Analyzing Single-Molecule Protein Transportation Experiments via Hierarchical Hidden Markov Models
针对蛋白质靶向过程中单分子实验产生的随机荧光时间序列数据，引入基于隐马尔可夫模型的贝叶斯分层模型进行分析，以揭示分子机制并回答生物学问题。
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.