Aging mechanism as the “down side” of adaptation: A network approach

Abstract

Many diverse hypotheses on aging are in play. All from "aging genes" over decreasing telomere length to increased level of gene mutations has been suggested to determine an organism's lifespan, but no one unifying theory exists. As part of a growing interest towards more integrative approaches in the field we propose a simplistic model based on the "useit-or-lose-it" concept: we hypothesize that biological aging is a systemic property and the down side of adaptation in complex biological networks at various levels of organization: from brain over the immune system to specialized tissues or organs. The simple dynamical model undergoes three phases during its lifetime: 1. General plasticity (childhood), 2. Optimization/adaptation to given conditions (youth and adolescence) and 3. Steady state associated with high rigidity (aging). Furthermore, our model mimics recent data on the dynamics of the immune system during aging and, although simplistic, thus captures some essential characteristics of the aging process. Finally, we discuss the abstract model in relation to current knowledge on aging and propose experimental set-ups for testing some of the theoretical predictions.

Biomarkers of Aging: From Function to Molecular Biology

“…Indeed, the understanding of aging has increased markedly, with current knowledge highlighting the importance of “network” theories of aging [ 7 ]. Based on these integrative theories, aging is best described as a multi-factorial process involving complex interactions between biological and molecular mechanisms [ 6 , 8 , 9 , 10 ]. Given this, it is unlikely that any single or readily defined set of biomarkers will provide a valid measure of biological aging.…”

Improving Healthy Aging by Monitoring Patients’ Lifestyle through a Wearable Device: Results of a Feasibility Study

“…However, only a few studies have explored the acceptance of the different types of smart devices in the older population [ 13 , 14 , 15 , 16 ], but the short duration of such studies (from one to a few weeks) has left the investigation of this novel technology limited to the first impression [ 17 ]. Short-term technology acceptance may not be correlated to long-term acceptance, because it seems that the use of wearables devices tends to drop after the first few weeks of ownership [ 18 ]. Furthermore, most existing studies [ 14 , 15 , 16 ] examined the acceptance level based on the technology acceptance model (TAM) [ 17 ].…”

Scaling Properties of an Evolving Network Model With Aging Mechanism

“…However, in many real-world evolving networks, aging is a universal effect. 10,11 For example, in citation network, very old papers are seldom cited (see Fig. 1); 12,13 in scientific collaboration network, scientists have a finite time span of being active; in movie actors network, popular stars are retiring from stage, and so on.…”