论文标题

使用机械稳定性分析了解细胞骨架雪崩

Understanding cytoskeletal avalanches using mechanical stability analysis

论文作者

Floyd, Carlos, Levine, Herbert, Jarzynski, Christopher, Papoian, Garegin A.

论文摘要

真核细胞由称为细胞骨架的聚合物网络机械支持,该网络消耗了化学能,以动态重塑其结构。在体内的最新实验表明,这种重塑偶尔会通过异常大型位移发生,让人联想到地震或雪崩。这些细胞骨架雪崩可能表明细胞骨架对不断变化的细胞环境的结构反应非常敏感,因此它们具有很大的生物学意义。但是,“细胞阻虫”的物理学知之甚少。在这里,我们使用基于代理的细胞骨架自组织的模拟来研究网络机械能中的波动。与在最小的细胞骨架模型中的积累相比,我们强劲地观察到非高斯统计数据和不对称的能量释放速率。发现大型能量释放的事件与细胞骨骼细丝的大型集体位移相关。我们还发现,张力的定位以及网络运动在振动正常模式上的投影的变化是不对称分布的,以释放和累积。这些结果暗示着一个类似雪崩的过程的缓慢储能的过程,该过程涉及集体网络重排的快速,大型能源释放事件。我们进一步表明,机械不稳定性是通过机器学习模型进行的,该模型通过使用振动频谱作为输入来动态预测cytoquake。我们的结果提供了Cytoquake现象与网络的机械能之间的第一个联系,并可以帮助指导对细胞骨架的结构敏感性的未来研究。

Eukaryotic cells are mechanically supported by a polymer network called the cytoskeleton, which consumes chemical energy to dynamically remodel its structure. Recent experiments in vivo have revealed that this remodeling occasionally happens through anomalously large displacements, reminiscent of earthquakes or avalanches. These cytoskeletal avalanches might indicate that the cytoskeleton's structural response to a changing cellular environment is highly sensitive, and they are therefore of significant biological interest. However, the physics underlying "cytoquakes" is poorly understood. Here, we use agent-based simulations of cytoskeletal self-organization to study fluctuations in the network's mechanical energy. We robustly observe non-Gaussian statistics and asymmetrically large rates of energy release compared to accumulation in a minimal cytoskeletal model. The large events of energy release are found to correlate with large, collective displacements of the cytoskeletal filaments. We also find that the changes in the localization of tension and the projections of the network motion onto the vibrational normal modes are asymmetrically distributed for energy release and accumulation. These results imply an avalanche-like process of slow energy storage punctuated by fast, large events of energy release involving a collective network rearrangement. We further show that mechanical instability precedes cytoquake occurrence through a machine learning model that dynamically forecasts cytoquakes using the vibrational spectrum as input. Our results provide the first connection between the cytoquake phenomenon and the network's mechanical energy and can help guide future investigations of the cytoskeleton's structural susceptibility.

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