Parameter inference for stochastic single-cell dynamics from lineage tree data.

ABSTRACT:

Background

With the advance of experimental techniques such as time-lapse fluorescence microscopy, the availability of single-cell trajectory data has vastly increased, and so has the demand for computational methods suitable for parameter inference with this type of data. Most of currently available methods treat single-cell trajectories independently, ignoring the mother-daughter relationships and the information provided by the population structure. However, this information is essential if a process of interest happens at cell division, or if it evolves slowly compared to the duration of the cell cycle.

Results

In this work, we propose a Bayesian framework for parameter inference on single-cell time-lapse data from lineage trees. Our method relies on a combination of Seq

SUBMITTER: Kuzmanovska I 

PROVIDER: S-EPMC5406901 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature