  Advances in OMICS technologies emerged both massive expression data sets and
huge networks modelling the molecular interplay of genes, RNAs, proteins and
metabolites. Network enrichment methods combine these two data types to extract
subnetwork responses from case/control setups. However, no methods exist to
integrate time series data with networks, thus preventing the identification of
time-dependent systems biology responses. We close this gap with Time Course
Network Enrichment (TiCoNE). It combines a new kind of human-augmented
clustering with a novel approach to network enrichment. It finds temporal
expression prototypes that are mapped to a network and investigated for
enriched prototype pairs interacting more often than expected by chance. Such
patterns of temporal subnetwork co-enrichment can be compared between different
conditions. With TiCoNE, we identified the first distinguishing temporal
systems biology profiles in time series gene expression data of human lung
cells after infection with Influenza and Rhino virus. TiCoNE is available
online (https://ticone.compbio.sdu.dk) and as Cytoscape app in the Cytoscape
App Store (http://apps.cytoscape.org/).
