Human menstrual cycle hormone dynamics captured with efficient model
A Mechanistic Model of the Human Menstrual Cycle
Emerging Technologies
Summary
The menstrual cycle is controlled by complex hormone signals that affect reproduction. The authors created a new mathematical model that balances biological accuracy with simplicity. This model tracks four key hormones over time and mimics real hormone patterns seen in experiments. It is fast to run and can be easily extended, making it a useful tool for simulations in medicine and drug testing.
What this means in practice
- •For pharmacologists: Simulate hormonal responses to drugs targeting menstrual cycle regulation for improved treatment design.
- •For personalized medicine teams: Model individual hormone patterns to better tailor reproductive health therapies using efficient computational methods.
- •For biomedical simulation developers: Integrate the model into software platforms to enhance realistic simulation of endocrine system behavior cheaply.$Commercial implications: Enables development of commercial simulation tools for healthcare providers and pharmaceutical companies needing menstrual cycle modeling.
Authors
Lena Reitinger, Nina Nikic, Esma Hamzic-Jahic, Barbara Arbeithuber, Andreas Springer, Werner Haselmayr, Stefan Angerbauer
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.