A network model for cellular aging

TL;DR

This paper introduces a mathematical gene interaction network model that explains cellular aging as an emergent property, showing how stochastic gene interactions lead to the exponential increase in mortality rate characteristic of aging.

Contribution

The study presents a novel network-based model of cellular aging where aging arises from stochastic gene interactions, providing mechanistic insights into the process.

Findings

Exponential mortality increase emerges during early aging in the model.

Aging rate is proportional to the average number of active gene interactions.

Stochastic heterogeneity in gene interactions influences aging dynamics.

Abstract

What is aging? Mechanistic answers to this question remain elusive despite decades of research. Here, we propose a mathematical model of cellular aging based on a model gene interaction network. Our network model is made of only non-aging components - the biological functions of gene interactions decrease with a constant mortality rate. Death of a cell occurs in the model when an essential gene loses all of its interactions to other genes, equivalent to the deletion of an essential gene. Gene interactions are stochastic based on a binomial distribution. We show that the defining characteristic of biological aging, the exponential increase of mortality rate over time, can arise from this gene network model during the early stage of aging. Hence, we demonstrate that cellular aging is an emergent property of this model network. Our model predicts that the rate of aging, defined by the Gompertz coefficient, is approximately proportional to the average number of active interactions per gene and that the stochastic heterogeneity of gene interactions is an important factor in the dynamics of the aging process. This theoretic framework offers a mechanistic foundation for the pleiotropic nature of aging and can provide insights on cellular aging.