Multiscale dynamical network mechanisms underlying aging from birth to death

TL;DR

This study provides a comprehensive analysis of the continuous-time evolution of biological networks from birth to death, revealing how structural and functional networks diverge with age and how interventions can alter lifespan and aging processes.

Contribution

It introduces a novel in vivo measurement approach across all organizational scales, enabling detailed analysis of network aging and the effects of interventions.

Findings

Structural and functional networks diverge during aging.

Dying involves formation of disjoint functional sub-networks.

Interventions impact lifespan and aging depending on timing and method.

Abstract

How self-organized networks develop, mature and degenerate is a key question for sociotechnical, cyberphysical and biological systems with potential applications from tackling violent extremism through to neurological diseases. So far, it has proved impossible to measure the continuous-time evolution of any in vivo organism network from birth to death. Here we provide such a study which crosses all organizational and temporal scales, from individual components (10^1) through to the mesoscopic (10^3) and entire system scale (10^6). These continuous-time data reveal a lifespan driven by punctuated, real-time co-evolution of the structural and functional networks. Aging sees these structural and functional networks gradually diverge in terms of their small-worldness and eventually their connectivity. Dying emerges as an extended process associated with the formation of large but disjoint functional sub-networks together with an increasingly detached core. Our mathematical model quantifies the very different impacts that interventions will have on the overall lifetime, period of initial growth, peak of potency, and duration of old age, depending on when and how they are administered. In addition to their direct relevance to online extremism, our findings offer fresh insight into aging in any network system of comparable complexity for which extensive in vivo data is not yet available.