肌动蛋白驱动转运下的波形蛋白组织模型
Models of Vimentin Organization Under Actin-Driven Transport
AI总结：
本研究针对微管破坏后胶质细胞内波形蛋白的转运机制问题，通过构建双态动力学模型结合差分进化算法与赤池信息准则分析，发现空间依赖的中间丝捕获或肌动蛋白转运速度能最好解释实验现象。
摘要：
中间丝形成遍布细胞质的重要结构网络，在细胞力学、胞内组织和分子信号传导中发挥关键作用。该网络的维持及其对细胞动态行为的适应依赖于多种涉及细胞骨架串扰的机制，目前这些机制尚未被完全阐明。数学建模使我们能够比较多种符合生物学现实的场景，以辅助解读实验数据。在本研究中，我们观察并模拟了接种在圆形微图案上的单个胶质细胞中，经诺考达唑处理破坏微管后的波形蛋白中间丝的动态变化。在这些条件下，波形蛋白丝向细胞中心移动并积累，最终达到稳态。在缺乏微管驱动转运的情况下，波形蛋白网络的运动主要由肌动蛋白相关机制驱动。为模拟这些实验结果，我们假设波形蛋白可存在两种状态：可动态和不可动态，且两种状态以未知的（恒定或非恒定）速率切换。我们假设可动态波形蛋白以恒定或非恒定的速度平流输送。基于这组假设，我们提出了多种符合生物学现实的场景。针对每种场景，我们使用差分进化算法寻找能产生与实验数据最匹配解的最优参数集，随后采用赤池信息准则评估这些假设。这种建模方法使我们得出结论：我们的实验数据最能被中间丝的空间依赖性捕获或肌动蛋白依赖性转运的空间依赖性速度所解释。
英文摘要：
Intermediate filaments form an essential structural network, spread throughout the cytoplasm and play a key role in cell mechanics, intracellular organization and molecular signaling. The maintenance of the network and its adaptation to the cell's dynamic behavior relies on several mechanisms implicating cytoskeletal crosstalk which are not fully understood. Mathematical modeling allows us to compare several biologically realistic scenarios to help us interpret experimental data. In this study, we observe and model the dynamics of the vimentin intermediate filaments in single glial cells seeded on circular micropatterns following microtubule disruption by nocodazole treatment. In these conditions, the vimentin filaments move towards the cell center and accumulate before eventually reaching a steady-state. In absence of microtubule-driven transport, the motion of the vimentin network is primarily driven by actin-related mechanisms. To model these experimental findings, we hypothesize that vimentin may exist in two states, mobile and immobile, and switches between the states at unknown (either constant or non-constant) rates. Mobile vimentin are assumed to advect with either constant or non-constant velocity. We introduce several biologically realistic scenarios using this set of assumptions. For each scenario, we use differential evolution to find the best parameter sets resulting in a solution that most closely matches the experimental data, then the assumptions are evaluated using the Akaike Information Criterion. This modeling approach allows us to conclude that our experimental data are best explained by a spatially dependent trapping of intermediate filaments or a spatially dependent speed of actin-dependent transport.
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