The objective of the Computational Modeling Core (CMC) is to develop predictive models of viral pathogenesis through iterative computational and experimental approaches. Our Systems Virology program will generate transcriptomic (mRNA & miRNA), proteomic, lipidomic and metabolomic data for multiple host tissue systems over at time course of infection in both human and animal model systems. The CMC will contribute to the program by providing computational expertise for all aspects of experimental design, data processing and predictive modeling. We have a multi-disciplinary team with expertise in bioinformatics, statistics and mechanistic modeling to provide an integrated toolbox of capabilities to support the Core goals. Our program leverages ongoing, established collaborations between the research projects, technology cores, and modelers, as well as substantial existing data with Influenza virus infections. We will utilize these data and our expertise to extend our investigations into the pathogenesis of Ebola and West Nile virus infections. Specifically, the goals for the CMC are: To identify and quantify changes in host response pathways during initiation and progression of viral infection through statistical evaluation of differential expression. ? To compare and contrast pathways activated in host tissues infected by different viral pathogens and specifically identify those involved in pathogenicity. ? To develop mathematical models which quantitatively predict dynamical host-pathogen interactions. To utilize the models to identify novel emergent properties of host-virus interactions that can be manipulated through therapeutic intervention. The renewal of our ongoing program will augment existing data with new data types (miRNA, phosphoproteomics, lipidomics and metabolomics), as well as provide mechanistic modeling of host response pathways and virtual tissue modeling.
The public health impact of this work is very high, as the Computational Modeling Core (CMC) provides a rigorous statistical framework to ensure that the studies are adequately powered and allow for a computational framework to develop predictive models of viral pathogenesis and identify molecular targets for therapeutic intervention.
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