A Dynamic Model of Immune Responses to Antigen Presentation Predicts Different Regions of Tumor or Pathogen Elimination

Abstract

The immune system must discriminate between agents of disease and an organism’s healthy cells. While the identification of an antigen as self/non-self is critically important, the dynamic features of antigen presentation may also determine the immune system’s response. Here, we use a simple mathematical model of immune activation to explore the idea of antigen discrimination through dynamics. We propose that antigen presentation is coupled to two nodes, one regulatory and one effecting the immune response, through an incoherent feedforward loop and repressive feedback. This circuit would allow the immune system to effectively estimate the increase of antigens with respect to time, a key determinant of immune reactivity in vivo. Our model makes the prediction that tumors growing at specific rates evade the immune system, despite the continuous presence of antigens indicating disease, a phenomenon closely related to clinically observed “two-zone tolerance.” Finally, we discuss a plausible biological instantiation of our circuit using combinations of regulatory and effector T cells.

Endocrine autoimmune disease as a fragility of immune-surveillance against hypersecreting mutants

“…Together with its essential mutant-removal function, ASHM poses a risk because it can degrade into autoimmune disease. Onset of autoimmune disease can occur, for example, if there is a change in the tissue that increases antigen presentation (54)(55)(56). Possibilities include tissue damage that causes release of antigen, cell damage that leads to senescence (57), or cell overload that leads to expression of strongly immunogenic variants of the antigen (6,9).…”

“…We present a version of the ASHM model in which T cells killing is according to the level of antigen in a cell relative to the mean antigen level in the tissue. Such mechanisms have been theoretically proposed by Sontag (2017), based on earlier work (117,118).…”

Importance of Feedback and Feedforward Loops to Adaptive Immune Response Modeling

“…Simplified models of tumor immune response considering important regulatory motifs, such as negative feedback and incoherent FFLs, show the models could successfully predict the tumor growth kinetics. [10][11][12] In general, a biological system with a negative or positive feedback can be depicted, as in Figure 1a, in which the input signal X activates A, A activates B, and, finally, B regulates A via a feedback. Figure 1b shows the response kinetics for three different cases: no feedback, positive feedback, and negative feedback.…”

Immune Digital Twin Blueprint: A Comprehensive Mechanistic Model of the Human Immune System

“…However, a single aggregated variable represented all the cytokines secreted by a cell type. Tumor's growth rate was estimated using a simple and abstract model of the immune system interactions with tumor cells (21) . Mechanistic models of CD4+ T cells predicted novel phenotypes and response to different doses of cytokines (6,15) , including signaling pathways regulating differentiation.…”