This comprehensive and highly integrated systems biology program seeks to delineate the complex host responses that determine the outcome of infections with potentially lethal viruses. To achieve this goal, cells and mice will be infected with wild-type and mutant influenza A, Ebola, and West Nile viruses to collect a variety of sample sets (these activities will be carried out by two Research Projects for influenza and Ebola or West Nile virus, respectively). These research projects will be led by Dr. Yoshihiro Kawaoka at the University of Wisconsin-Madison and Dr. Michael Diamond at Washington University-St. Louis. A Technical Core led by Dr. Richard Smith (Pacific Northwest National Lab; PNNL) will utilize EMSLs mass spectrometry resources to perform proteomics, lipidomics, and metabolomics profiling, whereas mRNA and miRNA profiling will be out-sourced on a fee-for service basis. In addition, multiple biological datasets, including virological data and data on protein-protein interactions, will be generated. All data will be analyzed by a Computational Modeling Core led by Dr. Katrina Waters (PNNL), which will integrate the diverse datasets and build predictive mechanistic and network models, including virtual lung and liver models. Based on these analyses, the two Research Projects will carry out comprehensive validation studies in vitro and in vivo (i.e., in knock-out mice). OMICs studies (as described above) from virus-infected knock-out and matched wild-type mice will be used for a second round of analysis, thereby achieving the systems biology paradigm of iterative sampling, modeling, and validation. Storage, management, and exchange of the large and diverse datasets, and outreach to the community, will be facilitated by a Data Management and Resources Dissemination Core led by Dr. Miron Livny at UW-Madison, whereas an Administrative Core led by Dr. Kawaoka will ensure that all administrative tasks are addressed. In summary, we propose a comprehensive and interactive systems biology program that will enhance predictive modeling of infectious disease and identify critical regulators of severe human virus pathogenicity that may be exploited for the development of therapeutic interventions.