发表机构
Institute for Communications Engineering and RF-Systems, Johannes Kepler University Linz; Department of Gynecology, Obstetrics, and Gynecological Endocrinology, Kepler Universitätsklinikum（约翰内斯·开普林茨大学通信工程与射频系统研究所; 开普大学附属医院妇科、产科及妇科内分泌科）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种平衡生物真实性与数学简洁性的人类月经周期机制模型，用常微分方程描述四种核心激素动态，再现实验趋势，适用于药理和个性化医疗模拟。
AI 中文摘要
人类月经周期受复杂的激素反馈机制调控，这些机制对生殖健康至关重要。现有的数学模型要么依赖于简化的现象学假设，要么以高数学复杂性和计算成本为代价来实现生理准确性。本文提出了一种新颖的机制模型，在生物现实性与数学简洁性之间取得了平衡。所提出的框架通过常微分方程组描述了四种核心周期驱动激素的动态，即促卵泡激素、促黄体生成激素、雌二醇和孕酮。机制细节通过反馈机制、卵泡发育、黄体化、黄体血管化以及激素合成机制方面的表征得以纳入。尽管其复杂性相对较低，该模型仍能再现实验数据中报道的激素活性和浓度动态的特征性趋势。由于其计算效率和模块化特性，该模型非常适合作为扩展的计算机模拟实验的基础，包括药理学和个性化医疗中的应用。我们的结果表明，有针对性地纳入生物机制能够实现对内分泌系统动态的准确且易处理的建模。
英文摘要
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.
Comments8 pages, 6 figures
