Computational Analysis and Modeling Core

计算分析和建模核心

• 批准号: 10617739
• 负责人: DOUGLAS A LAUFFENBURGER
• 金额: $ 13.4万
• 依托单位: LA JOLLA INSTITUTE FOR IMMUNOLOGY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10617739
• 关键词: Address; Affinity; Algorithms; Alphavirus; Animal Experiments; Animal Model; Animals; Antibodies; Antibody-mediated protection; Binding; Biological; Cells; Characteristics; Clinical Research; Collaborations; Complex; Computer Analysis; Computer Models; Computing Methodologies; Data; Data Analyses; Data Set; Discipline; Effector Cell; Engineering; Experimental Designs; Failure; Family; Filovirus; Goals; Human; Immune; Immune response; Immune system; Immunology; Immunotherapeutic agent; Individual; Infection; Intuition; Knowledge; Laboratories; Machine Learning; Mathematics; Measurement; Methods; Modeling; Modification; Molecular; Monoclonal Antibodies; Outcome; Pre-Clinical Model; Property; Publications; Recording of previous events; Resolution; Serology; Statistical Data Interpretation; Stream; System; Techniques; Therapeutic; Therapeutic Uses; Translating; Translations; Vaccine Design; Vaccines; Viral; Virus; Virus Diseases; Work; comparative; computer science; expectation; experimental analysis; feature selection; glycosylation; insight; neglect; novel; operation; outcome prediction; pathogen; pre-clinical; predicting response; prevent; receptor; success; therapeutic development; translation to humans; translational model; vaccine strategy; vaccinology

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.

核心C：计算分析和建模核心 - 摘要 计算分析和建模核心C的目的是与 项目，可以阐明确定保护的多个相互关联变量的计算方法 针对每个病毒家族，以提供基于统计的，实验验证的计算策略 优化免疫治疗活性。该核心将开发并应用一系列多变量数学 框架以协助项目确定各自研究中的受保护相关性，包括 跨物种的翻译。总体而言，我们将强调建模框架，其中多个功能是 同时考虑解释或预测反应。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受保护相关性的确定的影响。 核心C提供的努力和见解将通过协作整合到实验项目中 沿途的互动类似于我们在桥接的先前出版物中所做的事情 免疫学和疫苗学，包括我们的几个联盟项目和核心领导者。在C核心目标中 1和4，我们将与项目负责人合作，将既定的多变量方法应用于其实验数据 套。在目标2和3中，我们将采用新颖的方法，这些方法可能更强大地解决 这些项目引起的重要问题，传统方法无法轻易解决。 目标1：提供计算支持，以分析项目中生成的复杂多变量数据集。 目标2：提供一个计算建模框架，以促进对预测关系的理解 细胞效应函数和保护的分子特征。 目标3：提供一个计算建模框架，以支持跨保护的保护翻译 物种。 目标4：在统计分析方面为项目提供咨询协助 例如，在项目2和3的第3个项目中，将针对优化泛滥病毒和PAN-的实质性努力 基于系统血清学 实验测量MAB特性，例如：与αα病毒的潜水员阵列结合；糖基化 国家与同源受体结合；和免疫球效应函数的启发。因为没有 期望从分析的数十个中选择的一项功能将预测mab保护， 对这些数据的分析将需要使用多功能的计算模型来帮助建立阈值 保护，定义有助于保护和FC修饰的功能，有益于治疗的使用和 分析不同动物模型的比较效用。