Computational Analysis and Modeling Core

计算分析和建模核心

基本信息

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
项目类别：
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

项目摘要

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 – 总结计算分析和建模核心 C 的目标是与可以阐明决定保护的多个相互关联的变量的项目、计算方法针对每个病毒家族，提供基于统计、经过实验验证的计算策略优化免疫治疗活性该核心将开发和应用一系列多变量数学。协助项目确定各自研究中的保护相关性的框架，包括总的来说，我们将强调包含多个特征的建模框架。此外，我们将同时考虑如何解释或预测这些反应。多个变量相互作用以及这些相互作用对确定保护相关性的影响。 Core C 提供的努力和见解将通过协作整合到实验项目中沿着途径进行互动，类似于我们在之前的出版物中共同所做的事情，免疫学和疫苗学，包括我们的几个联盟项目和核心 C 目标的领导者。 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他们的实验数据在目标 2 和 3 中，我们将采用可能更有效的新方法来解决问题。这些项目产生的重要问题无法通过传统方法轻易解决。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，以促进保护相关性的转换物种。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 3 中，大量努力将针对优化泛丝状病毒和泛丝状病毒。甲病毒单克隆抗体并基于系统血清学获得有关作用机制的见解 mAb 特性的实验测量，例如：与多种甲病毒的结合；状态；与同源受体结合；以及引发免疫细胞效应功能。期望从数十个分析的特征中选择一个特征来预测 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，定义有助于保护的特征和有益于治疗用途的 Fc 修饰，以及分析不同动物模型的比较效用。