You do not have permission to edit this page, for the following reason:
The action you have requested is limited to users in the group: Users.
To protect the wiki against automated edit spam, we kindly ask you to solve the following CAPTCHA:
1+ paragraphs on any combination of the following:
The hypothesis/framework presented that risk of failure increases with linearity and tightness of coupling in the functioning of a system is interesting and stimulating, but I wondered about how to quantify these two axes in a practical manner in order to test this idea. How easy is it to classify biological systems on these axes? How much of this classification is artificial? I was also puzzled about how to separate these processes (brain functioning, immune response, and others) given their interactions and integration; but if all these different systems are integrated, then does it make sense to attribute risk of failure to properties of each system? Can this approach be adapted to take into account this integration?
How to define complexity in this context?
The fact that some aspects of senescence may be due to overdefense in later age, seems in line with other observations and theories that senescence may be the result of protective systems dysfunctioning with age; I wondered about the role of our interaction with infectious diseases in shaping our biology and therefore our life history through these mechanisms.
I would like to learn more about how mental disorders match "attractors" potentiated by tight coupling; should read more about this.
A very nice study system in killifish populations and species where climate/aridity/habitat (temporal ponds) correlates with divergence in life history. Lots of different approaches (QTL, genomics, comparative analysis, phenotypic studies of aging, mutation rates estimates): overall suggest that populations/species living in drier/more ephemeral habitats have accumulated a high genetic load affecting aging and lifespan through relaxed selection, which may be due in part to their demography with frequent bottlenecks. Nice to see such a complete study showcasing how mutation accumulation affects aging evolution. Reminds me of the work by the group of Christoph Haag on daphnia (also in ephemeral ponds)); see reference attached.
claims for a change in paradigm in our way of understanding and modelling cancer: number of mutatations would not be so relevant, because of selection on malignant cells. Model of fitness landscape with different peaks, changing with age. What are the mechanisms that make malignant cells unfit in young individuals (niche? policing by other cells? Can we model the interactions between cells to generate such fitness landscapes and their change with age?
What does explain that the increase in rate of cancer on a log scale is linear with time, with the same slope: can we predict that? what does it require in terms of assumptions?
Effect of caloric restriction on lifespan in 40 years long term experiments in monkeys; growth and immune function intertwined. What is the effect of caloric restriction with infectious disease? More generally would a better integration of infectious disease-driven evoluton and life history/aging evolution be necessary?
Hidden Markov models driving transition rates could be fitted using longitudinal data on health, frailty and morbidity: could we then estimate how fitness or potential wells change with age?
Fainting in young individuals broadly distributed taxonomically; could be an adaptation to escape predators in inidviduals that have no other options; links with capture myopathy
What are the life history correlates associated with capture myopathy vulnerability; points to insights in considering relationships with other species (here predation) in shaping evolutionarily features associated with various conditions
aging dynamics at different levels of organization or different aspects of aging (biological, phenotypic, functional) may be different; what are the articulations between these different levels and aspects of aging?
Idea that markers of biological age may be improve our predictive power of condition beyond predictions based on chronoogical age; changes in methylation with age; predictor of chronological age based on methylation age (R=0.98 idea that residuals may inform on condition, but not much residuals variation to play with? not much extra information? methylation not as predictive as biological markers; methylation increases in all tissues but not at the same rate with age: why and how is it related to function?
Schneider & Sheffer: in both presentations, slow recovery signals approaching a critical tipping point; can we get an idea about how close we are from the tipping point? Martin says that there are no obvious way. With systems that have no fixed points as equilibrium but naturally cycle, it is also harder to find signals of a critical transition
general discussion: questions that I like= 1) how to connect ideas about tipping-points/resilience/netowrks to classic evolutionary models of aging, 2) can we design simple models predicting exponential increase in rate of mortality with age? with constant slopes? 3) can fitness landscapes/potentail wells be more than useful images to help us think about aging? Can we parameterize these models? Can they be used to make more quantitative predictions?
other ideas: can we build models with both antagonistic pleiotropy and mutation accumulation and study their interactions? are there distinct predictions about collapses of networks based on these different source of genetic variation affecting aging?
role of social interactions at different levels in biological failure (cell to individual), role of interactions with other species (parasites, predatiors, mutualists)
1) Tenaillon (2014) reviews the insights brought upon by Fisher Geometric model in evolutionary genetics: could it be useful as well for our understanding of aging?
2) Martin (2014) shows how and when Fisher geometric model of adaptation emerges from complex networks of interacting modules
3) Promislow and Moorad (2088) did use that framework to model aging; could it be used to address different questions about aging, dfferent attractors, resilience?
4) tipping points on one side and evolutonary theories of aging on the other have been discussed as distinct frameworks, which should be better connected; how tipping points may be affected by evolution was discussed in a recent review (interested in ecological tipping points mostly); it could be a good starting point to read Dakos et al. (2019)
5) another example of mutation accumulation affecting aging in daphnia in Lohr et al. (2014)
6) still another one on fitness landscapes but combined with data on antibiotic resistance evolution to address questions about how these fitness landscapes change with the environment/stress: check Harmand et al. (2017)