Precision Health

Nov 12, 2026

LabCentral

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Add to Calendar + 2026-11-12 11:30 2026-11-12 13:30 America/New_York Tufts in Translation: Predicting Glucose Responses: Building a Virtual Platform for Precision Health

Welcome back to Tufts in Translation where we spotlight faculty advancing new therapeutic approaches, targets, platforms, and delivery systems. Sessions feature early-stage research across the drug discovery and design landscape, with an emphasis on translational potential and industry relevance. This session will feature Dr. Larissa Calancie is the Linda and Jeffrey Moslow Assistant Professor of Women’s Health and Menopause at the Friedman School of Nutrition Science and Policy, and is developing a predictive glucose simulation platform that uses physiologically based modeling to forecast how food intake, beverages, and physical activity influence blood glucose levels. A key focus of Dr. Calancie’s work is adapting the platform to model glucose dynamics during pregnancy, addressing the unique metabolic changes associated with maternal health and gestational diabetes, an area that remains underserved by existing digital health and decision-support tools. 

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Welcome back to Tufts in Translation where we spotlight faculty advancing new therapeutic approaches, targets, platforms, and delivery systems. Sessions feature early-stage research across the drug discovery and design landscape, with an emphasis on translational potential and industry relevance. This session will feature Dr. Larissa Calancie is the Linda and Jeffrey Moslow Assistant Professor of Women’s Health and Menopause at the Friedman School of Nutrition Science and Policy, and is developing a predictive glucose simulation platform that uses physiologically based modeling to forecast how food intake, beverages, and physical activity influence blood glucose levels. A key focus of Dr. Calancie’s work is adapting the platform to model glucose dynamics during pregnancy, addressing the unique metabolic changes associated with maternal health and gestational diabetes, an area that remains underserved by existing digital health and decision-support tools. 

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