Improved hidden Markov models for molecular motors, part 1: Basic theory

Fiona E. Müllner, Sheyum Syed, Paul R. Selvin, Fred J. Sigworth

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Hidden Markov models (HMMs) provide an excellent analysis of recordings with very poor signal/noise ratio made from systems such as ion channels which switch among a few states. This method has also recently been used for modeling the kinetic rate constants of molecular motors, where the observable variable - the position - steadily accumulates as a result of the motor's reaction cycle. We present a new HMM implementation for obtaining the chemical-kinetic model of a molecular motor's reaction cycle called the variable-stepsize HMM in which the quantized position variable is represented by a large number of states of the Markov model. Unlike previous methods, the model allows for arbitrary distributions of step sizes, and allows these distributions to be estimated. The result is a robust algorithm that requires little or no user input for characterizing the stepping kinetics of molecular motors as recorded by optical techniques.

Original languageEnglish (US)
Pages (from-to)3684-3695
Number of pages12
JournalBiophysical journal
Issue number11
StatePublished - Dec 1 2010
Externally publishedYes

ASJC Scopus subject areas

  • Biophysics


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