Computational Analysis and Modeling Core

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CORE C: COMPUTATIONAL ANALYSIS & MODELING CORE – SUMMARY The objective of the Computational Analysis & Modeling Core C is to deploy, in close partnership with the Projects, computational methods that can elucidate the multiple, interrelated variables that determine protection against each virus family, to provide statistically based, experimentally validated computational strategies for optimizing immunotherapeutic activity. This Core will develop and apply a spectrum of multi-variate mathematical frameworks to assist the Projects in ascertaining protective correlates in their respective studies, including toward translation across species. Overall, we will emphasize modeling frameworks in which multiple features are considered concomitantly for explanation or prediction of responses. In addition, we will evaluate how these multiple variables interact and the effect of these interactions on determination of the protective correlates. The efforts and insight provided by Core C will be integrated into the experimental Projects via collaborative interactions along avenues analogous to what we have done collectively in prior publications that bridge immunology and vaccinology, including with several of our consortium Project and Core leaders. In Core C Aims 1 and 4, we will work with Project leaders to apply established multi-variate methods to their experimental data sets. In Aims 2 and 3, we will employ novel methods that are likely to be even more powerful in addressing important questions arising from these Projects, which cannot be readily addressed by conventional approaches. Aim 1: To provide computational support for analysis of complex multi-variate data sets generated in the Projects. Aim 2: To provide a computational modeling framework for facilitating understanding of predictive relationships of molecular features to cellular effector functions and protection. Aim 3: To provide a computational modeling framework for facilitating translation of protection correlates across species. Aim 4: To provide consultative assistance to the Projects with respect to statistical analyses As examples, in Projects 2 and 3 substantial efforts will be directed toward optimizing pan-filovirus and pan- alphavirus mAbs and gaining insights concerning mechanisms of action, based on systems serology experimental measurements of mAb properties such as: binding to a diverse array of alphaviruses; glycosylation states; binding to cognate receptors; and elicitation of immune cell effector functions. Since there is no expectation that a single feature selected from among the dozens analyzed will be predictive of mAb protection, analysis of these data will require use multi-featured computational models to help establish thresholds of protection, define features that contribute to protection and Fc modifications beneficial for therapeutic use, and analyze comparative utility of different animal models.