Improved hidden Markov models for molecular motors, part 1: basic theory.
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.
Department of Cellular and Molecular Physiology, Yale University, New Haven, Connecticut, USA.
Fiona E
FE
Müllner
Sheyum
S
Syed
Paul R
PR
Selvin
Fred J
FJ
Sigworth
D000465:Algorithms; D003198:Computer Simulation; D016013:Likelihood Functions; D008390:Markov Chains; D008958:Models, Molecular; D020409:Molecular Motor Proteins / Q000737:chemistry*
D020409:Molecular Motor Proteins