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使用Lamm方程的自适应时空有限元解对分析超速离心实验进行建模。

Modeling analytical ultracentrifugation experiments with an adaptive space-time finite element solution of the Lamm equation.

作者信息

Cao Weiming, Demeler Borries

机构信息

Department of Applied Mathematics, The University of Texas at San Antonio, Texas, USA.

出版信息

Biophys J. 2005 Sep;89(3):1589-602. doi: 10.1529/biophysj.105.061135. Epub 2005 Jun 24.

Abstract

Analytical ultracentrifugation experiments can be accurately modeled with the Lamm equation to obtain sedimentation and diffusion coefficients of the solute. Existing finite element methods for such models can cause artifactual oscillations in the solution close to the endpoints of the concentration gradient, or fail altogether, especially for cases where somega(2)/D is large. Such failures can currently only be overcome by an increase in the density of the grid points throughout the solution at the expense of increased computational costs. In this article, we present a robust, highly accurate and computationally efficient solution of the Lamm equation based on an adaptive space-time finite element method (ASTFEM). Compared to the widely used finite element method by Claverie and the moving hat method by Schuck, our ASTFEM method is not only more accurate but also free from the oscillation around the cell bottom for any somega(2)/D without any increase in computational effort. This method is especially superior for cases where large molecules are sedimented at faster rotor speeds, during which sedimentation resolution is highest. We describe the derivation and grid generation for the ASTFEM method, and present a quantitative comparison between this method and the existing solutions.

摘要

分析超速离心实验可以用拉梅方程精确建模,以获得溶质的沉降系数和扩散系数。现有的针对此类模型的有限元方法可能会在浓度梯度端点附近的解中引起人为振荡,或者完全失效,特别是在(\omega^2/D)较大的情况下。目前,只有通过增加整个解中的网格点密度来克服这些失效情况,但这会以增加计算成本为代价。在本文中,我们基于自适应时空有限元方法(ASTFEM)提出了一种鲁棒、高精度且计算高效的拉梅方程解。与Claverie广泛使用的有限元方法和Schuck的移动帽方法相比,我们的ASTFEM方法不仅更精确,而且对于任何(\omega^2/D),在细胞底部周围都不会出现振荡,且无需增加计算量。该方法对于大分子在更快的转子速度下沉降的情况尤为优越,在此期间沉降分辨率最高。我们描述了ASTFEM方法的推导和网格生成,并给出了该方法与现有解之间的定量比较。

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