Dynamics and Evolution of Virus and Immune Response Networks

这项研究开发了新的数学和计算方法来理解进化病毒（如艾滋病毒）和免疫系统之间的竞争，免疫系统试图根除它们。受感染宿主体内病毒和免疫反应的动态可以看作是一个复杂的生态系统。在艾滋病毒感染期间，大量免疫细胞靶向该病毒，而艾滋病毒可以迅速进化出对多种免疫反应的抗性。随后的战斗沉淀了相互作用的病毒株和免疫反应变体的动态网络。了解形成病毒耐药途径和免疫动力学的主要因素对于设计有效的疫苗和免疫疗法至关重要。数学建模可以帮助阐明病毒逃避免疫攻击的模式，然而，整个系统的复杂性挑战了这种模型的分析。该项目开发数学模型和分析技术，以了解感染宿主内多种病毒和免疫反应群体的动态。本研究使用动态系统和计算方法来表征进化中的病毒免疫反应网络的持久结构。将利用数学模型调查潜在的艾滋病毒疫苗和治疗策略。此外，这些模型将与生物学家合作连接到猴免疫缺陷病毒（SIV）数据。同样重要的是本研究项目中研究生和本科生的跨学科培训所产生的教育影响。快速进化的病原体，如SIV和HIV，可以通过几个靶向表位（病毒蛋白）的突变来逃避宿主的免疫反应。多种病毒和免疫群体的同时相互作用需要考虑一个复杂的系统，以便了解病毒在多个表位上的逃逸。该项目开发并分析了病毒和免疫反应变体在网络中相互作用的数学模型。病毒/免疫群体的稳定性和持久性将根据阈值数量和潜在相互作用网络的结构来表征。一个重要的问题是确定是什么机制导致病毒在大多数药物初始HIV感染患者中赢得这场战斗，或者是什么导致了一小部分被称为精英控制者的药物初始HIV感染个体的长期病毒控制。微分方程模型和随机对应模型的结果将为病毒在多个表位上逃避免疫反应以及更复杂的病毒-抗体共同进化的情况提供信息。从更广泛的角度来看，这些方法将适用于其他捕食者-猎物生态网络，例如噬菌体-细菌群落。将研究与HIV生物学相关的扩展，例如在感染细胞生命周期中纳入异质免疫识别动力学，潜伏细胞感染和不同的疫苗/免疫治疗策略。结果将与数据联系起来，特别是通过跨学科合作，将模型与来自SIV感染实验的病毒基因组和免疫细胞数据联系起来。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This research develops new mathematical and computational methods to understand the competition between evolving viruses (like HIV) and the immune system, which seeks to eradicate them. The dynamics of virus and immune response within an infected host can be viewed as a complex ecosystem. During HIV infection, an extensive repertoire of immune cells target the virus, while HIV can rapidly evolve resistance to multiple immune responses. The ensuing battle precipitates a dynamic network of interacting viral strains and immune response variants. Understanding the main factors shaping viral resistance pathways and immune dynamics is crucial for designing effective vaccines and immunotherapies. Mathematical modeling can help elucidate patterns of viral escape from immune attack, however overall system complexity challenges the analysis of such models. This project develops mathematical models and analytical techniques for understanding the dynamics of multiple virus and immune response populations within infected hosts. This research uses dynamical systems and computational methods to characterize persistent structures of the evolving virus-immune response network. Potential HIV vaccine and treatment strategies will be investigated utilizing the mathematical models. Furthermore, the models will be connected to Simian Immunodeficiency Virus (SIV) data in collaboration with biologists. Also significant are the educational impacts arising from the interdisciplinary training of graduate and undergraduate students in this research project.Rapidly evolving pathogens, such as SIV and HIV, can evade the host immune responses via mutations at several targeted epitopes (viral proteins). The concurrent interaction of diverse virus and immune populations necessitate considering a complex system in order to understand viral escape at multiple epitopes. This project develops and analyzes mathematical models for virus and immune response variants interacting in a network. Stability and persistence of viral/immune populations will be characterized in terms of threshold quantities and the structure of the underlying interaction network. An important question is to determine what mechanisms cause the virus to win this battle in most drug-naive HIV infected patients, and alternatively what leads to long-term viral control in a small percentage of drug-naive HIV infected individuals known as Elite Controllers. Results from the differential equation models and stochastic counterparts will inform upon viral escape of immune response at multiple epitopes, along with the more complex scenario of virus-antibody coevolution. From a broader perspective, the methods will be applicable to other predator-prey ecological networks, for example phage-bacteria communities. Extensions relevant to HIV biology, such as incorporating heterogeneous immune recognition kinetics during the infected cell lifecycle, latent cell infection and different vaccine/immunotherapy strategies, will be investigated. Results will be linked to data, in particular through interdisciplinary collaboration connecting the models with viral genomic and immune cell data from SIV infection experiments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.