Computational Analysis and Modeling Core

核心C：计算分析和建模核心-摘要 计算分析与建模核心C的目标是与 项目，可以阐明决定保护的多个相互关联的变量的计算方法 针对每个病毒家族，提供基于统计的、经过实验验证的计算策略 优化免疫治疗活性。这个核心将开发和应用多变量数学的频谱 协助项目在各自研究中确定保护性相关性的框架，包括 跨物种的翻译。总体而言，我们将重点介绍在其中包含多个功能的建模框架 同时被认为是对反应的解释或预测。此外，我们将评估这些 多个变量相互作用，以及这些相互作用对确定保护性相关因素的影响。 Core C提供的努力和洞察力将通过协作整合到实验项目中 与我们在以前的出版物中共同完成的类似于桥梁的途径上的交互 免疫学和疫苗学，包括与我们的几个联盟项目和核心领导人。核心C目标 1和4，我们将与项目负责人合作，将已建立的多变量方法应用于他们的实验数据 布景。在目标2和目标3中，我们将使用新的方法，这些方法可能在解决问题方面更加强大 由这些项目产生的重要问题，这些问题不能通过传统方法轻易解决。 目的1：为项目中产生的复杂多变量数据集的分析提供计算支持。 目标2：提供便于理解预测关系的计算建模框架 分子特征对细胞效应器功能和保护的影响。 目标3：提供一个计算建模框架，以促进保护关联在 物种。 目标4：在统计分析方面向项目提供咨询援助 例如，在项目2和项目3中，将针对优化泛丝状病毒和泛丝状病毒进行大量努力。 基于系统血清学的甲型病毒单抗和对作用机制的深入了解 单抗性质的实验测量，如：与多种甲型病毒的结合；糖基化 状态；与同源受体的结合；免疫细胞效应器功能的激发。因为没有 期望从分析的几十个特征中选择一个特征将预测单抗保护， 对这些数据的分析将需要使用多功能计算模型来帮助建立阈值 保护，定义有助于保护和有益于治疗用途的FC修改的特征，以及 分析不同动物模型的比较效用。

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.