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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.