Metabolic networks are typically large, containing many metabolites and reactions. Dynamical models that aim to simulate such networks will consist of a large number of ordinary differential equations, with many kinetic parameters that must be estimated from experimental data. We assume these data to be metabolomics measurements made under steady-state conditions for different input fluxes. Assuming linear kinetics, analytical criteria for parameter identifiability are provided. For normally distributed error terms, we also calculate the Fisher information matrix analytically to be used in the D-optimality criterion. A test network illustrates the developed tool chain for finding an optimal experimental design. The first stage is to verify global or pointwise parameter identifiability, the second stage to find optimal input fluxes, and finally remove redundant measurements.

中文翻译：

---

代谢网络稳态线性模型中参数估计的实验设计。

代谢网络通常很大，包含许多代谢物和反应。旨在模拟此类网络的动力学模型将由大量的常微分方程组成，其中许多动力学参数必须根据实验数据进行估算。我们假设这些数据是在稳态条件下针对不同输入通量进行的代谢组学测量。假设线性动力学，提供了参数可识别性的分析标准。对于正态分布的误差项，我们还分析性地计算了Fisher信息矩阵以用于D优化准则。一个测试网络说明了用于找到最佳实验设计的已开发工具链。第一阶段是验证全局或逐点参数的可识别性，第二阶段是找到最佳输入通量，