Learning of regulatory modules and predictive models of global transcriptional dynamics: application to the extremophile Halobacterium NRC-1

From Q-bio

Our system for network inference and modeling consists of three major components: cMonkey (a method for learning co-regulated biclusters and pathways), the Inferelator (regulatory network inference, regulatory networks modeled as large systems of ODEs). We describe our application of these methods to Halobacterium and several other model organisms. This effort represents one of the first coordinated functional genomics effort in archaea and in particular, under hypersaline conditions. The Inferelator infers regulatory influences for genes and/or gene clusters from mRNA and/or protein expression levels. The procedure models transcriptional networks as large sets of coupled ODEs and can simultaneously model equilibrium and time-course expression levels, such that both kinetic and equilibrium expression levels may be predicted by the resulting models (or used to learn/parameterize regulatory network models). Through the explicit inclusion of time, and gene-knockout information, the method is capable of learning causal relationships. It also includes a novel solution to the problem of encoding interactions between predictors. We discuss the results from an initial application of this method to the halophilic archaeon, Halobacterium NRC-1. We have found the network to be predictive of 150 newly collected microarray datasets and have also validated parts of the network using ChIP-chip. This network offers a means of deciphering how this intensely tough organism maintains homeostasis and responds to wide varieties of metabolic, genetic and environmental states.