EM-based algorithms for single particle tracking of Ornstein-Uhlenbeck motion from sCMOS camera data

• Published in: 2021 American Control Conference (ACC) Vol. 2021; pp. 3945 - 3950
• Main Authors: Lin, Ye, Andersson, Sean B.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: American Automatic Control Council 25.05.2021
• Subjects: Cameras; Estimation; Location awareness; Molecular biophysics; Monte Carlo methods; Parameter estimation; Smoothing methods
• ISSN: 0743-1619; 2378-5861
• DOI: 10.23919/ACC50511.2021.9483034

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.