Modelling regulatory pathways in E. coli from time series expression profiles.

Conference Paper

MOTIVATION: Cells continuously reprogram their gene expression network as they move through the cell cycle or sense changes in their environment. In order to understand the regulation of cells, time series expression profiles provide a more complete picture than single time point expression profiles. Few analysis techniques, however, are well suited to modelling such time series data. RESULTS: We describe an approach that naturally handles time series data with the capabilities of modelling causality, feedback loops, and environmental or hidden variables using a Dynamic Bayesian network. We also present a novel way of combining prior biological knowledge and current observations to improve the quality of analysis and to model interactions between sets of genes rather than individual genes. Our approach is evaluated on time series expression data measured in response to physiological changes that affect tryptophan metabolism in E. coli. Results indicate that this approach is capable of finding correlations between sets of related genes.

Full Text

Duke Authors

Cited Authors

  • Ong, IM; Glasner, JD; Page, D

Published Date

  • 2002

Published In

Volume / Issue

  • 18 Suppl 1 /

Start / End Page

  • S241 - S248

PubMed ID

  • 12169553

International Standard Serial Number (ISSN)

  • 1367-4803

Digital Object Identifier (DOI)

  • 10.1093/bioinformatics/18.suppl_1.s241

Conference Location

  • England