The Modeling Core targets the development, validation and re?nement of models to predict pathogen genetic and host immune response and physiological features affecting viral hemorrhagic fever survival and long-term sequelae of Lassa virus (LASV) and Ebola virus (EBOV) infection. Our multidisciplinary team carries expertise across statistical thinking, mathematical modeling, evolutionary biology and computing to leverage sequenc- ing, immunological pro?ling, mobile sensor and clinical data. We provide to the Consortium for Viral Systems Biology Cores and Projects guidance in phylogenetic reconstruction to de?ne evolutionary trajectories and cataloguing LASV and EBOV intra-host variants, genetic association studies mapping host determinants and, importantly, consultation on all statistical aspects of experimental design in the Projects. Our chief innova- tions are three-fold. First, we incorporate viral sequence evolution into predictive survival models through the development of phylogenetic survival analysis to uncover the viral and host genetic determinants of host time-to-event health outcomes while appropriately controlling for shared evolutionary history and incorporat- ing adaptive immunity repertoire development. We integrate large-scale non-omics data into these survival models using advancing computing technology to include time-dependent immunological and physiological features arising from wireless patient monitors and clinical tests. Third, we exploit systems-level prediction evaluation and re?nement for iterative model building with internal validation, biological experimentation and network analysis. The Core will deliver effective analysis tools enabled for real-time and scriptable use in open- source, reproducible research and will marshall both hands-on short-courses and a regular virtual quantitative clinic to catalyze the interactions between modeling and experimentation.
| Saphire, Erica Ollmann; Schendel, Sharon L; Fusco, Marnie L et al. (2018) Systematic Analysis of Monoclonal Antibodies against Ebola Virus GP Defines Features that Contribute to Protection. Cell 174:938-952.e13 |
| Holmes, Edward C; Rambaut, Andrew; Andersen, Kristian G (2018) Pandemics: spend on surveillance, not prediction. Nature 558:180-182 |
| Suchard, Marc A; Lemey, Philippe; Baele, Guy et al. (2018) Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol 4:vey016 |
| Gunn, Bronwyn M; Yu, Wen-Han; Karim, Marcus M et al. (2018) A Role for Fc Function in Therapeutic Monoclonal Antibody-Mediated Protection against Ebola Virus. Cell Host Microbe 24:221-233.e5 |
| Saphire, Erica Ollmann; Schendel, Sharon L; Gunn, Bronwyn M et al. (2018) Antibody-mediated protection against Ebola virus. Nat Immunol 19:1169-1178 |
| Rambaut, Andrew; Drummond, Alexei J; Xie, Dong et al. (2018) Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7. Syst Biol 67:901-904 |
| Siddle, Katherine J; Eromon, Philomena; Barnes, Kayla G et al. (2018) Genomic Analysis of Lassa Virus during an Increase in Cases in Nigeria in 2018. N Engl J Med 379:1745-1753 |
