Michigan Postdoctoral Pioneer Program, 2021

Collaborative PIs


Santiago Schnell, DPhil (Oxon), FRSC

Interim Chair, Molecular & Integrative Physiology
John A. Jacquez Collegiate Professor of Physiology
Professor, Molecular & Integrative Physiology
Professor, Computational Medicine & Biology


Co - Mentor

Steven L Britton, Ph.D.

Steven L Britton
Professor of Anesthesiology
Professor, Molecular & Integrative Physiology




Modeling the energy transfer hypothesis in health and disease.

Our interest in disease-complexity-evolution connections originated from wanting to deliver a mechanistic explanation for the strong clinical linkage between low exercise capacity and complex disease risks. For this we developed the Energy Transfer Hypothesis (ETH) that states: Variation in capacity for energy transfer is the central mechanistic determinant of the divide between disease and health. As an unbiased test of the ETH we reasoned that: two-way artificial selection of rats based on low and high intrinsic treadmill running capacity would yield contrasting models of capacity for energy transfer that also divide for disease risks. As used clinically, we adopted maximal running capacity as a surrogate for energy transfer. Within highly complex conditions, with many possible outcomes, large scale selection proved to be predictive of the ETH. That is, disease risks segregated with selection for Low Capacity Runners and resistance to risks segregated with selection for High Capacity Runners. For explanation of the ETH we invoked operation of the principle of Maximal Entropy Production (MEP). According to MEP, non-equilibrium systems evolve to maximize its Entropy Production Rate. From the strong biological outcome of the rat model selection studies, and ideas from MEP, we have formulated a word model of the ETH that starts with the Big Bang. We propose translating this model into mathematical terms to identify and better predict the molecular and physical mechanisms responsible for the continuum from low to high disease risks.

The candidate should have experience in the mathematical modeling and numerical solutions of differential equations, inverse modeling with genetic algorithms and an understanding of biochemistry and physiology. This position would suit an individual who has a PhD in engineering, applied mathematics, or theoretical physics-chemistry.