Lin Ye, Andersson Sean B
Division of Systems Engineering, Boston, MA 02215, USA.
Department of Mechanical Engineering, Boston University, Boston, MA 02215, USA.
Proc Am Control Conf. 2021 May;2021:3945-3950. doi: 10.23919/acc50511.2021.9483034. Epub 2021 Jul 28.
Single particle tracking plays an important role in studying physical and kinetic properties of biomolecules. In this work, we introduce the application of Expectation Maximization (EM) based algorithms for solving localization and parameter estimation problems in SPT using data captured from scientific complementary metal-oxide semiconductor (sCMOS) camera sensors. Two representative methods are considered for generating the filtered and smoothed distributions needed by EM: Sequential Monte Carlo - EM, and Unscented - EM. The SMC method uses particle filtering and particle smoothing to handle general distributions, while the U scheme reduces the computational burden through the use of an unscented Kalman Filter and an unscented Rauch-Tung Striebel Smoother. We also investigate the influence of the number of images in the dataset on the final estimates through intensive simulations as well as the computational efficiency of the two methods.
单粒子追踪在研究生物分子的物理和动力学性质方面发挥着重要作用。在这项工作中，我们介绍了基于期望最大化（EM）算法的应用，用于使用从科学互补金属氧化物半导体（sCMOS）相机传感器捕获的数据来解决单粒子追踪中的定位和参数估计问题。考虑了两种代表性方法来生成EM所需的滤波和平滑分布：顺序蒙特卡罗 - EM和无迹 - EM。SMC方法使用粒子滤波和粒子平滑来处理一般分布，而U方案通过使用无迹卡尔曼滤波器和无迹Rauch-Tung-Striebel平滑器来减轻计算负担。我们还通过密集模拟研究了数据集中图像数量对最终估计的影响以及这两种方法的计算效率。
