Exploring the evolution of aging using genetic simulations
Presentation by Martin Bagic, Jena
Time: 15:30–16:00
Aging is a complex phenotype that evolves over time giving rise to the rich diversity of aging trajectories and lifespans. Great efforts are being put into decoding aging as if it is a static, final phenotype; however, aging phenotypes are dynamic and despite their very different states, their evolution is driven by shared evolutionary forces.
Understanding evolution of aging elucidates some of the fundamental questions in the field of biology of aging such as “Why do certain species live significantly longer than others?” and “Why do some species display negligible senescence while others age rapidly?” By studying the evolution of aging, we gain insights that help interpret scientific findings related to exceptionally long-living species like the naked mole rat and bowhead whale, as well as surprisingly short-living species such as the turquoise killifish. Additionally, it refines our understanding of the limits to biological aging and lifespan.
Furthermore, populations of the same species often exhibit different aging trajectories. Exploring the evolution of aging provides us with explanations for these divergent patterns including the phenotypic diversity and frequency of aging-driving variants. These insights are also vital for constructing accurate evolutionary models capable of detecting evolutionary pressures and elucidating the phenotypic consequences of such variants.
While most bioinformatics methods focus on specific aging phenotypes and require specific data as input, genetic simulations offer a data-independent, species-agnostic method that unveils underlying principles of biology of aging. By employing these simulations, we can tackle broader and more fundamental questions in the field of biology of aging.