The dynamics of systems biological processes are usually modeled by a system of ordinary differential equations (ODEs) with many unknown parameters that need to be inferred from noisy and sparse measurements. Here, we introduce systems-biology informed neural networks for parameter estimation by incorporating the system of ODEs into the neural networks. To complete the workflow of system identification, we also describe structural and practical identifiability analysis to analyze the identifiability of parameters. We use the ultridian endocrine model for glucose-insulin interaction as the example to demonstrate all these methods and their implementation.

翻译：系统生物过程的动态通常以普通差异方程式(ODEs)系统为模型,其中有许多未知参数,需要从噪音和稀少的测量中推断出来。在这里,我们引入系统-生物学知情神经网络,通过将ODE系统纳入神经网络来进行参数估计。为了完成系统识别工作流程,我们还描述了结构和实际的可识别性分析,以分析参数的可识别性。我们用聚苯乙烯-内分泌模型作为示例,以展示所有这些方法及其实施。