Quantitative Exercise Biology

David Clarke

Abstract

The Clarke Lab works on developing data-driven predictive models of the physiological responses to exercise. Projects include systematic review and meta-analysis, models of skeletal muscle cell plasticity, and analytics for longitudinal time series data from individual and team sports. The work involves collaboration between kinesiologists, physiologists, statisticians, bioinformaticians, and engineers. Our work has three major themes: 1. Systematic review and meta-analysis of exercise intervention studies. In this project, we seek to develop empirical models of the time-dependent dose-response relationship between training load and outcomes with respect to health, fitness, or performance. 2. Models of skeletal muscle cell plasticity. We are interested in how the whole-body stress of exercise is sensed and integrated at the cellular level in order to provoke fitness-related cellular adaptations (e.g., mitochondrial biogenesis). Several lines of work are involved within this project, including the development of ordinary-differential-equations-based biochemical kinetics models of skeletal muscle cell signaling, broader network-level logic models of signaling, bioinformatic approaches for high-throughput data, and computational optimization of experimental designs. 3. Longitudinal time series data analytics. Global positioning system (GPS) and powermeter data represent new frontiers for our ability to quantify training load and predict physiology and performance. We are involved in projects examining the power-duration relationship in cycling, impulse-response modeling of training and performance in endurance athletes, and team sport analytics. Each project involves mathematical and computational models, which inherently demands an interdisciplinary approach. Our group is therefore composed of kinesiologists, physiologists, statisticians, bioinformaticians, and engineers.

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