A time-varying approach to single particle tracking with a nonlinear observation model.

Boris I Godoy, Ye Lin, Sean B Andersson
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Abstract

Single Particle Tracking (SPT) is a powerful class of tools for analyzing the dynamics of individual biological macromolecules moving inside living cells. The acquired data is typically in the form of a sequence of camera images that are then post-processed to reveal details about the motion. In this work, we develop a local time-varying estimation algorithm for estimating motion model parameters from the data considering nonlinear observations. Our approach uses several well-known existing tools, namely the Expectation Maximization (EM) algorithm combined with an Unscented Kalman filter (UKF) and an Unscented Rauch-Tung-Striebel smoother (URTSS), and applies them to the time-varying case through a sliding window methodology. Due to the shot noise characteristics of the photon generation process, this model uses a Poisson distribution to capture the measurement noise inherent in imaging. In order to apply our time-varying approach to the UKF, we first need to transform the measurements into a model with additive Gaussian noise. This is carried out using a variance stabilizing transform. Results from simulations show that our approach is successful in tracing time-varying diffusion constants at a range of physically relevant signal levels. We also discuss the initialization for the EM algorithm based on the available data.

采用非线性观测模型的单粒子时变跟踪法
单粒子跟踪(SPT)是一类功能强大的工具,用于分析活细胞内单个生物大分子的运动动态。获取的数据通常以相机图像序列的形式存在,然后对其进行后处理,以揭示运动细节。在这项工作中,我们开发了一种局部时变估计算法,用于从数据中估计运动模型参数,同时考虑非线性观测。我们的方法使用了几种众所周知的现有工具,即期望最大化(EM)算法与无标点卡尔曼滤波器(UKF)和无标点劳赫-董-斯特里贝平滑器(URTSS)相结合,并通过滑动窗口方法将其应用于时变情况。由于光子产生过程的射击噪声特性,该模型使用泊松分布来捕捉成像中固有的测量噪声。为了将时变方法应用于 UKF,我们首先需要将测量结果转换为加性高斯噪声模型。这需要使用方差稳定变换来实现。模拟结果表明,我们的方法能在一系列物理相关信号水平下成功追踪时变扩散常数。我们还讨论了基于可用数据的 EM 算法初始化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
2.40
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