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模型聚合物自组装成生物随机网络。

Self assembly of model polymers into biological random networks.

作者信息

Bailey Matthew H J, Wilson Mark

机构信息

Physical and Theoretical Chemistry Laboratory, South Parks Road, Oxford OX1 3QZ, United Kingdom.

出版信息

Comput Struct Biotechnol J. 2021 Feb 12;19:1253-1262. doi: 10.1016/j.csbj.2021.02.001. eCollection 2021.

Abstract

The properties of biological networks, such as those found in the ocular lens capsule, are difficult to study without simplified models. Model polymers are developed, inspired by "worm-like" curve models, that are shown to spontaneously self assemble to form networks similar to those observed experimentally in biological systems. These highly simplified coarse-grained models allow the self assembly process to be studied on near-realistic time-scales. Metrics are developed (using a polygon-based framework) which are useful for describing simulated networks and can also be applied to images of real networks. These metrics are used to show the range of control that the computational polymer model has over the networks, including the polygon structure and short range order. The structure of the simulated networks are compared to previous simulation work and microscope images of real networks. The network structure is shown to be a function of the interaction strengths, cooling rates and external pressure. In addition, "pre-tangled" network structures are introduced and shown to significantly influence the subsequent network structure. The network structures obtained fit into a region of the network landscape effectively inaccessible to random (entropically-driven) networks but which are occupied by experimentally-derived configurations.

摘要

生物网络的特性,比如在眼晶状体囊膜中发现的那些特性,如果没有简化模型就很难进行研究。受“蠕虫状”曲线模型启发,开发了模型聚合物,这些聚合物被证明能自发地自我组装形成与在生物系统中实验观察到的网络相似的网络。这些高度简化的粗粒度模型使得能够在接近实际的时间尺度上研究自我组装过程。开发了一些度量标准(使用基于多边形的框架),这些标准对于描述模拟网络很有用,也可应用于真实网络的图像。这些度量标准用于展示计算聚合物模型对网络的控制范围,包括多边形结构和短程有序。将模拟网络的结构与之前的模拟工作以及真实网络的显微镜图像进行了比较。网络结构被证明是相互作用强度、冷却速率和外部压力的函数。此外,引入了“预缠结”网络结构,并证明其对后续网络结构有显著影响。所获得的网络结构适合于随机(熵驱动)网络实际上难以到达但被实验得出的构型所占据的网络景观区域。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d398/7918283/f98195381f9a/ga1.jpg

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