Improved hidden Markov models for molecular motors, part 2: extensions and application to experimental data.

ABSTRACT: Unbiased interpretation of noisy single molecular motor recordings remains a challenging task. To address this issue, we have developed robust algorithms based on hidden Markov models (HMMs) of motor proteins. The basic algorithm, called variable-stepsize HMM (VS-HMM), was introduced in the previous article. It improves on currently available Markov-model based techniques by allowing for arbitrary distributions of step sizes, and shows excellent convergence properties for the characterization of staircase motor timecourses in the presence of large measurement noise. In this article, we extend the VS-HMM framework for better performance with experimental data. The extended algorithm, variable-stepsize integrating-detector HMM (VSI-HMM) better models the data-acquisition process, and account

SUBMITTER: Syed S 

PROVIDER: S-EPMC2998599 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature