A Mechanistic Model of the Human Menstrual Cycle

2026-10-02

AI Summary

This study addresses the challenge that existing endocrine dynamics models struggle to simultaneously achieve physiological accuracy and computational efficiency. We propose a mechanistic model that balances biological realism with mathematical parsimony, employing a system of ordinary differential equations to integrate core mechanisms such as feedback regulation and follicular development for accurately simulating the dynamic interactions of four key hormones. Achieving high fidelity with low complexity, the model faithfully reproduces experimentally observed hormonal trajectories, thereby validating the effectiveness of targeted mechanistic modeling. Furthermore, its architecture supports modular extensions, providing a robust computational framework for personalized medicine applications.

Abstract

The human menstrual cycle is regulated by complex hormonal feedback mechanisms which are essential for reproductive health. Existing mathematical models either rely on simplified phenomenological assumptions or achieve physiological accuracy at the cost of high mathematical complexity and computational costs. This paper presents a novel mechanistic model which balances biological realism with mathematical simplicity. The proposed framework describes the dynamics of the four central cycle-driving hormones, namely follicle stimulating hormone, luteinizing hormone, estradiol, and progesterone, using a system of ordinary differential equations. Mechanistic detail is incorporated through representations of feedback mechanisms, follicular development, luteinization, vascularization of the corpus luteum, and aspects of hormone synthesis mechanisms. Despite its comparatively low complexity, the model reproduces characteristic trends of hormone activity and concentration dynamics reported in experimental data. Due to its computational efficiency and modularity, the model is well suited as a basis for extended in-silico experiments, including applications in pharmacology and personalized medicine. Our results demonstrate that targeted incorporation of biological mechanisms enables accurate yet tractable modeling of endocrine system dynamics.

Problem

• menstrual cycle
• mathematical modeling
• hormonal dynamics
• mechanistic model
• computational efficiency

Innovation

• mechanistic model
• menstrual cycle
• ordinary differential equations
• hormonal dynamics
• computational efficiency