title: Dividing organelle tracks into Brownian and motor-driven intervals by variational maximization of the Bayesian evidence.
abstract: Many organelles and vesicles in live cells move in a start-stop manner when observed for ~10 s by optical microscopy. Changes in velocity and directional persistence of such particles are a potentially rich source of insight into the mechanisms leading to the start and stop states. Unbiased assessment of the most probable number of states, the properties of each state, and the most probable state for the particle at each moment can be accomplished by variational Bayesian methods combined with a hidden Markov model and a Gaussian mixture model. Our track analysis method, "vbTRACK", applied this combination of methods to particle velocity v or changes in the direction of travel evaluated from simulated tracks and from tracks of peroxisomes in live cells. When tested with numerical data, vbTRACK reliably determined the number of states, the mean and variance of the velocity or the direction of travel for each state, and the most probable state during each frame. When applied to the tracks of peroxisomes in live cells, some tracks separated into two states, one with high velocity and directionality, the other approximately Brownian. Other tracks of particles in live cells separated into several diffusive states with distinct diffusion constants.
authors.affiliation: Department of Physics, Wake Forest University, Winston-Salem, NC, USA.
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Martin
authors.affiliation: Department of Physics, Wake Forest University, Winston-Salem, NC, USA.|Department of Biochemistry, School of Medicine, Wake Forest University, Winston-Salem, NC, USA.
authors.forename: Amanda M
authors.initials: AM
authors.lastname: Smelser
authors.affiliation: Department of Physics, Wake Forest University, Winston-Salem, NC, USA. gholz@wfu.edu.
authors.forename: George
authors.initials: G
authors.lastname: Holzwarth
mesh_terms: D001499:Bayes Theorem; D001692:Biological Transport; D008962:Models, Theoretical; D009038:Motion; D020675:Peroxisomes / Q000378:metabolism*
keywords: Gaussian mixture model; Hidden Markov model; Mean-squared-displacement; Particle tracking; Variational Bayes