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