间充质细胞的速度可能不明确

Guilherme S. Y. Giardini, Gilberto L. Thomas, Carlo R. da Cunha, Rita M. C. de Almeida
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引用次数: 0

摘要

单细胞在平面上的迁移动力学通常用由短时间的弹道运动和随机游动组成的类朗万问题来建模。很长一段时间。相反,最近的研究揭示了以前被忽视的非常短间隔的随机运动,这将排除定义细胞瞬时速度和可靠测量程序的可能性。先前解决这一问题的尝试考虑了非各向异性迁移模型,该模型考虑了沿速度定义良好的极化方向和与描述随机游走的极化矢量正交的方向。虽然数值和解析计算的均方位移和自相关与该模型的实验数据一致,但速度分布在零处达到峰值,这与实验观测到的极化方向恒定漂移相矛盾。此外,波茨模型模拟表明,瞬时速度无法在任何方向上测量。这里,我们考虑细胞极化的动力学方程,它是可测量的,并引入了一个与极化相关的位移,避免了瞬时速度定义不清的问题。极化是一个定义良好的量,在短时间间隔内保持记忆,并为表征细胞迁移提供了一个可靠的测量程序。我们认为细胞极化动力学遵循一个修正的朗格万方程,该方程产生细胞位移分布,峰值为正值,与实验和波茨模型模拟一致。此外,位移自相关函数呈现两个不同的时间尺度,提高了理论拟合和实验或模拟之间的一致性。
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Velocities of Mesenchymal Cells May be Ill-Defined
The dynamics of single cell migration on flat surfaces is usually modeled by a Langevin-like problem consisting of ballistic motion for short periods and random walk. for long periods. Conversely, recent studies have revealed a previously neglected random motion at very short intervals, what would rule out the possibility of defining the cell instantaneous velocity and a robust measurement procedure. A previous attempt to address this issue considered an anisotropic migration model, which takes into account a polarization orientation along which the velocity is well-defined, and a direction orthogonal to the polarization vector that describes the random walk. Although the numerically and analytically calculated mean square displacement and auto-correlation agree with experimental data for that model, the velocity distribution peaks at zero, which contradicts experimental observations of a constant drift in the polarization direction. Moreover, Potts model simulations indicate that instantaneous velocity cannot be measured for any direction. Here, we consider dynamical equations for cell polarization, which is measurable and introduce a polarization-dependent displacement, circumventing the problem of ill defined instantaneous velocity. Polarization is a well-defined quantity, preserves memory for short intervals, and provides a robust measurement procedure for characterizing cell migration. We consider cell polarization dynamics to follow a modified Langevin equation that yields cell displacement distribution that peaks at positive values, in agreement with experiments and Potts model simulations. Furthermore, displacement autocorrelation functions present two different time scales, improving the agreement between theoretical fits and experiments or simulations.
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