Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
J Theor Biol. 2022 Aug 21;547:111150. doi: 10.1016/j.jtbi.2022.111150. Epub 2022 May 11.
We present a modelling and simulation framework for the dynamics of ovarian follicles and key hormones along the hypothalamic-pituitary-gonadal axis throughout consecutive human menstrual cycles. All simulation results (hormone concentrations and ovarian follicle sizes) are in biological units and can easily be compared to clinical data. The model takes into account variability in follicles' response to stimulating hormones, which introduces variability between cycles. The growth of ovarian follicles in waves is an emergent property in our model simulations and further supports the hypothesis that follicular waves are also present in humans. We use Approximate Bayesian Computation and cluster analysis to construct a population of virtual subjects and to study parameter distributions and sensitivities. The model can be used to compare and optimize treatment protocols for ovarian hyperstimulation, thus potentially forming the integral part of a clinical decision support system in reproductive endocrinology.
我们提出了一个建模和模拟框架，用于研究整个连续人类月经周期中卵巢卵泡和关键激素沿着下丘脑-垂体-性腺轴的动力学。所有的模拟结果（激素浓度和卵巢卵泡大小）都是在生物学单位，并且可以很容易地与临床数据进行比较。该模型考虑了卵泡对刺激激素反应的可变性，这在周期之间引入了可变性。我们的模型模拟中，卵巢卵泡的生长呈波状，这是一种涌现性质，进一步支持了卵泡波也存在于人类中的假设。我们使用近似贝叶斯计算和聚类分析来构建虚拟受试者群体，并研究参数分布和敏感性。该模型可用于比较和优化卵巢过度刺激的治疗方案，从而可能成为生殖内分泌学中临床决策支持系统的组成部分。
