Time varying causal network reconstruction of a mouse cell cycle.

ABSTRACT:

Background

Biochemical networks are often described through static or time-averaged measurements of the component macromolecules. Temporal variation in these components plays an important role in both describing the dynamical nature of the network as well as providing insights into causal mechanisms. Few methods exist, specifically for systems with many variables, for analyzing time series data to identify distinct temporal regimes and the corresponding time-varying causal networks and mechanisms.

Results

In this study, we use well-constructed temporal transcriptional measurements in a mammalian cell during a cell cycle, to identify dynamical networks and mechanisms describing the cell cycle. The methods we have used and developed in part deal with Granger causality, Vector

SUBMITTER: Masnadi-Shirazi M 

PROVIDER: S-EPMC6542064 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature