A stochastic model of cytotoxic T cell responses

Chao, Dennis L.; Davenport, Miles P.; Forrest, Stephanie; Perelson, Alan S.

doi:10.1016/j.jtbi.2003.12.011

Cited by 92 publications

(93 citation statements)

References 85 publications

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“…This model, described in detail in the Materials and methods section and in [15], generates a set of 30 000 random peptides to represent "self" peptides expressed in the thymus of an organism and a set of T cell clones, each with a single randomly generated TCR, to represent the pre-selection T cell repertoire. Each peptide is presented by one of three different MHC types to form pMHC.…”

Section: Resultsmentioning

confidence: 99%

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The effects of thymic selection on the range of T cell cross‐reactivity

Chao¹,

Davenport²,

Forrest³

et al. 2005

Eur J Immunol

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Based on the results of a computational model of thymic selection, we propose a mechanism that produces the observed wide range of T cell cross-reactivity. The model suggests that the cross-reactivity of a T cell that survives thymic selection is correlated with its affinity for self peptides. In order to survive thymic selection, a T cell with low affinity for all self peptides expressed in the thymus must have high affinity for major histocompatibility complex (MHC), which makes it highly cross-reactive. A T cell with high affinity for any self peptide must have low MHC affinity to survive selection, which makes it highly specific for its cognate peptide. Our model predicts that (1) positive selection reduces by only 17% the number of T cells that can detect any given foreign peptide, even though it eliminates over 95% of pre-selection cells; (2) negative selection decreases the average cross-reactivity of the pre-selection repertoire by fivefold; and (3) T cells responding to foreign peptides similar to self peptides will have a lower average cross-reactivity than cells responding to epitopes dissimilar to self. IntroductionThe ability of an individual T cell to recognize related antigenic peptides is an essential part of the body's defense against mutating pathogens. T cells detect intracellular pathogens by binding to foreign peptides presented by major histocompatibility complex (MHC)-encoded proteins on the surfaces of infected cells or on specialized antigen-presenting cells [1][2][3]. Although T cells are specific to their cognate peptides, they crossreact with many others [4]. It has been estimated that a T cell can detect an average of 10 6 different peptides [5]. However, not all T cells are equally cross-reactive; it has been observed that the number of peptides to which a single T cell can respond varies widely [6].T cell recognition of antigen is determined by binding interactions of T cell receptors (TCR) with peptide-MHC complexes (pMHC). Each T cell expresses thousands of copies of identical TCR that bind with high affinity to their cognate peptide presented by MHC. T cells detect the presence of pathogens when their receptors have sufficiently high affinity for the pMHC on a target cell and remain in contact with pMHC for a time sufficient to generate a stimulatory signal. Because the pMHC presents a single binding target for the TCR [7], both the peptide and the structure of the presenting MHC molecule play a role in determining affinity.TCR are initially generated by V(D)J recombination, with specificities to a wide range of peptides, including self peptides generated from normal proteins produced by healthy cells [8,9]. Most self-reactive T cells are screened out early in their maturation process in the thymus, where they are exposed to a large array of the body's peptides presented on MHC molecules [10]. During positive selection, T cells that have an extremely low avidity to self peptides bound to MHC die by neglect [11][12][13]. It is believed that this process eliminates T cells that ha...

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Section: Resultsmentioning

confidence: 99%

“…We used a computational model of T cell binding and thymic selection that was previously described in [15]. An expanded description of the model's implementation of peptide binding and thymic selection is presented below.…”

Section: Methodsmentioning

confidence: 99%

The effects of thymic selection on the range of T cell cross‐reactivity

Chao¹,

Davenport²,

Forrest³

et al. 2005

Eur J Immunol

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“…We are so far dissatisfied with our attempts to model EBV infection using differential equations (Duca, unpublished) and difference equations (Shapiro, Delgado-Eckert, unpublished). Moreover, there are reasons to mistrust the spatial homogeneity and well-mixed assumptions that underlie continuous models based on ordinary differential equations (ODEs) [10][11][12], despite the success of such models in immunology and virology [13][14][15][16][17][18][19][20][21]. Disease processes are spatially distributed and it is likely that this spatial distribution is critical in determining the course of infection, as has been argued by many, including [22,23].…”

Section: Introductionmentioning

confidence: 99%

A virtual look at Epstein–Barr virus infection: Simulation mechanism

Shapiro

Duca

Lee

et al. 2008

Journal of Theoretical Biology

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Epstein-Barr virus (EBV) is an important human pathogen that establishes a lifelong persistent infection and for which no precise animal model exists. In this paper we describe in detail an agentbased model and computer simulation of EBV infection. Agents representing EBV and sets of B and T lymphocytes move and interact on a three-dimensional grid approximating Waldeyer's ring, together with abstract compartments for lymph and blood. The simulation allows us to explore the development and resolution of virtual infections in a manner not possible in actual human experiments. Specifically, we identify parameters capable of inducing clearance, persistent infection, or death.

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“…The use of computational system biology models in immunology has been very successful and has represented an insightful and essential complement to in-vivo and in-vitro experimental design and interpretation. Indeed computational system biology models of HIV dynamics have proven valuable in understanding the mechanisms of many of the observed features of the progression of the HIV infection, see for example (Celada and Seiden, 1996;Chao et al, 2004;De Boer and Perelson, 1995;Ho et al, 1995;Perelson et al, 1996;Wei et al, 1995;Wiegel and Perelson, 2004;Wodarz and Nowak, 2002).…”

Section: Introductionmentioning

confidence: 99%

An integrated modelling approach for R5–X4 mutation and HAART therapy assessment

2010

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We have modelled the within-patient evolutionary process during HIV infection using different methodologies. New viral strains arise during the course of HIV infection. These multiple strains of the virus are able to use different coreceptors, in particular the CCR5 and the CXCR4 (R5 and X4 phenotypes, respectively) influence the progression of the disease to the AIDS phase. We present a model of HIV early infection and CTLs response which describes the dynamics of R5 quasispecies, specifying the R5 to X4 switch and effects of immune response. We illustrate dynamics of HIV multiple strains in the presence of multidrug HAART therapy. The HAART combined with X4 strain blocker drugs might help to reduce infectivity and lead to slower progression of disease. On the methodology side, our model represents a paradigm of integrating formal methods and mathematical models as a general framework to study HIV multiple strains during disease progression, and will inch towards providing help in selecting among vaccines and drug therapies. The results presented here are one of the rare cases of methodological cross comparison (stochastic and deterministic) and a novel implementation of model checking in therapy validation.

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A stochastic model of cytotoxic T cell responses

Cited by 92 publications

References 85 publications

The effects of thymic selection on the range of T cell cross‐reactivity

The effects of thymic selection on the range of T cell cross‐reactivity

A virtual look at Epstein–Barr virus infection: Simulation mechanism

An integrated modelling approach for R5–X4 mutation and HAART therapy assessment

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