SUMMARY A system-level understanding of the dengue virus (DENV) host relationship, in particular, the network structure and dynamics, can be derived from experimental data with computational analysis across data sets and modeling. The host response to infection is a complex process involving entire networks of RNA transcription, protein signaling, and metabolism that complementarily influence cellular, tissue, and whole organism behaviors. This complexity demands a systems biology approach for understanding immune response, since investigation of single pathways is unlikely to explain changes taking place across the entire network. The Data Analysis and Modeling Core (Core E) will not only perform standard multivariate analyses on each dataset to find reliable biomarkers for differentiating outcomes of infection, but will furthermore integrate them with the full range of public network and pathway data to construct a multiscale, holistic network model of biologically meaningful DENV-host interactions. Because this model is quantitative and mathematically defined, it is well suited for training advanced classifiers that can predict both individualized clinical outcomes with more accuracy than biomarkers alone and novel “key driver” biomolecules that can be validated with ex vivo siRNA screens (Project 3). These data should further inform on the synergy among multiple interrelating molecular pathways and networks that underpin the differences in phenotype between individuals. The scale of our proposed model for DENV is unprecedented, spanning the genomic, transcriptomic, proteomic, intercellular signaling, and immune cell subpopulation levels—and only with this scale of modeling will superior unbiased, data driven models that address the key biological questions in each of the Projects emerge, explaining the diverse subtleties of host response to DENV infection and vaccination that affect clinical outcomes.