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1+ paragraphs on any combination of the following:
* Considerations of time-scales. Where/how is time encoded?
* With time, new structures emerge, network connectivity changes
* Can hyperactivity that optimizes growth and development contribute to aging (antagonistic pleiotropy)
* Peto’s paradox—what things besides the number of cells, which should influence probably of neoplastic transformation, confer differences in species-level cancer risk?
* Only the intercept, not the slope for the age-specific rates of lung cancer differ between smokers and never smokers.
* May not be intrinsic damage accumulation and instead may be the difference of the “aged” vs. young environment which differentially selects for oncogenesis
* Clonal hematopoiesis could be facilitated/accelerated by the old versus young environment?
* How does this fit in to the findings from heterochronic parabiosis? Would changing the environment of the niche from old to you or vice versa alter 1) clonal hematopoiesis, or 2) probably of neoplastic transformation? What is the impact of HRAS in young versus old environment?
* Aging may be both the small perturbations to the system and the reaction to perturbations in neighboring systems for which one depends on. With a healthy neighbor idea, a small perturbation may not matter if the interacting system is functioning well, but ass the systems all go down hill together, there will be feedback that will cause an acceleration in the rate of multi-system declines. This may account for why things that change linearly with age, produce exponential morbidity/mortality cures at the level of the population.
* CR is changes a lot of pathways that together produce both lifespan and health span extension.
* One can start to map the CR response to systems-level (or network) changes involved in a variety of pathways.
* Interesting overlap with the genetic architecture of insulin resistance
* The same pathways that come up in CR are also the ones we are finding relate to the epigenetic clocks in humans. Also evidence that CR decreases mouse epigenetic age.
* Wells that define healthy vs. sick and transition out of a well depends on depth, curvature, and the size of the fluctuations.
* A hidden Markov is a good way to model this because the transition to a given well/state depends upon the current state—doesn’t matter how you got there just the parameters above (I would also add that there are likely many well and proximity to a well influences probability of transition).
* I would ask whether the overall landscape is changing, or whether the available landscape (what is in your vantage point given where you are) is the only thing that changes. If we think of the wells as discrete states, they shouldn’t actually change, just your probability of sampling a given space given your current space will change.
* Can you also model entropy using these wells? Basically you will have a tendency to move lower in a landscape towards equilibrium and moving uphill requires energy input.
* Does it make sense to consider single-cell data and investigate the probability distributions of having a multi-dimensional profile as a function of age (could use something like Mahalanobis distance).