scholarly journals A qualitative mathematical model of immunocompetence with applications to SARS-CoV-2 immunity

2021 ◽  
Author(s):  
Javier Burgos-Salcedo
Keyword(s):  
Mathematical Model ◽  
Immune Response ◽  
Immune System ◽  
Random Graphs ◽  
Practical Method ◽  
Higher Order ◽  
Effector Cells ◽  
Emergent Structure

A qualitative mathematical model of the notion of immunocompetence is developed, based on the formalism of Memory Evolutive Systems (MES), from which, immunocompetence is defined as an emergent structure of a higher order arising from the signal networks that are established between effector cells and molecules of the immune response in the presence of a given antigen. In addition, a possible mechanism of functorial nature is proposed, which may explain how immunocompetence is achieved in an organism endowed with innate and adaptive components of its immune system. Finally, a practical method to measure the immunocompetence status is established, using elements of the theory of small random graphs and taking into account the characteristics of the immune networks, established through transcriptional studies, of patients with severe COVID-19 and healthy patients, assuming that both types of patients were vaccinated with an effective biological against SARS-CoV-2.

scholarly journals Mathematical modelling of trastuzumab-induced immune response in an in vivo murine model of HER2+ breast cancer

2018 ◽  
Vol 36 (3) ◽  
pp. 381-410 ◽  
Author(s):  
Angela M Jarrett ◽  
Meghan J Bloom ◽  
Wesley Godfrey ◽  
Anum K Syed ◽  
David A Ekrut ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Experimental Data ◽  
Mathematical Model ◽  
Immune Response ◽  
Immune System ◽  
Murine Model ◽  
Tumour Growth ◽  
Experimental Approach ◽  
Her2 Breast Cancer ◽  
The Mathematical Model

Abstract The goal of this study is to develop an integrated, mathematical–experimental approach for understanding the interactions between the immune system and the effects of trastuzumab on breast cancer that overexpresses the human epidermal growth factor receptor 2 (HER2+). A system of coupled, ordinary differential equations was constructed to describe the temporal changes in tumour growth, along with intratumoural changes in the immune response, vascularity, necrosis and hypoxia. The mathematical model is calibrated with serially acquired experimental data of tumour volume, vascularity, necrosis and hypoxia obtained from either imaging or histology from a murine model of HER2+ breast cancer. Sensitivity analysis shows that model components are sensitive for 12 of 13 parameters, but accounting for uncertainty in the parameter values, model simulations still agree with the experimental data. Given theinitial conditions, the mathematical model predicts an increase in the immune infiltrates over time in the treated animals. Immunofluorescent staining results are presented that validate this prediction by showing an increased co-staining of CD11c and F4/80 (proteins expressed by dendritic cells and/or macrophages) in the total tissue for the treated tumours compared to the controls ($p < 0.03$). We posit that the proposed mathematical–experimental approach can be used to elucidate driving interactions between the trastuzumab-induced responses in the tumour and the immune system that drive the stabilization of vasculature while simultaneously decreasing tumour growth—conclusions revealed by the mathematical model that were not deducible from the experimental data alone.

Cellular side of acquired immunity (T cells)

2013 ◽  
Author(s):  
Assia Eljaafari ◽  
Pierre Miossec
Keyword(s):  
Immune Response ◽  
T Cells ◽  
Immune System ◽  
Disease Process ◽  
T Cell Response ◽  
Th2 Cells ◽  
Cell Mediated Immunity ◽  
Immune Effector ◽  
Effector Cells ◽  
Th22 Cells

The adaptive T-cell response represents the most sophisticated component of the immune response. Foreign invaders are recognized first by cells of the innate immune system. This leads to a rapid and non-specific inflammatory response, followed by induction of the adaptive and specific immune response. Different adaptive responses can be promoted, depending on the predominant effector cells that are involved, which themselves depend on the microbial/antigen stimuli. As examples, Th1 cells contribute to cell-mediated immunity against intracellular pathogens, Th2 cells protect against parasites, and Th17 cells act against extracellular bacteria and fungi that are not cleared by Th1 and Th2 cells. Among the new subsets, Th22 cells protect against disruption of epithelial layers secondary to invading pathogens. Finally these effector subsets are regulated by regulatory T cells. These T helper subsets counteract each other to maintain the homeostasis of the immune system, but this balance can be easily disrupted, leading to chronic inflammation or autoimmune diseases. The challenge is to detect early changes in this balance, prior to its clinical expression. New molecular tools such as microarrays could be used to determine the predominant profile of the immune effector cells involved in a disease process. Such understanding should provide better therapeutic tools to counteract deregulated effector cells.

