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Improved Hidden Markov Models for Molecular Motors, Part 1: Basic Theory

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Mullner,  F. E.
Department: Cellular and Systems Neurobiology / Bonhoeffer, MPI of Neurobiology, Max Planck Society;

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Citation

Mullner, F. E., Syed, S., Selvin, P. R., & Sigworth, F. J. (2010). Improved Hidden Markov Models for Molecular Motors, Part 1: Basic Theory. Biophysical Journal, 99(11), 3684-3695.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0012-1F3B-E
Abstract
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