Modeling of the Hypothalamic-Pituitary-Adrenal Axis dynamics by stoichiometric networks

Abstract

The hypothalamic-pituitary-adrenal (HPA) axis is a neuroendocrine system that regulates the body’s response to stress and maintains homeostasis through the secretion of cortisol, its primary hormone. Dysregulation of the HPA axis is implicated in numerous stress-related disorders, including obesity, depression, chronic pain, metabolic disorders, etc. Therefore, understanding the HPA axis is vital for comprehending stress-related diseases and developing effective interventions. Investigating the dynamic nature of HPA axis activity presents significant challenge, which can be effectively addressed through mathematical modelling. Modelling can provide deep insights into the system’s responses to stress, regulatory mechanisms involving ultradian and circadian rhythms, feedback loops, and hormonal interactions. Furthermore, modelling the HPA axis facilitates understanding how various factors influence its functioning, offering a powerful tool for studying related disorders and developing targeted interventions. Hence, this paper presents a detailed mathematical modelling approach utilizing stoichiometric networks to describe the dynamics within the HPA axis. The model captures the interplay of response strategies in the HPA axis, providing a framework for simulating its behaviour under different conditions. This model has potential for studying stress modulation, improving stress management strategies, and addressing health outcomes related to HPA axis dysregulation.