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COMPLEX TIME: Adaptation, Aging, & Arrow of Time

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Dynamic Multi-System Resilience in Human Aging/Conceptual models of human aging and resilience

From Complex Time

November 12, 2018
11:00 am - 11:40 am


Peter M. Hoffmann (Wayne State Univ.)


Humans are “complex biological systems consisting of multiple levels of non-linearly interacting elements”. Our bodies have astonishing powers of self-repair, and in the absence of catastrophic stress or genetic defects, can maintain homeostasis for a long time. Humans are one of the most long-lived species on the planet. However, there seems to be a “natural limit” of human life span that has not changed substantially despite the advances of medicine. Self-repair and recovery from stresses “naturally” diminish with age. Eventually, tipping points push the system into increasingly less resilient states. Are there any purely conceptual models that can describe human aging, resilience and frailty, especially the slow-down of recovery and the emergence of tipping points? This talk will provide a high-level overview of some potentially useful models and how they relate to each other – focusing on models of entropic/informational breakdown and stochastic stress response. However, while such models can capture aspects of the problem, challenges remain to connect these mathematical models to the complex, multi-hierarchical, multi-timescale, feedback-controlled system of a human body.

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Post-meeting Reflection

Peter M. Hoffmann (Wayne State Univ.) Link to the source page

I was concerned that “high-level” models would not be useful in this context, but that turned out not to be the case. It was fascinating to see how actual physiological data can potentially be analyzed and understood in the context of metastable states, complexity, networks, critical states, 1/f noise etc. A full understanding how these things connect and how they relate to real data is still in its infancy, which should make this an exciting area to think about.

The goal will be to "marry" conceptual models and data collected from real systems. How can conceptual models capture stress and perturbation responses, time scales, tipping points & attractors, feedback loops, variability, noise and complexity seen in real systems? Conceptual models should be helpful if we want to learn what measured signals (transient, noise etc.) can tell us about the structure and dynamic state of the underlying system.

Reference Material

Title Author name Source name Year Citation count From Scopus. Refreshed every 5 days. Page views Related file
Reliability Theory of Aging and Longevity Leonid A. Gavrilov, Natalia S. Gavrilova Handbook of the Biology of Aging 2005 63 0
Reliability Theory of Aging and Longevity4 Leonid A. Gavrilov, Natalia S. Gavrilova J. Theor. Biol. 2001 63 15