Join Jonathan Brestoff, MD, PhD, MPH, for an exploration of how energy expenditure can be better described thanks to a new bioinformatic algorithm known as Clambake.
Metabolic cages are powerful tools to understand energy homeostasis and are frequently used in biomedical research on metabolic diseases such as obesity, exercise, cold exposure, and other physiologic stressors. Although metabolic cage systems allow for estimation of energy expenditure (EE), whole body EE is a composite of basal metabolic rate, behavioral factors such as activity levels, food intake, and adaptive thermogenic mechanisms such as Uncoupling protein 1 (UCP1)-induced heat production. In this presentation, Jonathan Brestoff describes a new bioinformatic algorithm called Clambake that allows for deconvolution of how much energy expenditure comes from basal metabolic rate, the thermic effect of food, physical activity, and adaptive thermogenesis. He discusses the theory behind this algorithm, his initial validation efforts, and its capacity to predict defects in beige/brown fat function in mice that appear to have normal EE. Clambake may allow for a deeper understanding of metabolic cage data and enable identification of hidden phenotypes that might otherwise be overlooked.
Key Topics Include:
- Identify the main factors that contribute to whole-body energy expenditure
- Learn about a new machine learning algorithm called Clambake that allows for deconvolution of these factors using metabolic cage data
- Understand how to use Clambake results to characterize energy homeostasis and predict hidden defects in adaptive thermogenesis
Department of Pathology and Immunology
Washington University School of Medicine