Modelling regulatory pathways in E. coli from time series expression profiles.
BACKGROUND
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.
Ong
Irene M
IM
Department of Computer Sciences, Department of Biostatistics & Medical Informatics Department of Genetics, University of Wisconsin, Madison 53706, USA. ong@cs.wisc.edu
Glasner
Jeremy D
JD
Page
David
D
D000222:Adaptation, Physiological / Q000502:physiology; D001499:Bayes Theorem; D004926:Escherichia coli / Q000502:physiology*; D029968:Escherichia coli Proteins / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods*; D015964:Gene Expression Regulation, Bacterial / Q000502:physiology*; D008954:Models, Biological; D015233:Models, Statistical; D015398:Signal Transduction / Q000502:physiology*; D013997:Time Factors
D029968:Escherichia coli Proteins