Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processes.

ABSTRACT: Multiple biological processes are driven by oscillatory gene expression at different time scales. Pulsatile dynamics are thought to be widespread, and single-cell live imaging of gene expression has lead to a surge of dynamic, possibly oscillatory, data for different gene networks. However, the regulation of gene expression at the level of an individual cell involves reactions between finite numbers of molecules, and this can result in inherent randomness in expression dynamics, which blurs the boundaries between aperiodic fluctuations and noisy oscillators. This underlies a new challenge to the experimentalist because neither intuition nor pre-existing methods work well for identifying oscillatory activity in noisy biological time series. Thus, there is an acute need for an objective stat

SUBMITTER: Phillips NE 

PROVIDER: S-EPMC5444866 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature