A major challenge in plant developmental biology is to understand how plant growth is coordinated by interacting hormones and genes. To meet this challenge, it is important to not only use experimental data, but also formulate a mathematical model. For the mathematical model to best describe the true biological system, it is necessary to understand the parameter space of the model, along with the links between the model, the parameter space and experimental observations. We develop sequential history matching methodology, using Bayesian emulation, to gain substantial insight into biological model parameter spaces. This is achieved by finding sets of acceptable parameters in accordance with successive sets of physical observations. These methods are then applied to a complex hormonal crosstalk model for Arabidopsis root growth. In this application, we demonstrate how an initial set of 22 observed trends reduce the volume of the set of acceptable inputs to a proportion of 6.1 × 10⁻⁷ of the original space. Additional sets of biologically relevant experimental data, each of size 5, reduce the size of this space by a further three and two orders of magnitude respectively. Hence, we provide insight into the constraints placed upon the model structure by, and the biological consequences of, measuring subsets of observations.

中文翻译：

---

通过模拟和历史匹配了解拟南芥根发育中的激素串扰。

植物发育生物学的主要挑战是了解激素和基因之间的相互作用如何协调植物的生长。为了应对这一挑战，不仅要使用实验数据，而且要建立数学模型，这一点很重要。为了使数学模型最好地描述真实的生物系统，有必要了解模型的参数空间，以及模型，参数空间和实验观察值之间的联系。我们使用贝叶斯仿真开发顺序历史记录匹配方法，以深入了解生物学模型参数空间。这是通过根据物理观测的连续集合找到可接受参数的集合来实现的。然后将这些方法应用于拟南芥根生长的复杂激素串扰模型。在此应用程序中，原始空间的^-7。每组大小为5的其他生物学相关实验数据集分别将这个空间的大小减少了另外三个和两个数量级。因此，我们提供了对测量观测子集的模型结构所施加的约束及其生物学后果的见解。