Building a model of the Hypothalamic-Pituitary-Gonadal (HPG) axis

促性腺激素释放激素（GnRH）由下丘脑神经元分泌，控制垂体前叶促性腺激素LH和FSH的分泌，从而介导生殖的中枢控制。它以脉冲形式分泌，其作用取决于脉冲特性（特别是脉冲频率）。最近在生殖领域最重要的发现之一是KNDy系统的发现。这是一个多肽能神经元振荡器，驱动脉动性GnRH分泌。它还介导性腺类固醇激素的反馈效应，这些激素对关键的生殖过程至关重要，如青春期的时间和季节性繁殖（即农场动物），以及卵巢周期中促性腺激素分泌的微调。所有这些组成部分（下丘脑脉冲发生器/垂体脉冲解码器/性腺）对人类生殖是绝对必要的，所以这三者都是治疗干预的场所也许并不奇怪，就像它们中的任何一个受到干扰都可能导致疾病状态下的不孕一样。我们深信，要充分了解这样一个复杂的系统，需要数学和统计方法。为此，我们已经生成了一个复杂的神经元振荡器的数学模型，我们已经在wetlab上进行了训练。数据和现在被用来探索可能的机制调节性腺类固醇。同样，我们开发了一个数学模型，用于解码垂体促性腺激素的脉冲动力学，并利用它来探索细胞对GnRH反应的细胞变异性及其对信息传递的影响。我们现在计划采用类似的方法来模拟促性腺激素对性腺功能的影响，我们将不得不引入种群动态作为一个额外的特征（即排卵前颗粒细胞的生长和排卵后黄体细胞的形成）。然后，我们计划简化这些子系统模型，以便组件可以组装成整个下丘脑-垂体-性腺系统的有意义的模型。在这个项目中，学生将学习：下丘脑脉冲发生器的数学建模，垂体脉冲解码器和LH和FSH对性腺细胞的影响。数值优化通过执行参数拟合和灵敏度分析使用可用的实验数据集的组合参数化和验证模型组件。子系统模型的简化（根据敏感性分析），以及系统湿实验室数据的训练。计算方法，允许生成可测试的模型预测有关生殖系统及其组成部分在整个生命周期。

Gonadotropin-releasing hormone (GnRH) is secreted from hypothalamic neurones to control secretion of the gonadotropin hormones LH and FSH form the anterior pituitary, and thereby mediates central control of reproduction. It secretes in pulses and its effects are dependent on pulse characteristics (particularly pulse frequency). One of the most important recent discoveries in the field of reproduction is that of the KNDy system. This is a peptidergic neuronal oscillator that drives pulsatile GnRH secretion. It also mediates feedback effects of gonadal steroid hormones that are essential for key reproductive processes such as the timing of puberty and seasonal breeding (i.e. in farm animals) and the fine-tuning of gonadotropin secretion across the ovarian cycle. All of these components (hypothalamic pulse generator/pituitary pulse decoder/gonads) are absolutely essential for human reproduction so it is perhaps not surprising that all three are sites for therapeutic intervention, just as perturbation of any of them can cause infertility in disease states.We are convinced that mathematical and statistical approaches are required to fully understand such a complex system. To this end we have already generated a sophisticated mathematical model of the neuronal oscillator that we have trained on wetlab. data and are now using to explore possible mechanisms for modulation by gonadal steroids. Similarly, we have developed a mathematical model for the decoding of pulse dynamics by pituitary gonadotropes and have used this to explore cell-cell variability in responses to GnRH and its impact on information transfer. We now plan to take a similar approach to modelling effects of gonadotropin hormones on gonadal function, where we will have to introduce population dynamics as an additional feature (i.e. growth of granulosa cells before ovulation and formation of luteal cells thereafter). We then plan to simplify these sub-system models so that the components can be assembled into a meaningful model of the entire hypothalamo-pituitary-gonadal system.In this project the student will learn:Mathematical modelling of the hypothalamic pulse generator, the pituitary pulse decoder and the effects of LH and FSH on gonadal cells.Numerical optimisation by performing parameter fitting and sensitivity analysis using combination of available experimental data sets to parametrise and validate model components.Simplification of the sub-system models (as informed by sensitivity analysis) and again, training on system wet-lab data.Computational methods that allow the generation of testable model predictions regarding the reproductive system and its components across the lifespan.