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A number of critical questions were raised about the best levels at which to establish causality when it comes to understanding both natural and disease-related aging. Namely what are the best observables to consider? Should these be single measurements or network based measurements. Could the best indicators involve comparisons across genetic and cognitive networks applying similar methods, or as is more typical time-dependent changes in a given network at one level of analysis. A recurring question was the relationship between energy and information and how their reciprocal dependencies change over the course of time and the course of disease.
Some very general issues that arose in conversation that require further exploration include:
#Approaching disease from a first-principles theoretical perspective - as is common in ecology - thus establishing principled data collection objectives (this would require a rigorous operational definition of the disease state in formal terms)
#The value and limitation of the current inductive, big data approach, that focuses on time-dependent associations
#The meaning of cognitive reserve, exercise or error correction, and the limits to these
#How adaptive phenomena that are ongoing mitigate the disease state or at some point perhaps accelerate it.
#How we might better explore causality in large systems with extensive non-linear feedback mechanisms.