Universal algorithm for identification of fractional Brownian motion. A case of telomere subdiffusion.

ABSTRACT: We present a systematic statistical analysis of the recently measured individual trajectories of fluorescently labeled telomeres in the nucleus of living human cells. The experiments were performed in the U2OS cancer cell line. We propose an algorithm for identification of the telomere motion. By expanding the previously published data set, we are able to explore the dynamics in six time orders, a task not possible earlier. As a result, we establish a rigorous mathematical characterization of the stochastic process and identify the basic mathematical mechanisms behind the telomere motion. We find that the increments of the motion are stationary, Gaussian, ergodic, and even more chaotic--mixing. Moreover, the obtained memory parameter estimates, as well as the ensemble average mean square d

SUBMITTER: Burnecki K 

PROVIDER: S-EPMC3491722 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature