Department of Mathematics, University of Utah, Salt Lake City, UT 84112-0090, United States.
J Theor Biol. 2011 Apr 21;275(1):59-69. doi: 10.1016/j.jtbi.2011.01.031. Epub 2011 Jan 27.
We model the stages of a T cell response from initial activation to T cell expansion and contraction using a system of ordinary differential equations. Results of this modeling suggest that state transitions enable the T cell population to detect change and respond effectively to changes in antigen stimulation levels, rather than simply the presence or absence of antigen. A key component of the system that gives rise to this emergent change detector is initial activation of naïve T cells. The activation step creates a barrier that separates the long-term, slow dynamics of naïve T cells from the short-term, fast dynamics of effector T cells. This separation allows the T cell population to compare current, up-to-date changes in antigen levels to long-term, steady state levels. As a result, the T cell population responds very effectively to sudden shifts in antigen levels, even if the antigen were already present prior to the change. This feature provides a mechanism for T cells to react to rapidly expanding sources of antigen stimulation, such as viruses, while maintaining tolerance to constant or slowly fluctuating sources of stimulation, such as healthy tissue during growth. In addition to modeling T cell activation, we also formulate a model of the proliferation of effector T cells in response to the consumption of positive growth signal, secreted throughout the T cell response. We discuss how the interaction between T cells and growth signal generates an emergent threshold detector that responds preferentially to large changes in antigen stimulation while ignoring small ones. As a final step, we discuss how the de novo generation of adaptive regulatory T cells during the latter phase of the T cell response creates a negative feedback loop that controls the duration and magnitude of the T cell response. Hence, the immune network continually adjusts to a shifting baseline of (self and non-self) antigens, and responds primarily to abrupt changes in these antigens rather than merely their presence or absence.
我们使用常微分方程系统来模拟 T 细胞反应的各个阶段,从初始激活到 T 细胞扩增和收缩。该模型的结果表明,状态转换使 T 细胞群能够检测到变化,并对抗原刺激水平的变化做出有效反应,而不仅仅是抗原的存在或不存在。使这种新出现的变化检测器出现的系统的一个关键组成部分是初始激活幼稚 T 细胞。激活步骤创建了一个障碍,将幼稚 T 细胞的长期缓慢动力学与效应 T 细胞的短期快速动力学分开。这种分离使 T 细胞群能够将当前的、最新的抗原水平变化与长期的、稳定状态的水平进行比较。因此,T 细胞群对抗原水平的突然变化反应非常迅速,即使抗原在变化之前已经存在。该特性为 T 细胞提供了一种机制,使其能够对迅速扩张的抗原刺激源(如病毒)做出反应,同时保持对恒定或缓慢波动的刺激源(如生长过程中的健康组织)的耐受。除了对 T 细胞激活进行建模之外,我们还构建了一个模型,用于模拟效应 T 细胞在消耗整个 T 细胞反应中分泌的正生长信号时的增殖。我们讨论了 T 细胞与生长信号之间的相互作用如何生成一个新的阈值检测器,该检测器优先响应抗原刺激的大变化,而忽略小变化。最后,我们讨论了在 T 细胞反应的后期阶段新生成的适应性调节性 T 细胞如何产生负反馈回路,从而控制 T 细胞反应的持续时间和幅度。因此,免疫网络不断地对(自身和非自身)抗原的变化基线进行调整,并主要对这些抗原的突然变化做出反应,而不仅仅是它们的存在或不存在。