On the Coupling of Two Models of the Human Immune Response to an Antigen

Quintela, Bárbara de Melo; Santos, Rodrigo Weber dos; Lobosco, Marcelo

doi:10.1155/2014/410457

Cited by 17 publications

(26 citation statements)

References 56 publications

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“…This is an interesting and important challenge from the perspective of mathematical modelling, which has appeared in the context of modelling the dynamics of immune responses to viral infections [78][79][80], as well as in the studies of early stages of outbreaks of infectious diseases modelled at population level [81,82]. There are several methods for addressing this problem, such as including time delays or additional compartments to better represent the initial phase of the immune response [83][84][85][86], or considering spatial aspects of the immune dynamics using such approaches as partial differential equations, cellular automata, or multi-scale methods [17,87,88].…”

Section: Discussionmentioning

confidence: 99%

Quantifying the Role of Stochasticity in the Development of Autoimmune Disease

Nicholson

Blyuss

Fatehi

2020

Cells

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In this paper, we propose and analyse a mathematical model for the onset and development of autoimmune disease, with particular attention to stochastic effects in the dynamics. Stability analysis yields parameter regions associated with normal cell homeostasis, or sustained periodic oscillations. Variance of these oscillations and the effects of stochastic amplification are also explored. Theoretical results are complemented by experiments, in which experimental autoimmune uveoretinitis (EAU) was induced in B10.RIII and C57BL/6 mice. For both cases, we discuss peculiarities of disease development, the levels of variation in T cell populations in a population of genetically identical organisms, as well as a comparison with model outputs.

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Section: Discussionmentioning

confidence: 99%

Quantifying the Role of Stochasticity in the Development of Autoimmune Disease

Nicholson

Blyuss

Fatehi

2020

Cells

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“…The mathematical model presented herein, extends previous studies by taking into account the interconnectivity between cellular and cytokine responses and captures ex vivo and potential in vivo dynamics of the immune response (Quintela et al, 2014;Brady et al, 2016). This study focuses on examining the immune response to acute stimulation by a pathogen, which leads to a cascade of cellular signals recruiting leukocytes, or white blood cells, throughout the body (Hoebe et al, 2004;Anderson et al, 2019;Xue and Falcon, 2019).…”

Section: Introductionmentioning

confidence: 90%

“…The details of the cellular model equations and the coupling from the tissue and nearest lymph node are available in Quintela et al (2014). The novel cellular-cytokine model that is based both on the macrophage activation part of this cellular model and on the inflammatory cytokine model by Brady et al, is described below.…”

Section: Cellular Modelmentioning

confidence: 99%

A Mathematical Model of the Dynamics of Cytokine Expression and Human Immune Cell Activation in Response to the Pathogen Staphylococcus aureus

Talaei

Garan

Quintela

et al. 2021

Front. Cell. Infect. Microbiol.

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Cell-based mathematical models have previously been developed to simulate the immune system in response to pathogens. Mathematical modeling papers which study the human immune response to pathogens have predicted concentrations of a variety of cells, including activated and resting macrophages, plasma cells, and antibodies. This study aims to create a comprehensive mathematical model that can predict cytokine levels in response to a gram-positive bacterium, S. aureus by coupling previous models. To accomplish this, the cytokines Tumor Necrosis Factor Alpha (TNF-α), Interleukin 6 (IL-6), Interleukin 8 (IL-8), and Interleukin 10 (IL-10) are included to quantify the relationship between cytokine release from macrophages and the concentration of the pathogen, S. aureus, ex vivo. Partial differential equations (PDEs) are used to model cellular response and ordinary differential equations (ODEs) are used to model cytokine response, and interactions between both components produce a more robust and more complete systems-level understanding of immune activation. In the coupled cellular and cytokine model outlined in this paper, a low concentration of S. aureus is used to stimulate the measured cellular response and cytokine expression. Results show that our cellular activation and cytokine expression model characterizing septic conditions can predict ex vivo mechanisms in response to gram-negative and gram-positive bacteria. Our simulations provide new insights into how the human immune system responds to infections from different pathogens. Novel applications of these insights help in the development of more powerful tools and protocols in infection biology.

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“…A large number of works use mathematical and computational tools to model the Human Immune System (HIS) using distinct techniques, such as ordinary differential equations (ODEs) ( Perelson, 1989 ; Baker et al, 1997 ; Chang et al, 2005 ; Vodovotz et al, 2006 ; Jarrett et al, 2015 ; Bonin et al, 2016 ), partial differential equations (PDEs) ( Pettet et al, 1996 ; Su et al, 2009 ; Flegg et al, 2012 ; Pigozzo et al, 2013 ; Quintela et al, 2014 ), stochastic methods ( Chao et al, 2004 ; Xavier et al, 2017 ), cellular automaton and agents ( Celada and Seiden, 1992 ; Morpurgo et al, 1995 ; Kohler et al, 2000 ; Bernaschi and Castiglione, 2001 ; Pappalardo et al, 2018 ). On the other hand, very few papers were published describing the dynamics of SARS-CoV-2 ( Almocera et al, 2020 ; Du and Yuan, 2020 ; Hernandez-Vargas and Velasco-Hernandez, 2020 ; Xavier et al, 2020 ), although some additional non-peer-reviewed papers can also be found on the internet.…”

Section: Related Workmentioning

confidence: 99%

A Validated Mathematical Model of the Cytokine Release Syndrome in Severe COVID-19

Reis

Pigozzo

Bonin³

et al. 2021

Front. Mol. Biosci.

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By June 2021, a new contagious disease, the Coronavirus disease 2019 (COVID-19), has infected more than 172 million people worldwide, causing more than 3.7 million deaths. Many aspects related to the interactions of the disease’s causative agent, SAR2-CoV-2, and the immune response are not well understood: the multiscale interactions among the various components of the human immune system and the pathogen are very complex. Mathematical and computational tools can help researchers to answer these open questions about the disease. In this work, we present a system of fifteen ordinary differential equations that models the immune response to SARS-CoV-2. The model is used to investigate the hypothesis that the SARS-CoV-2 infects immune cells and, for this reason, induces high-level productions of inflammatory cytokines. Simulation results support this hypothesis and further explain why survivors have lower levels of cytokines levels than non-survivors.

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On the Coupling of Two Models of the Human Immune Response to an Antigen

Cited by 17 publications

References 56 publications

Quantifying the Role of Stochasticity in the Development of Autoimmune Disease

Quantifying the Role of Stochasticity in the Development of Autoimmune Disease

A Mathematical Model of the Dynamics of Cytokine Expression and Human Immune Cell Activation in Response to the Pathogen Staphylococcus aureus

A Validated Mathematical Model of the Cytokine Release Syndrome in Severe COVID-19

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