论文标题
系统生物学：通过系统生物学知情神经网络可识别性分析和参数识别
Systems Biology: Identifiability analysis and parameter identification via systems-biology informed neural networks
论文作者
论文摘要
系统生物过程的动力学通常是通过普通微分方程（ODE）系统建模的，其中许多未知参数需要从嘈杂和稀疏的测量值中推断出来。在这里，我们通过将ODE系统纳入神经网络中，介绍了系统生物学知情的神经网络，以进行参数估计。为了完成系统识别的工作流程，我们还描述了结构性和实践可识别性分析，以分析参数的可识别性。我们将超同学内分泌模型用于葡萄糖 - 胰岛素相互作用作为证明所有这些方法及其实现的示例。
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.
