Characterizing dynamic changes in the human blood transcriptional network.

ABSTRACT: Gene expression data generated systematically in a given system over multiple time points provides a source of perturbation that can be leveraged to infer causal relationships among genes explaining network changes. Previously, we showed that food intake has a large impact on blood gene expression patterns and that these responses, either in terms of gene expression level or gene-gene connectivity, are strongly associated with metabolic diseases. In this study, we explored which genes drive the changes of gene expression patterns in response to time and food intake. We applied the Granger causality test and the dynamic Bayesian network to gene expression data generated from blood samples collected at multiple time points during the course of a day. The simulation result shows that combinin

SUBMITTER: Zhu J 

PROVIDER: S-EPMC2820517 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature