Hidden Markov Models with Applications in Cell Adhesion Experiments

Abstract

Cell adhesion experiments refer to biomechanical experiments that study protein, DNA, and RNA at the level of single molecules. The study of cell adhesion plays a key role in many physiological and pathological processes, especially in tumor metastasis in cancer research. Motivated by the analysis of a specific type of cell adhesion experiments, a new framework based on hidden Markov model is proposed. A double penalized order selection procedure is introduced and shown to be consistent in estimating the number of hidden states in hidden Markov models. Simulations show that the proposed framework outperforms existing methods. Applications of the proposed methodology to real data demonstrate the accuracy of estimating receptor-ligand bond lifetimes and waiting times, which are essential in kinetic parameter estimation. ?