(136) Mathematical Modeling of Vertebrate Memory-Based Immunity and Autoimmunity: A Computational Study of Memory Dynamics and Innate-Adaptive Feedback

Introduction/Rationale: We present a mathematical and computational study of the vertebrate adaptive immune system, focusing on T cell-mediated responses and their interaction with innate immunity. Our framework employs nonlinear ordinary differential equations to model the dynamics of both innate and acquired immune responses to pathogens and cancer.

Methods: Our model treats immune memory as a dynamic variable separate from circulating B and T cell concentrations, and includes autoimmunity as a factor influencing host fitness. Using computational simulations and stability analysis, we investigate different scenarios that could either support immune homeostasis or cause tolerance breakdown and the development of autoimmunity.

Results: The results demonstrate how nonlinear feedback between innate and adaptive immunity can influence the dynamics of pathogen, cancer cells, and healthy host cells. This model thus reveals how selection will act on different parts of the vertebrate immune system under challenges from both pathogens and cancer.

Conclusion: This modeling framework reveals how regulatory feedbacks shape the stability and adaptability of vertebrate immunity. It identifies conditions that promote immune homeostasis or trigger autoimmune dynamics, offering a quantitative basis for understanding how disruptions in immune regulation contribute to disease progression.