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Published October 18, 2016 | Accepted Version
Journal Article Open

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


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.

Additional Information

© 2016 American Statistical Association. Received 01 Aug 2014, Accepted author version posted online: 02 Feb 2016, Published online: 18 Oct 2016. S. Shan's research is supported in part by NIH grant GM078024 and the Gordon and Betty Moore Foundation through Grant GBMF2939. S. C. Kou's research is supported in part by grants from NSF and ARO.

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