Functional Switching and Stability of Regulatory T Cells

Wilson, Shelby; Levy, Doron

doi:10.1007/s11538-013-9875-9

Cited by 10 publications

(2 citation statements)

References 45 publications

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“…It has been found in [37] that recent experimental studies on the primary T cell response to acute infection suggest that a portion of adaptive regulatory T cells support the expansion dynamics of CD4+ T cells when there is a lack of cytokine IL-2. Interestingly, this mechanism is modelled by a saturation function which yields a step-like change in the magnitude of this support mechanism in the days following infection.…”

Section: Discussionmentioning

confidence: 99%

Modelling and Simulation of the Dynamics of the Antigen-Specific T Cell Response Using Variable Structure Control Theory

Anelone

Spurgeon

2016

PLoS ONE

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Experimental and mathematical studies in immunology have revealed that the dynamics of the programmed T cell response to vigorous infection can be conveniently modelled using a sigmoidal or a discontinuous immune response function. This paper hypothesizes strong synergies between this existing work and the dynamical behaviour of engineering systems with a variable structure control (VSC) law. These findings motivate the interpretation of the immune system as a variable structure control system. It is shown that dynamical properties as well as conditions to analytically assess the transition from health to disease can be developed for the specific T cell response from the theory of variable structure control. In particular, it is shown that the robustness properties of the specific T cell response as observed in experiments can be explained analytically using a VSC perspective. Further, the predictive capacity of the VSC framework to determine the T cell help required to overcome chronic Lymphocytic Choriomeningitis Virus (LCMV) infection is demonstrated. The findings demonstrate that studying the immune system using variable structure control theory provides a new framework for evaluating immunological dynamics and experimental observations. A modelling and simulation tool results with predictive capacity to determine how to modify the immune response to achieve healthy outcomes which may have application in drug development and vaccine design.

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Section: Discussionmentioning

confidence: 99%

Modelling and Simulation of the Dynamics of the Antigen-Specific T Cell Response Using Variable Structure Control Theory

Anelone

Spurgeon

2016

PLoS ONE

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“…In the present paper T cells with nearly complementary TCR to self-MHC-peptide complexes are designated as regulatory T cells, Treg cells. 47 In particular, the complete repertoire of Treg cells is able to reflect the whole set of self antigens (See Ref. 1 and Fig.…”

Section: A Single T Cell Cannot Discriminate Self and Nonself Only A ...mentioning

confidence: 99%

MiStImm: a simulation tool to compare classical nonsef-centered immune models with a novel self-centered model

Szabados,

Kerepesi,

Bakács

2015

Preprint

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Our main purpose is to compare classical nonself-centered, two-signal theoretical models of the adaptive immune system with a novel, self-centered, one-signal model developed by our research group. Our model hypothesizes that the immune system of a fetus is capable learning the limited set of self antigens but unable to prepare itself for the unlimited variety of nonself antigens. We have built a computational model that simulates the development of the adaptive immune system. For simplicity, we concentrated on humoral immunity and its major components: T cells, B cells, antibodies, interleukins, non-immune self cells, and foreign antigens. Our model is a microscopic one, similar to the interacting particle models of statistical physics and agent-based models in immunology. Furthermore, our model is stochastic: events are considered random and modeled by a continuous time, finite state Markov process, that is, they are controlled by finitely many independent exponential clocks.The simulation begins after conception, develops the immune system from scratch and learns the set of self antigens. The simulation ends several months after birth when a more-or-less stationary state of the immune system has been established. We investigate how the immune system can recognize and fight against a primary infection. We also investigate under what conditions can an immune memory be created that results in a more effective immune response to a repeated infection. The simulations show that our self-centered model is realistic. Moreover, in case of a primary adaptive immune reaction, it can destroy infections more efficiently than a classical nonself-centered model.Predictions of our theoretical model were clinically supported by autoimmune-related adverse events in high-dose immune checkpoint inhibitor immunotherapy trials and also by safe and successful low-dose immune checkpoint inhibitor combination treatment of

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Modeling the Effect of Memory in the Adaptive Immune Response

Wyatt

Levy

2020

Bull Math Biol

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Functional Switching and Stability of Regulatory T Cells

Cited by 10 publications

References 45 publications

Modelling and Simulation of the Dynamics of the Antigen-Specific T Cell Response Using Variable Structure Control Theory

Modelling and Simulation of the Dynamics of the Antigen-Specific T Cell Response Using Variable Structure Control Theory

MiStImm: a simulation tool to compare classical nonsef-centered immune models with a novel self-centered model

Modeling the Effect of Memory in the Adaptive Immune Response

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