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任意维度下蠕虫状链的端到端分布

End-to-end distribution for a wormlike chain in arbitrary dimensions.

作者信息

Mehraeen Shafigh, Sudhanshu Bariz, Koslover Elena F, Spakowitz Andrew J

机构信息

Department of Mechanical Engineering, Stanford University, Stanford, California 94305, USA.

出版信息

Phys Rev E Stat Nonlin Soft Matter Phys. 2008 Jun;77(6 Pt 1):061803. doi: 10.1103/PhysRevE.77.061803. Epub 2008 Jun 9.

Abstract

We construct an efficient methodology for calculating wormlike chain statistics in arbitrary D dimensions over all chain rigidities, from fully rigid to completely flexible. The structure of our exact analytical solution for the end-to-end distribution function for a wormlike chain in arbitrary D dimensions in Fourier-Laplace space (i.e., Fourier-transformed end position and Laplace-transformed chain length) adopts the form of an infinite continued fraction, which is advantageous for its compact structure and stability for numerical implementation. We then proceed to present a step-by-step methodology for performing the Fourier-Laplace inversion in order to make full use of our results in general applications. Asymptotic methods for evaluating the Laplace inversion (power-law expansion and Rayleigh-Schrödinger perturbation theory) are employed in order to improve the accuracy of the numerical inversions of the end-to-end distribution function in real space. We adapt our results to the evaluation of the single-chain structure factor, rendering simple, closed-form expressions that facilitate comparison with scattering experiments. Using our techniques, the accuracy of the end-to-end distribution function is enhanced up to the limit of the machine precision. We demonstrate the utility of our methodology with realizations of the chain statistics, giving a general methodology that can be applied to a wide range of biophysical problems.

摘要

我们构建了一种高效的方法,用于计算任意维度D下、所有链刚性(从完全刚性到完全柔性)的蠕虫状链统计量。我们在傅里叶 - 拉普拉斯空间(即傅里叶变换后的末端位置和拉普拉斯变换后的链长)中针对任意维度D的蠕虫状链的端到端分布函数的精确解析解结构,采用了无限连分数的形式,这因其紧凑结构和数值实现的稳定性而具有优势。然后,我们给出了一种逐步进行傅里叶 - 拉普拉斯反演的方法,以便在一般应用中充分利用我们的结果。为了提高实空间中端到端分布函数数值反演的精度,我们采用了评估拉普拉斯反演的渐近方法(幂律展开和瑞利 - 薛定谔微扰理论)。我们将结果应用于单链结构因子的评估,得到了便于与散射实验进行比较的简单闭式表达式。使用我们的技术,端到端分布函数的精度提高到了机器精度的极限。我们通过链统计量的实例展示了我们方法的实用性,给出了一种可应用于广泛生物物理问题的通用方法。

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