Novel Hybrid Computational Models to Disentangle Complex Immune Responses

新型混合计算模型可解开复杂的免疫反应

• 批准号: 10794448
• 负责人: Esteban Aberlardo Hernandez Vargas
• 金额: $ 17.18万
• 依托单位: UNIVERSITY OF IDAHO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-26 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10794448
• 关键词: 2019-nCoV; Address; Antigen-Antibody Complex; Biological; Biological Sciences; Biology; Biomedical Research; COVID-19 pandemic; Cell model; Collaborations; Computational algorithm; Computer Models; Computing Methodologies; Dangerousness; Data; Differential Equation; Dimensions; Disease; Educational process of instructing; Equation; Equilibrium; Etiology; Europe; Event; Family; Generations; Hybrids; Idaho; Immune; Immune Targeting; Immune response; Immune system; Immunologic Factors; Immunotherapy; Infection; Influenza; Innate Immune System; Knowledge; Laboratory Finding; Machine Learning; Mathematics; Measures; Mediating; Methods; Modeling; Molecular; Morbidity - disease rate; Mus; Parameter Estimation; Pathology; Play; Predictive Value; Public Health; Public Policy; Rhinovirus; Role; Science; Scientist; Severities; Severity of illness; System; Techniques; Testing; Therapeutic; Time; Training; Universities; Viral; Viral Respiratory Tract Infection; Virus; Virus Diseases; attenuation; biological systems; co-infection; complex biological systems; computational suite; computerized tools; data integration; design; experimental study; fictional works; high dimensionality; high reward; high risk; human disease; immunoregulation; improved; influenza infection; innovation; large datasets; machine learning algorithm; machine learning method; mathematical methods; mathematical model; mathematical sciences; mortality; novel; pandemic influenza; pathogenic virus; predictive modeling; prevent; respiratory; sound; theories

Most quantitative models in biomedical research have been formulated by ordinary differential equations (ODEs). Despite the great contributions ODEs have made to biology and beyond, the high-dimensional, time-dependent factors of the immune system still pose a significant challenge to the predictive value of ODEs as it would require several hundred equations and thousands of parameters to be estimated. The recent rise of machine learning as a powerful computational tool to integrate large datasets presents a special opportunity to deal with the inherent complexity of biological systems. However, machine learning approaches do not consider the mechanistic knowledge of the underlying interactions. Preliminary studies that combine ODEs and machine learning highlight that these computational algorithms could be on the cusp of a major revolution. Remarkably enough, however, no parameter estimation theory exists to integrate simultaneously both approaches. We propose to create new hybrid models and test their predictions in a mouse viral coinfection system to address a central vexation for infection biology which is how and when to modulate immune responses to mitigate mortality during lethal respiratory viral infection. At the interface between mathematical and life sciences, we will develop and analyze a novel suite of computational models that will integrate the underlying biological mechanisms to manage ill-posed problems and explore massive design spaces, allowing for robust predictions from complex biological systems. To validate and test our novel and foundational mathematical approaches, we will generate the biological data from a mouse infection system with a mild viral pathogen (rhinovirus) two days before infection with a lethal viral pathogen (influenza) that results in reduced disease compared to single infection alone. We hypothesize that this system can train our mathematical models in a natural way how the innate immune system can be manipulated to reduce mortality to lethal infections and beyond. Key model predictions will be tested by targeted immune system manipulation during lethal infection, paving the way to understanding the role of complex immune interactions in respiratory viral disease pathology.

生物医学研究中的大多数定量模型都是用常微分方程来表示的 （ODE）。尽管ODE对生物学和其他领域做出了巨大贡献，但高维， 免疫系统的时间依赖性因素仍然对免疫系统的预测价值构成重大挑战 常微分方程，因为它需要几百个方程和数千个参数来估计。的 最近兴起的机器学习作为一种强大的计算工具来整合大型数据集， 处理生物系统固有复杂性的特殊机会。然而，机器学习 这些方法不考虑潜在相互作用的机械知识。初步研究 联合收割机ODE和机器学习突出了这些计算算法可以在 一场重大革命的风口浪尖然而，值得注意的是，没有参数估计理论存在， 同时将两种方法结合起来。我们建议创建新的混合模型，并测试其 在小鼠病毒共感染系统中的预测，以解决感染生物学的中心烦恼， 如何以及何时调节免疫应答以降低致命呼吸道病毒感染期间的死亡率。 在数学和生命科学之间的接口，我们将开发和分析一套新颖的 计算模型，将整合潜在的生物机制，以管理不适定 问题和探索大规模的设计空间，允许从复杂的生物 系统.为了验证和测试我们新颖的基础数学方法，我们将生成 来自两天前具有温和病毒病原体（鼻病毒）的小鼠感染系统的生物学数据 与单一感染相比，感染致命病毒病原体（流感）导致疾病减少 一个人我们假设这个系统可以以自然的方式训练我们的数学模型， 可以操纵免疫系统以降低致命感染和其他感染死亡率。重点型号 在致命感染期间，预测将通过有针对性的免疫系统操作进行测试， 了解复杂的免疫相互作用在呼吸道病毒疾病病理学中的作用。