Leopold van den Daele
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on the atlas · 33
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highlights · 21
This will both provide a better un- derstanding of the possible trajectories of decline, i.e., to what extent individuals age differently, and enable nuanced assess- ment of potential interventions, providing a more rapid and infor- mation-rich readout than standard metrics such as lifespan.
Untangling Aging Using Dynamic, Organism-Level Phenotypic NetworksFortunately, reducing a network of phenotypes to a single biological age is unnecessary given modern analytics; fully utilizing this type of network model re- quires departing from reductionism, embracing complexity and the idea of emergent phenomena, and navigating high-dimen- sional analysis ( Cohen, 2016 ), but the reward is a far more accu- rate picture of reality
Untangling Aging Using Dynamic, Organism-Level Phenotypic NetworksFurther, because organism-level phenotypes are gener- ated by the combined state of the molecules and cells that make up the organism, a relatively small number of such phenotypes may describe overall system state as or more accurately than a very large number of cell and molecular phenotypes
Untangling Aging Using Dynamic, Organism-Level Phenotypic NetworksPartial correlation is the re- maining correlation after accounting for and removing the effect of all other measured phenotypes.
Untangling Aging Using Dynamic, Organism-Level Phenotypic Networkseach node of the network represents a measured pheno- type, and edges describe relationships between phenotypes
Untangling Aging Using Dynamic, Organism-Level Phenotypic Networksaging, then, can be modeled as the change in the network over time
Untangling Aging Using Dynamic, Organism-Level Phenotypic NetworksThe more deleterious phenotypes ameliorated and the more people whose quality or duration of life is improved, the more valuable a therapy becomes, regardless of whether it is branded a dis- ease therapy or an aging therapy.
Untangling Aging Using Dynamic, Organism-Level Phenotypic Networks‘‘Aging’’ and ‘‘disease’’ represent cultural and clinical judgments about normality applied to non-discrete realities ( Gladyshev and Gladyshev, 2016 ).
Untangling Aging Using Dynamic, Organism-Level Phenotypic NetworksRather, the mechanisms that limit healthspan and lifespan simply differ from environment to envi- ronment, and the choice of environment, like the choice of ge- netic background, should be driven by relevance.
Untangling Aging Using Dynamic, Organism-Level Phenotypic NetworksThe idea that aging and disease are fundamentally distinct relies on an arbitrary ‘‘normal’’ reference point in exactly the same manner as attempts to distinguish populations that exhibit ‘‘true aging’’ from those that do not
Untangling Aging Using Dynamic, Organism-Level Phenotypic Networksif true, it could be argued that studying the biology of aging is frankly a waste of time: if a set of phenotypes has nothing in common, then studying them as a set would yield no greater (and likely less) insight than studying each individually
Untangling Aging Using Dynamic, Organism-Level Phenotypic NetworksThis arises from the fallacious notion that chronological age rep- resents a singular biological process rather than serving as a sta- tistical proxy for a multitude of biological changes.
Untangling Aging Using Dynamic, Organism-Level Phenotypic Networksby measuring and modeling a large number of age-dependent phe- notypes in each population of interest, this process can be quan- titative (e.g., network alignment) instead of qualitative (e.g., ‘‘an increase in mouse lifespan is probably equivalent to an increase in human lifespan’’) and may improve the clinical translation of aging therapies
Untangling Aging Using Dynamic, Organism-Level Phenotypic Networksthereisthepossibilitythatthemechanismsofaging are so specific to species and the environment that virtually no important mechanisms are shared between multiple populations, andmodel organismswill, atbest,bea poorabstraction ofhuman aging
Untangling Aging Using Dynamic, Organism-Level Phenotypic Networksis an erroneous extension of the observation that, with rare exception ( Finch, 1998, 2009 ), all biological entities deteriorate with time
Untangling Aging Using Dynamic, Organism-Level Phenotypic Networksthe phenotypes we term aging are based on a reference point. It follows then that there is no formal requirement that the mechanisms of aging be shared between populations—as the phenotypes of aging vary by population, the mechanisms driving those phenotypes might vary by population as well
Untangling Aging Using Dynamic, Organism-Level Phenotypic Networksmeasuring the multi-dimensional set of all possible age-dependent pheno- types, which can change between populations and environ- ments.
Untangling Aging Using Dynamic, Organism-Level Phenotypic Networksdefine and measure ‘‘true aging.’’ There is no such thing.
Untangling Aging Using Dynamic, Organism-Level Phenotypic NetworksThe concept that aging is worth studying is essentially a hypothesis that these conditions share causal mechanisms and that working to identify and com- bat those shared mechanisms is a viable strategy to improve the quality and duration of life.
Untangling Aging Using Dynamic, Organism-Level Phenotypic NetworksGiving even a tiny amount of money to charity is hundreds or even thousands of times more effective than almost any political action you can take.
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