John Doyle, California Institute of Technology, “Robust Efficiency in Healthy Heart Rate Control and Variability”
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“Robust Efficiency in Healthy Heart Rate Control and Variability”
John G Braun Professor of Control and Dynamical Systems, Electrical Engineer, and BioEngineering at Caltech. Has BS and MS in EE, MIT (1977), and a PhD, Math, UC Berkeley (1984). Current research interests are in theoretical foundations for complex networks in engineering and biology with focus on:
- hard limits on achievable robust performance ( “laws”) including measurement, prediction, communication, computation, decision, and control,
- the organizing principles that succeed or fail in achieving them (architectures and protocols),
- the resulting high variability data and “robust yet fragile” behavior observed in real systems and case studies (behavior, data), and
- the processes by which systems evolve (variation, selection, design).
Case studies are drawn from throughout technology plus cell biology, human physiology, ecology, neuroscience, and multiscale physics. Early work was in the mathematics of robust control, including extensions to nonlinear and networked systems. His group has been involved in software projects including the Robust Control Toolbox (muTools), SOSTOOLS, SBML (Systems Biology Markup Language), and FAST (Fast AQM, Scalable TCP). Prize papers include IEEE Baker, IEEE Automatic Control Transactions Axelby (twice), and best conference papers in ACM Sigcomm and AACC American Control Conference. Individual awards include AACC Eckman, and IEEE Control Systems Field and Centennial Outstanding Young Engineer Awards. He has held national and world records and championships in various sports. He is best known for having excellent co-authors, students, friends, and colleagues.
For more information on John Doyle, Click Here to view his homepage.
Abstract
“Robust Efficiency in Healthy Heart Rate Control and Variability”
My research focuses on developing a more “unified” theory for robust complex networks motivated by biology, medicine, and technology, and involving several elements: hard limits on achievable robust performance ( “laws”), the organizing principles that succeed or fail in achieving them (architectures and protocols), the resulting high variability data and “robust yet fragile” behavior observed in real systems and case studies (behavior, data), and the processes by which systems evolve (variation, selection, design). I’ve recently focused more attention in two directions: developing methods directly relevant to clinicians and making the theoretical insights more accessible to a broader audience (see PNAS 2011). In the medical direction, my group and I in collaboration with several clinician scientists have completed a study of how robust efficiency tradeoffs and actuator saturation explain healthy heart rate control and heart rate variability (HRV). This is intended to both address the nature and mechanisms underlying healthy HRV and introduce a methodology that should be relevant to understanding robustness in all physiological control systems.
Heart rate variability (HRV) is the area within medicine that is perhaps most comparable to glycolysis (see Science 2011) in having received massive theoretical and experimental investigations with essential mysteries unresolved. The correlation of healthy states with heart rate variability (HRV) using time series analyses is well documented. While these studies note the accepted proximal role of autonomic nervous system (ANS) balance in HRV patterns, the responsible deeper physiological (and clinically relevant) mechanisms have not been fully explained. Using mathematical tools from control theory, we have combined mechanistic models of basic physiology with experimental data from human subjects to explain causal relationships among states of stress vs. health, HR control, and HRV, and more importantly, the physiologic requirements and constraints underlying these relationships.
Note: Light lunch will be served starting at 11:30am.
