从单粒子轨迹揭示扩散态及其跃迁的系统级方法
Systems-level approach to uncovering diffusive states and their transitions from single particle trajectories
摘要：
扩散粒子的随机运动包含粒子与结合伙伴及其局部环境相互作用的信息。然而，准确地确定基本的扩散特性，超出正常扩散，仍然具有挑战性时，分析粒子轨迹的个人基础上。在这里，我们介绍了限制扩散和分数布朗运动的极大似然估计（MLE）。我们证明，这种极大似然估计比传统的均方位移分析产生更好的估计。我们还介绍了一个模型选择方案（我们称之为mleBIC），将个人轨迹分类到一个给定的扩散模式。我们通过mleBIC使用模拟数据证明了分类的统计局限性。为了克服这些限制，我们引入了一个新版本的扰动期望最大化（pEMv 2），它同时分析了一组粒子轨迹，以揭示系统的相互作用，从而产生独特的正常和/或非正常的扩散状态的人口。我们测试和评估pEMv 2的性能，在不同的模拟粒子轨迹，正常和非正常扩散的几种模式之间的过渡，突出采用这种分析方法的关键考虑因素。
英文摘要：
The stochastic motions of a diffusing particle contain information concerning the particle's interactions with binding partners and with its local environment. However, accurate determination of the underlying diffusive properties, beyond normal diffusion, has remained challenging when analyzing particle trajectories on an individual basis. Here, we introduce the maximum likelihood estimator (MLE) for confined diffusion and fractional Brownian motion. We demonstrate that this MLE yields improved estimation over traditional mean square displacement analyses. We also introduce a model selection scheme (that we call mleBIC) that classifies individual trajectories to a given diffusion mode. We demonstrate the statistical limitations of classification via mleBIC using simulated data. To overcome these limitations, we introduce a new version of perturbation expectation-maximization (pEMv2), which simultaneously analyzes a collection of particle trajectories to uncover the system of interactions which give rise to unique normal and/or non-normal diffusive states within the population. We test and evaluate the performance of pEMv2 on various sets of simulated particle trajectories, which transition among several modes of normal and non-normal diffusion, highlighting the key considerations for employing this analysis methodology.
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