Modelling regulatory pathways in <i>E. coli</i> from timeseries expression profiles

Abstract

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.