Learning Robust Dynamic Networks in Prokaryotes by Gene Expression Networks Iterative Explorer (GENIE)

Abstract

Genetic and genomic approaches have been used successfully to assign genes to distinct regulatory networks, but the uncertainty concerning the connections between genes, the ambiguity inherent to the biological processes, and the impossibility of experimentally determining the underlying biological properties only allow a rough prediction of the dynamics of genes. Here we describe the GENIE methodology that formulates alternative models of genetic regulatory networks based on the available literature and transcription factor binding site evidence. It also provides a framework for the analysis of these models optimized by genetic algorithms, inferring their optimal parameters, simulating their behavior, evaluating them by integrating robustness, realness and flexibility criteria, and contrasting the predictions to experimentally results obtained by Gene Fluorescence Protein analysis. The application of this method to the regulatory network of the bacterium Salmonella enterica uncovered new mechanisms that enable the inter-connection of the PhoP/PhoQ and the PmrA/PmrB two component systems. The predictions were experimentally verified to establish that both transcriptional and post-transcriptional mechanisms are employed to connect these two systems.