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Hidden Markov modeling (HMM) is a powerful alternative to common approaches for analyzing particle trajectories, such as kymograph and mean-square displacement (MSD) analyses, due to its ability to annotate heterogeneous motion locally along a single trajectory. HMMs account for the possibility of stochastic switching between distinct motion states with single-step temporal resolution without time averaging along a trajectory. Incorporation of Bayesian model selection into the inference process additionally enables objective selection of the simplest stochastic motion model that describes a given trajectory.


We developed a versatile HMM procedure that can be applied both to diffusive switching and to active transport processes interspersed with random pausing events. This HMM analysis approach requires the statistical hypothesis that a particle explores a finite set of diffusive and directed transport motion states whose switching can be modeled as a Markov process. Our procedure, HMM-Bayes, models diffusive and directed motion states along particle trajectories and performs Bayesian model selection to infer the simplest stochastic motion model that is consistent with the observed particle displacements. The procedure can be applied to either a single trajectory or a set of pooled trajectories, annotating intermittent periods of diffusive and directed motion locally along each trajectory to reveal when and where switching between distinct types of motion occurs in space and time.


For more information about HMM-Bayes, please see our recent publication:

Monnier, N., Barry, Z., Park, H.Y., Su, K.C., Katz, Z., English, B., Dey, A., Pan, K., Cheeseman, I., Singer, R., Bathe, M. Inferring transient particle transport dynamics in live cells. Nature Methods, 12: 838 (2015). [ PubMed Article ]