Investigating Immune System Aging: System Dynamics and Agent-Based Modeling

TL;DR

This paper compares system dynamics and agent-based models to understand immune system aging, focusing on T cell output and diversity to evaluate potential therapies and improve health span.

Contribution

It assesses the suitability of different simulation methods for modeling immune aging, specifically analyzing T cell dynamics and diversity.

Findings

Agent-based models capture individual T cell behaviors.

System dynamics provide a broad overview of immune decline.

Model comparisons inform therapeutic strategies.

Abstract

System dynamics and agent based simulation models can both be used to model and understand interactions of entities within a population. Our modeling work presented here is concerned with understanding the suitability of the different types of simulation for the immune system aging problems and comparing their results. We are trying to answer questions such as: How fit is the immune system given a certain age? Would an immune boost be of therapeutic value, e.g. to improve the effectiveness of a simultaneous vaccination? Understanding the processes of immune system aging and degradation may also help in development of therapies that reverse some of the damages caused thus improving life expectancy. Therefore as a first step our research focuses on T cells; major contributors to immune system functionality. One of the main factors influencing immune system aging is the output rate of naive T cells. Of further interest is the number and phenotypical variety of these cells in an individual, which will be the case study focused on in this paper.