随机重置诱导的单粒子轨迹的受限模式
Confined modes of single-particle trajectories induced by stochastic resetting
AI总结：
本文通过随机重置解释活细胞中受限粒子轨迹的拉普拉斯与林尼克分布，揭示其幂律等待时间机制，并讨论相关模型。
摘要：
活细胞中单个粒子的随机轨迹包含关于粒子之间以及粒子与细胞环境之间相互作用的信息。然而，在分别应用于每条粒子轨迹时，对超越正常扩散的潜在随机性质的精确考虑仍然是一个挑战。在本文中，我们展示了活细胞中受限粒子的位置不仅可以服从拉普拉斯分布，还可以服从林尼克分布。这一特征在细胞中G蛋白和偶联受体运动的实验数据中被检测到，其起源用随机重置来解释。这一重置过程产生幂律等待时间，在受限运动中产生林尼克统计，并且也包括指数分布时间作为极限情况，导致拉普拉斯分布。受重置影响的随机过程可以是细胞中常见的布朗运动。还讨论了产生类似效应的其他可能模型。
英文摘要：
Random trajectories of single particles in living cells contain information about the interaction between particles, as well as, with the cellular environment. However, precise consideration of the underlying stochastic properties, beyond normal diffusion, remains a challenge as applied to each particle trajectory separately. In this paper, we show how positions of confined particles in living cells can obey not only the Laplace distribution, but the Linnik one. This feature is detected in experimental data for the motion of G proteins and coupled receptors in cells, and its origin is explained in terms of stochastic resetting. This resetting process generates power-law waiting times, giving rise to the Linnik statistics in confined motion, and also includes exponentially distributed times as a limit case leading to the Laplace one. The stochastic process, which is affected by the resetting, can be Brownian motion commonly found in cells. Other possible models producing similar effects are discussed.
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