scholarly journals Immune Response in Thyroid Cancer: Widening the Boundaries

Scientifica ◽  
2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Laura Sterian Ward
Keyword(s):  
Immune Response ◽  
Thyroid Cancer ◽  
Immune System ◽  
Cancer Progression ◽  
Epidemiological Data ◽  
Future Trend ◽  
Innate Immune Responses ◽  
Adaptive Responses ◽  
Effector Cells ◽  
Thyroid Cancer Cells

The association between thyroid cancer and thyroid inflammation has been repeatedly reported and highly debated in the literature. In fact, both molecular and epidemiological data suggest that these diseases are closely related and this association reinforces that the immune system is important for thyroid cancer progression. Innate immunity is the first line of defensive response. Unlike innate immune responses, adaptive responses are highly specific to the particular antigen that induced them. Both branches of the immune system may interact in antitumor immune response. Major effector cells of the immune system that directly target thyroid cancer cells include dendritic cells, macrophages, polymorphonuclear leukocytes, mast cells, and lymphocytes. A mixture of immune cells may infiltrate thyroid cancer microenvironment and the balance of protumor and antitumor activity of these cells may be associated with prognosis. Herein, we describe some evidences that immune response may be important for thyroid cancer progression and may help us identify more aggressive tumors, sparing the vast majority of patients from costly unnecessary invasive procedures. The future trend in thyroid cancer is an individualized therapy.

scholarly journals A mathematical model for understanding how lymph node architecture scales with host body size

2019 ◽  
Author(s):  
soumya banerjee
Keyword(s):  
Mathematical Model ◽  
Immune Response ◽  
Lymph Node ◽  
Immune System ◽  
Lymph Nodes ◽  
Optimal Distribution ◽  
Host Animal ◽  
Anatomical Structures ◽  
Node Architecture ◽  
Host Body Size

The immune system can detect and respond against pathogens in time that does not varywith the size of the host animal. We suggest that this is due to the architecture of lymphnodes. Lymph nodes are anatomical structures that facilitate the otherwise serendipitousencounter of immune system cells with pathogens. We develop two complementarymathematical approaches to derive the optimal distribution of lymph nodes that enable arapid immune response. Our work gives insights into the optimal design and architectureof the immune system and provides valuable inspiration for designing efficientcomputing systems.

A MATHEMATICAL MODEL OF IMMUNE RESPONSE TO TUMOR INVASION INCORPORATED WITH DANGER MODEL

2015 ◽  
Vol 23 (03) ◽  
pp. 505-526 ◽  
Author(s):  
XUEFANG LI ◽  
JIAN-XIN XU
Keyword(s):  
Mathematical Model ◽  
Immune Response ◽  
Immune System ◽  
Tumor Cells ◽  
Tumor Invasion ◽  
Interleukin 12 ◽  
Specific System ◽  
Control Tumor Growth ◽  
Control Tumor ◽  
Broad Understanding

In this paper, a new mathematical model of the interactions between a growing tumor and an immune system is presented by incorporating the danger model. The populations involved are tumor cells, CD8+T-cells, natural killer cells (NK-cells), dendritic cells (DCs) and cytokine interleukin-12 (IL-12). A key feature of this work is the inclusion of the danger model into the dynamics of the immune system, which is rarely considered by previous works. Regarding the constructed mathematical model, both the location of equilibria and their stability properties are discussed, which are useful not only to gain a broad understanding of the specific system dynamics, but also to help guide the development of therapies. Moreover, numerical simulations of the system with chemotherapy and immunotherapy by using specific parameters are presented to illustrate that proper therapy is able to eliminate the entire tumor. In addition, we illustrate cases for which neither chemotherapy nor immunotherapy alone are able to control tumor growth, but a combination treatment is sufficient to eliminate the tumor cells.

scholarly journals CHARACTERISTICS OF CELLULAR COMPARTMENT CHANGES OF IMMUNE SYSTEM IN THE PATIENTS WITH CHRONIC POLYPOUS RHINOSINUSITIS DEPEND ON EFFICIENCY OF DRUG THERAPY

