Deciphering subcellular processes in live imaging datasets via dynamic probabilistic networks.

ABSTRACT:

Motivation

Designing mathematical tools that can formally describe the dynamics of complex intracellular processes remains a challenge. Live cell imaging reveals changes in the cellular states, but current simple approaches extract only minimal information of a static snapshot.

Results

We implemented a novel approach for analyzing organelle behavior in live cell imaging data based on hidden Markov models (HMMs) and showed that it can determine the number and evolution of distinct cellular states involved in a biological process. We analyzed insulin-mediated exocytosis of single Glut4-vesicles, a process critical for blood glucose homeostasis and impaired in type II diabetes, by using total internal reflection fluorescence microscopy (TIRFM). HMM analyses of movie sequences o

SUBMITTER: Letinic K 

PROVIDER: S-EPMC2916721 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature