WEBINAR—Neural Network-inspired Models for Understanding Metabolism-behavior Interactions

Using indirect calorimetry and time-series data—including energy expenditure, blood glucose, food intake and locomotion—as a modeling approach captures dynamic temporal relationships among metabolic variables over time, offering a more precise view of metabolic phenotypes. Learn about a novel time-lagged linear mixed model pipeline that moves beyond traditional metrics toward precise, temporarily resolved characterization of metabolic phenotypes. 

What You’ll Learn 

• The limitations of defining physiological states as binary categories and the need for quantitative approaches that capture change over time. 
• How indirect calorimetry (metabolic cages) provides continuous physiological and behavioral data that can be used to examine metabolic changes over time. 
• The principles of the time-lagged linear mixed model pipeline.

Who Should Attend 

• Metabolic researchers.
• Endocrinology researchers.
• Researchers interested in the neural control of metabolism.

Speaker

Li Ye, PhD
Professor and Howard Hughes Medical Institute Investigator, Scripps Research 

Li Ye, PhD, is the N. Paul Whittier Endowed Professor and a Howard Hughes Medical Institute investigator in the Department of Neuroscience at Scripps Research. He received his BS from Tsinghua University in China and his doctoral degree from Harvard University. Ye’s lab integrates neuroscience, molecular metabolism and chemical biology to study the nervous system and peripheral tissues as a unified network. 

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