Analyzing Single-Molecule Protein Transportation Experiments via Hierarchical Hidden Markov Models. Issue 515 (2nd July 2016)

• Record Type: Journal Article
• Authors: Chen, Yang; Shen, Kuang; Shan, Shu-Ou; Kou, S. C.
• Is Part Of: Journal of the American Statistical Association. Volume 111:Issue 515(2016)
• Page Start: 951
• Page End: 966
• Publication Date: 2016-07-02
• Subjects: Conformational change -- FRET -- Hierarchical model -- MCMC (Markov chain Monte Carlo) -- Model checking -- Protein targeting; Statistics -- Periodicals; Statistics -- Periodicals; Statistiques -- Périodiques; États-Unis -- Statistiques -- Périodiques; 519.5
• DOI: 10.1080/01621459.2016.1140050
• Languages: English
• ISSNs: 0162-1459

Abstract

ABSTRACT: To maintain proper cellular functions, over 50% of proteins encoded in the genome need to be transported to cellular membranes. The molecular mechanism behind such a process, often referred to as protein targeting, is not well understood. Single-molecule experiments are designed to unveil the detailed mechanisms and reveal the functions of different molecular machineries involved in the process. The experimental data consist of hundreds of stochastic time traces from the fluorescence recordings of the experimental system. We introduce a Bayesian hierarchical model on top of hidden Markov models (HMMs) to analyze these data and use the statistical results to answer the biological questions. In addition to resolving the biological puzzles and delineating the regulating roles of different molecular complexes, our statistical results enable us to propose a more detailed mechanism for the late stages of the protein targeting process.