title: An information-based approach to change-point analysis with applications to biophysics and cell biology.
abstract: This article describes the application of a change-point algorithm to the analysis of stochastic signals in biological systems whose underlying state dynamics consist of transitions between discrete states. Applications of this analysis include molecular-motor stepping, fluorophore bleaching, electrophysiology, particle and cell tracking, detection of copy number variation by sequencing, tethered-particle motion, etc. We present a unified approach to the analysis of processes whose noise can be modeled by Gaussian, Wiener, or Ornstein-Uhlenbeck processes. To fit the model, we exploit explicit, closed-form algebraic expressions for maximum-likelihood estimators of model parameters and estimated information loss of the generalized noise model, which can be computed extremely efficiently. We implement change-point detection using the frequentist information criterion (which, to our knowledge, is a new information criterion). The frequentist information criterion specifies a single, information-based statistical test that is free from ad hoc parameters and requires no prior probability distribution. We demonstrate this information-based approach in the analysis of simulated and experimental tethered-particle-motion data.
authors.affiliation: Departments of Physics, Bioengineering and Microbiology, University of Washington, Seattle, Washington. Electronic address: pwiggins@uw.edu.
authors.forename: Paul A
authors.initials: PA
authors.lastname: Wiggins
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004247:DNA / Q000737:chemistry; D007257:Information Theory; D056968:Lac Repressors / Q000737:chemistry; D016013:Likelihood Functions; D008954:Models, Biological; D009038:Motion; D013269:Stochastic Processes
chemical_list: D056968:Lac Repressors; D004247:DNA