Conghua Wang;Yuan Zhang;Tingwen Huang;Zhichun Yang
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引用次数: 0
摘要
在免疫系统中,先天免疫反应和 CD8 T 细胞在抗击病毒病原体方面发挥着至关重要的协同作用。在本文中,我们建立了一个延迟病毒感染模型,以研究支配感染结果的动态机制。该模型将病毒感染分为五种状态:清除、轻度、中度、重度和复发。一个重要发现是,先天免疫反应的减弱造成了一种双稳态状态,使初始抗原负荷和病毒潜伏期的长短成为决定感染是轻度还是重度的切换开关。此外,病毒潜伏期会诱发霍普夫分岔,使轻度感染从稳定状态转变为周期性振荡,从而可能导致反复感染。有趣的是,增强先天性免疫反应不仅能促进 CD8 T 细胞介导的对感染细胞的破坏,还能延缓霍普夫分岔的发生,减少潜伏期的不利影响。这些见解表明,加强先天免疫反应和开发缩短潜伏期的药物是对抗病毒感染的可行策略。
Dynamics and Regulatory Mechanisms of Innate Immunity and CD8 T Cells Synergy in Response to Viral Infections
In immune systems, the innate immune response and CD8 T cells play a crucial synergistic role in combating viral pathogens. In this paper, we develop a delayed viral infection model to investigate the dynamic mechanisms governing infection outcomes. The model categorizes viral infections into five states: clearance, mild, moderate, heavy, and recurrent. A key finding is that diminished innate immune responses create a bistable condition, enabling the initial antigen load and the length of the viral incubation period to act as toggle switches that determine whether infections will be mild or heavy. Furthermore, the viral incubation period induces a Hopf bifurcation, changing mild infections from a stable state to periodic oscillations, potentially leading to recurrent infections. Interestingly, enhancing the innate immune response not only bolsters the CD8 T cell-mediated destruction of infected cells but also delays the onset of the Hopf bifurcation and reduces the adverse effects of incubation periods. These insights suggest that strengthening the innate immune response and developing drugs to shorten the incubation period are viable strategies to combat viral infections.
期刊介绍:
The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.