English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

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

MPS-Authors
/persons/resource/persons39003

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

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Müllner, 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. doi:10.1016/j.bpj.2010.09.067.


Cite as: https://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