Churkin Alexander, Reinharz Vladimir, Lewkiewicz Stephanie, Dahari Harel, Barash Danny
Department of Software Engineering, Sami Shamoon College of Engineering, Beer-Sheva, Israel.
Center for Soft and Living Matter, Institute for Basic Science, Ulsan, South Korea.
AIP Conf Proc. 2020;2293. doi: 10.1063/5.0026600. Epub 2020 Nov 25.
Callibration in mathematical models that are based on differential equations is known to be of fundamental importance. For sophisticated models such as age-structured models that simulate biological agents, parameter estimation or fitting (callibration) that solves all cases of data points available presents a formidable challenge, as efficiency considerations need to be employed in order for the method to become practical. In the case of multiscale models of hepatitis C virus dynamics that deal with partial differential equations (PDEs), a fully numerical parameter estimation method was developed that does not require an analytical approximation of the solution to the multiscale model equations, avoiding the necessity to derive the long-term approximation for each model. However, the method is considerably slow because of precision problems in estimating derivatives with respect to the parameters near their boundary values, making it almost impractical for general use. In order to overcome this limitation, two steps have been taken that significantly reduce the running time by orders of magnitude and thereby lead to a practical method. First, constrained optimization is used, letting the user add constraints relating to the boundary values of each parameter before the method is executed. Second, optimization is performed by derivative-free methods, eliminating the need to evaluate expensive numerical derviative approximations. These steps that were successful in significantly speeding up a highly non-efficient approach, rendering it practical, can also be adapted to multiscale models of other viruses and other sophisticated differential equation models. The newly efficient methods that were developed as a result of the above approach are described. Illustrations are provided using a user-friendly simulator that incorporates the efficient methods for multiscale models. We provide a simulator called HCVMultiscaleFit with a Graphical User Interface that applies these methods and is useful to perform parameter estimation for simulating viral dynamics during antiviral treatment.
基于微分方程的数学模型中的校准具有至关重要的意义。对于诸如模拟生物主体的年龄结构模型等复杂模型,求解所有可用数据点情况的参数估计或拟合(校准)是一项艰巨的挑战,因为需要考虑效率才能使该方法变得实用。在处理偏微分方程(PDE)的丙型肝炎病毒动力学多尺度模型的情况下,开发了一种完全数值参数估计方法,该方法不需要对多尺度模型方程的解进行解析近似,避免了为每个模型推导长期近似值的必要性。然而,由于在估计参数边界值附近的导数时存在精度问题,该方法相当缓慢,几乎无法普遍使用。为了克服这一限制,采取了两个步骤,将运行时间显著减少了几个数量级,从而产生了一种实用的方法。首先,使用约束优化,让用户在方法执行前添加与每个参数边界值相关的约束。其次,通过无导数方法进行优化,无需评估昂贵的数值导数近似值。这些成功显著加速了一种效率极低的方法并使其变得实用的步骤,也可适用于其他病毒的多尺度模型和其他复杂的微分方程模型。描述了因上述方法而开发的新的高效方法。使用一个包含多尺度模型高效方法的用户友好模拟器提供了示例。我们提供了一个名为HCVMultiscaleFit的模拟器,它带有图形用户界面,应用这些方法,可用于在抗病毒治疗期间模拟病毒动力学进行参数估计。