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Thermodynamic Modeling of the Statistics of Cell Spreading on Ligand-Coated Elastic Substrates

机译:配体涂覆的弹性基材上细胞扩散统计的热力学建模

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摘要

Biological spread cells exist in a perpetually fluctuating state and therefore cannot be described in terms of a unique deterministic system. For modeling approaches to provide novel insight and uncover new mechanisms that drive cell behavior, a framework is required that progresses from traditional deterministic methods (whereby simulation of an experiment predicts a single outcome). In this study, we implement a new, to our knowledge, modeling approach for the analysis of cell spreading on ligand-coated substrates, extending the framework for nonequilibrium thermodynamics of cells developed by Shishvan et al. to include active focal adhesion assembly. We demonstrate that the model correctly predicts the coupled influence of surface collagen density and substrate stiffness on cell spreading, as reported experimentally by Engler et al. Low surface collagen densities are shown to result in a high probability that cells will be restricted to low spread areas. Furthermore, elastic free energy induced by substrate deformation lowers the probability of observing a highly spread cell, and, consequentially, lower cell tractions affect the assembly of focal adhesions. Experimentally measurable observables such as cell spread area and aspect ratio can be directly postprocessed from the computed homeostatic ensemble of (several million) spread states. This allows for the prediction of mean and SDs of such experimental observables. This class of cell mechanics modeling presents a significant advance on conventional deterministic approaches.
机译:生物扩散细胞以永久波动的状态存在,因此无法根据独特的确定性系统进行描述。为了提供新颖的见解并发现驱动细胞行为的新机制的建模方法,需要一个从传统确定性方法发展而来的框架(由此,模拟实验可预测单个结果)。在这项研究中,我们采用了我们所知的一种新的建模方法来分析细胞在配体涂覆的基质上的扩散,从而扩展了Shishvan等人开发的细胞非平衡热力学框架。包括主动粘着组件。我们证明该模型正确预测了表面胶原蛋白密度和底物刚度对细胞扩散的耦合影响,如Engler等人的实验报道。低表面胶原蛋白密度显示出将细胞限制在低扩散区域的高可能性。此外,由基板变形引起的弹性自由能降低了观察到细胞高度扩散的可能性,因此,较低的细胞牵引力会影响粘着斑的组装。实验上可测量的可观测值(例如细胞扩散面积和长宽比)可以直接从(几百万)扩散状态的稳态集合中进行后处理。这可以预测此类实验可观察物的平均值和标准差。这类细胞力学建模代表了传统确定性方法的重大进步。

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