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Analyzing Single-Molecule Protein Transportation Experiments via Hierarchical Hidden Markov Models

Author

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  • Yang Chen
  • Kuang Shen
  • Shu-Ou Shan
  • S. C. Kou

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.

Suggested Citation

  • Yang Chen & Kuang Shen & Shu-Ou Shan & S. C. Kou, 2016. "Analyzing Single-Molecule Protein Transportation Experiments via Hierarchical Hidden Markov Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 951-966, July.
    • Handle: RePEc:taf:jnlasa:v:111:y:2016:i:515:p:951-966
    DOI: 10.1080/01621459.2016.1140050
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    Cited by:

    1. Sadie Piatt & Allen C Price, 2019. "Analyzing dwell times with the Generalized Method of Moments," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-20, January.
    2. Emily Lin & Chu-Lan Michael Kao & Natasha Sonia Adityarini, 2021. "Data-driven tree structure for PIN models," Review of Quantitative Finance and Accounting, Springer, vol. 57(2), pages 411-427, August.

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