Version 1
: Received: 12 October 2024 / Approved: 14 October 2024 / Online: 14 October 2024 (15:24:13 CEST)
How to cite:
Kern, C.; Stebbing, J. The Architecture of Aging: Decoding Evolution's Calculations. Preprints2024, 2024101043. https://doi.org/10.20944/preprints202410.1043.v1
Kern, C.; Stebbing, J. The Architecture of Aging: Decoding Evolution's Calculations. Preprints 2024, 2024101043. https://doi.org/10.20944/preprints202410.1043.v1
Kern, C.; Stebbing, J. The Architecture of Aging: Decoding Evolution's Calculations. Preprints2024, 2024101043. https://doi.org/10.20944/preprints202410.1043.v1
APA Style
Kern, C., & Stebbing, J. (2024). The Architecture of Aging: Decoding Evolution's Calculations. Preprints. https://doi.org/10.20944/preprints202410.1043.v1
Chicago/Turabian Style
Kern, C. and Justin Stebbing. 2024 "The Architecture of Aging: Decoding Evolution's Calculations" Preprints. https://doi.org/10.20944/preprints202410.1043.v1
Abstract
We lack a unified theory of aging that spans multiple scientific disciplines, including life history theory, evolution, cellular and molecular biology, and medicine. This gap has hindered both our ability to effectively target and treat age-related diseases in humans and our understanding of the vast variation in aging rates across species. This paper builds on the Blueprint Theory of Aging, which suggests that biological pathways can be ‘dis’-activated outside their intended context, leading to pathological pathways (patho-pathways) that drive cellular and tissue ‘dys’-function and age-related ‘dis’-eases, ultimately limiting both healthspan and lifespan. We propose that evolution operates through complex trade-offs within interconnected networks (network constraints), optimizing beneficial functions while minimizing the costs of misactivated patho-pathways. These optimizations are shaped by a species' environment and are driven by a central evolutionary dilemma: prioritize immediate reproduction or reduce current fecundity in favour of future reproductive success. Evolution adjusts network constraints along a continuum, balancing early-life reproduction with lifetime reproductive output. When environmental pressures result in lower juvenile survival than adult survival, the optimal strategy shifts toward maximizing total progeny over a lifetime. This strategy, in turn, determines the final trade-offs selected, the structure of network constraints, and ultimately, the rate of aging for that species. Understanding these evolutionary calculations not only explains species-specific aging patterns but also sheds light on the wide variability in aging rates across nature. Finally, we reveal hidden evolutionary connections between mechanisms such as Antagonistic Pleiotropy and Mutational Accumulation, deepening our insight into the evolutionary dynamics underlying aging.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
