The haematopoietic system has a highly regulated and complex structure in which cells are organized to successfully create and maintain new blood cells. It is known that feedback regulation is crucial to tightly control this system, but the specific mechanisms by which control is exerted are not completely understood. In this work, we aim to uncover the underlying mechanisms in haematopoiesis by conducting perturbation experiments, where animal subjects are exposed to an external agent in order to observe the system response and evolution. We have developed a novel Bayesian hierarchical framework for optimal design of perturbation experiments and proper analysis of the data collected. We use a deterministic model that accounts for feedback and feedforward regulation on cell division rates and self-renewal probabilities. A significant obstacle is that the experimental data are not longitudinal, rather each data point corresponds to a different animal. We overcome this difficulty by modelling the unobserved cellular levels as latent variables. We then use principles of Bayesian experimental design to optimally distribute time points at which the haematopoietic cells are quantified. We evaluate our approach using synthetic and real experimental data and show that an optimal design can lead to better estimates of model parameters.

中文翻译：

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造血数学模型的优化实验设计


造血系统具有高度调节和复杂的结构，其中细胞被组织起来以成功地产生和维持新的血细胞。众所周知，反馈调节对于严格控制该系统至关重要，但施加控制的具体机制尚不完全清楚。在这项工作中，我们的目标是通过进行扰动实验来揭示造血的潜在机制，其中动物受试者暴露于外部介质以观察系统反应和进化。我们开发了一种新颖的贝叶斯分层框架，用于扰动实验的优化设计和对收集的数据的正确分析。我们使用确定性模型来解释细胞分裂率和自我更新概率的反馈和前馈调节。一个重要的障碍是实验数据不是纵向的，而是每个数据点对应于不同的动物。我们通过将未观察到的细胞水平建模为潜在变量来克服这一困难。然后，我们使用贝叶斯实验设计原理来优化分布造血细胞定量的时间点。我们使用合成和真实的实验数据评估我们的方法，并表明优化设计可以更好地估计模型参数。