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Advances in OMICS technologies emerged both massive expression data sets andhuge networks modelling the molecular interplay of genes, RNAs, proteins andmetabolites. Network enrichment methods combine these two data types to extractsubnetwork responses from case/control setups. However, no methods exist tointegrate time series data with networks, thus preventing the identification oftime-dependent systems biology responses. We close this gap with Time CourseNetwork Enrichment (TiCoNE). It combines a new kind of human-augmentedclustering with a novel approach to network enrichment. It finds temporalexpression prototypes that are mapped to a network and investigated forenriched prototype pairs interacting more often than expected by chance. Suchpatterns of temporal subnetwork co-enrichment can be compared between differentconditions. With TiCoNE, we identified the first distinguishing temporalsystems biology profiles in time series gene expression data of human lungcells after infection with Influenza and Rhino virus. TiCoNE is availableonline (https://ticone.compbio.sdu.dk) and as Cytoscape app in the CytoscapeApp Store (http://apps.cytoscape.org/).

Elucidation of time-dependent systems biology cell response patterns with time course network enrichment