Changes in gene regulation are hypothesized to play a major role in adaptation and evolution of organisms. In recent years, functional genomics approaches have been used to measure different aspects of the gene regulation machinery in single species and extended to multiple species. These functional genomics datasets give us the unique opportunity to develop more comprehensive models of gene regulation. However, doing so requires us to develop novel computational tools that integrate such datasets within one species and across multiple species.
In this talk, I will present computational methods to integrate different types of datasets for the regulatory network of the model organism, Drosophila melanogaster. We show that data integration is key to improved performance and increased coverage of the fly regulatory network.
     I will then describe a multi-species analysis framework, which comprises a (1) novel multi-species clustering algorithm, Arboretum that identifies modules in species across large evolutionary distances, and (2) a set of metrics to examine patterns of conservation and divergence in these modules, as well as the factors that drive divergence. We applied our approach to expression profiles measured in 8 species of Ascomycota fungi under glucose depletion and under heat shock. In both responses, the transcriptomes are captured by five conserved expression modules, however, the degree of gene content conservation in the module was substantially lower in heat shock than glucose depletion, suggesting a stronger conservation of the latter response.  
Our approaches for integrating different types of regulatory datasets, within one organism and across multiple organisms, can lead us to systematically understand the structure, function, and evolution of regulatory networks.