论文标题
使用时间表数据估算和评估微分方程模型
Estimating and Assessing Differential Equation Models with Time-Course Data
论文作者
论文摘要
普通的微分方程（ODE）模型被广泛用于描述化学或生物过程。本文根据时间表数据考虑了此类模型的估计和评估。由于实验局限性，时间课程数据通常是嘈杂的，并且可能不会观察到系统的某些组件。此外，数值集成的计算需求阻碍了使用ODE的时间课程分析的广泛采用。为了应对这些挑战，我们探讨了最近开发的MAGI（歧管受限的高斯工艺推断）方法的效力。首先，通过一系列示例，我们表明MAGI能够通过适当的不确定性定量来推断参数和系统轨迹，包括未观察到的组件。其次，我们说明了如何使用MAGI根据MAGI对模型预测的有效计算进行时间课程数据来评估和选择不同的ODE模型。总体而言，我们认为MAGI是在ODE模型中分析时间课程数据的有用方法，它绕开了对任何数值集成的需求。
Ordinary differential equation (ODE) models are widely used to describe chemical or biological processes. This article considers the estimation and assessment of such models on the basis of time-course data. Due to experimental limitations, time-course data are often noisy and some components of the system may not be observed. Furthermore, the computational demands of numerical integration have hindered the widespread adoption of time-course analysis using ODEs. To address these challenges, we explore the efficacy of the recently developed MAGI (MAnifold-constrained Gaussian process Inference) method for ODE inference. First, via a range of examples we show that MAGI is capable of inferring the parameters and system trajectories, including unobserved components, with appropriate uncertainty quantification. Second, we illustrate how MAGI can be used to assess and select different ODE models with time-course data based on MAGI's efficient computation of model predictions. Overall, we believe MAGI is a useful method for the analysis of time-course data in the context of ODE models, which bypasses the need for any numerical integration.
