Santa Fe Institute Collaboration Platform

COMPLEX TIME: Adaptation, Aging, & Arrow of Time

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Contact: Caitlin Lorraine McShea, Program Manager, cmcshea@santafe.edu

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A list of all pages that have property "Pre-meeting notes" with value "Aging is associated with numerous changes at all levels of biological organization. Harnessing this information to develop measures that accurately and reliably quantify the biological aging process will require incorporation of functioning/failure at various levels that can be integrated using systems level approaches. This talk will provide illustrations on how DNA methylation data (DNAm) can be integrated with cellular, physiological, proteomic, and clinical data to model age-related changes that propagate up the levels—finally manifesting as age-related disease or death. We will also show how network modeling can be used to generate a ‘diseasome’ model in order to identify hub methylation signatures with implication for multiple pathways and outcomes. Given the complexity of the biological aging process, modeling of systems dynamics over time will both lead to the development of better biomarkers of aging, and also inform our conceptualization of how alterations at the molecular level propagate up levels of organization to eventually influence morbidity and mortality risk.    ". Since there have been only a few results, also nearby values are displayed.

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    • Hallmarks of Biological Failure/Systems-Level Modeling of Aging across Biological Levels of Organization  + (Aging is associated with numerous changes Aging is associated with numerous changes at all levels of biological organization. Harnessing this information to develop measures that accurately and reliably quantify the biological aging process will require incorporation of functioning/failure at various levels that can be integrated using systems level approaches. This talk will provide illustrations on how DNA methylation data (DNAm) can be integrated with cellular, physiological, proteomic, and clinical data to model age-related changes that propagate up the levels—finally manifesting as age-related disease or death. We will also show how network modeling can be used to generate a ‘diseasome’ model in order to identify hub methylation signatures with implication for multiple pathways and outcomes. Given the complexity of the biological aging process, modeling of systems dynamics over time will both lead to the development of better biomarkers of aging, and also inform our conceptualization of how alterations at the molecular level propagate up levels of organization to eventually influence morbidity and mortality risk.    nfluence morbidity and mortality risk.    )