Computational Analysis and Modeling Core

计算分析和建模核心

• 批准号: 10402340
• 负责人: DOUGLAS A LAUFFENBURGER
• 金额: $ 19.58万
• 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 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10402340
• 关键词: Address; Affinity; Algebra; 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; Consultations; 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; Viral Antibodies; Virus; Virus Diseases; Work; base; comparative; computer science; expectation; experimental analysis; falls; glycosylation; insight; neglect; novel; operation; outcome prediction; pathogen; pre-clinical; predicting response; prevent; receptor; success; therapeutic development; translation to humans; 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：预防性分析和建模核心-摘要 计算分析和建模核心C的目标是与 项目，计算方法，可以阐明多个，相互关联的变量，确定保护 针对每个病毒家族，提供基于统计学的，实验验证的计算策略， 优化免疫活性。该核心将开发和应用一系列多变量数学模型， 框架，以帮助项目在各自的研究中确定保护相关性，包括 跨物种翻译总的来说，我们将强调其中包含多个特性的建模框架。 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确定保护性相关因素的影响。 Core C提供的努力和见解将通过合作整合到实验项目中。 沿着沿着类似于我们在以前的出版物中共同完成的途径进行互动， 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 集.在目标2和3中，我们将采用新的方法，这些方法可能在解决 这些项目产生的重要问题，不能用传统的方法来解决。 目标1：为项目中生成的复杂多变量数据集的分析提供计算支持。 目标2：提供一个计算建模框架，以促进对预测关系的理解 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，以促进跨 物种 目标4：为项目提供统计分析方面的咨询援助 例如，在项目2和项目3中，大量工作将针对优化泛丝状病毒和泛丝状病毒。 甲病毒单克隆抗体和获得有关作用机制的见解，基于系统血清学 mAb特性的实验测量，例如：与多种甲病毒的结合;糖基化 状态;与同源受体的结合;和免疫细胞效应子功能的引发。由于没有 期望从分析的几十个特征中选择的单个特征将预测mAb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修饰的特征，和 分析不同动物模型的比较效用。