2019 ◽  
Vol 21 (4) ◽  
pp. 715-724
Author(s):  
E. L. Savlevich ◽  
A. V. Zurochka ◽  
S. V. Khaidukov
Keyword(s):  
Immune Response ◽  
Immune System ◽  
Drug Therapy ◽  
T Lymphocytes ◽  
Cytotoxic T Lymphocytes ◽  
Nasal Polyps ◽  
Inflammatory Process ◽  
Nkt Cells ◽  
Absolute Number ◽  
Effector Cells

Despite numerous attempts to control the course of chronic rhinosinusitis with nasal polyps (CRSwNP) by means of pharmacological treatment and new surgical approaches, the majority of patients experience lifelong persistence of this disorder, at recurrence rates of 50-60% within 18 months after surgical treatment. Since CRSwNP is a chronic persistent inflammatory process, it affects the entire body condition, including the state of systemic immune response. An elevation of NK (CD3-CD16+CD56+), activated NK (CD8+CD3-), NKT cells (CD16+CD56+CD3+), Treg (CD4+CD25brightCD127low to neg) cells and activated T-lymphocytes (CD3+CD25+) was revealed elsewhere among all the patients with CRSwNP, using a flow ytometry method. There was no difference between various disease phenotypes. We analyzed the status of cellular component of systemic immunity, dependent on clinical course of the disease and efficiency of the administered therapy of CRSwNP. The patients were divided into three subgroups. The follow-up period was 1 year. The first group comprised the patients who showed positive dynamics after conservative therapy, resulting into regression of nasal polyps and their grade than a year ago. The second group included the patients in whom the size of polyps remained the same. The third group included the patients with higher incidence of nasal polyps than a year ago.We have shown a decrease of Treg, NKT cells, NK and activated NK, cytotoxic T-lymphocytes (CD3+CD8+), activated T-cell numbers in clinical group 3 with aggressive growth of polyps and low effect of standard therapy, which may cause deterioration of the immune system cellular populations, accompanied by presence of persistent productive inflammatory process of nasal cavity and paranasal sinuses. In the second group, a significant elevation of total lymphocyte number, total and activated T cells, T helpers (CD3+CD4+), cytotoxic T lymphocytes, NK and NKT cells was shown. Meanwhile, a decrease in absolute number of activated NK was observed despite the NK growth. Therefore, we can assume that the mechanism of their activation was disturbed and compensated by production of NKT cells and cytotoxic T lymphocytes. Moreover, we have shown in this group that the absolute number of Treg cells is increased; and these cells had a suppressive influence on effector cells of adaptive immune response, thus inducing incomplete elimination of infectious agents, which contribute to permanent incomplete course of inflammatory process. Chronic inflammatory process in CRSwNP affects systemic cellular immunity depending on the morbidity characteristics in the course of pathological process. The maximal intensity of systemic cellular immunity is observed in the group of patients that require permanent basic drug therapy. In case of aggressive CRSwNP and failure of standard drug therapy, we observed a decrease in absolute numbers of effector cells, along with decreased Treg lymphocyte numbers which may explain inefficient immune regulation of inflammatory process and medical interventions in this group of patients.

Cellular side of acquired immunity (T cells)

2013 ◽  
pp. 365-370
Author(s):  
Assia Eljaafari ◽  
Pierre Miossec
Keyword(s):  
Immune Response ◽  
T Cells ◽  
Immune System ◽  
T Cell Response ◽  
Th2 Cells ◽  
Cell Mediated Immunity ◽  
Effector Cells ◽  
Therapeutic Tools ◽  
Bacteria And Fungi ◽  
Epithelial Layers

The adaptive T-cell response represents the most sophisticated component of the immune response. Foreign invaders are recognized first by cells of the innate immune system. This leads to a rapid and non-specific inflammatory response, followed by induction of the adaptive and specific immune response. Different adaptive responses can be promoted, depending on the predominant effector cells that are involved, which themselves depend on the microbial/antigen stimuli. As examples, Th1 cells contribute to cell-mediated immunity against intracellular pathogens, Th2 cells protect against parasites, and Th17 cells act against extracellular bacteria and fungi that are not cleared by Th1 and Th2 cells. Among the new subsets, Th22 cells protect against disruption of epithelial layers secondary to invading pathogens. Finally these effector subsets are regulated by regulatory T cells. These T helper subsets counteract each other to maintain the homeostasis of the immune system, but this balance can be easily disrupted, leading to chronic inflammation or autoimmune diseases. The challenge is to detect early changes in this balance, prior to its clinical expression. New molecular tools such as microarrays could be used to determine the predominant profile of the immune effector cells involved in a disease process. Such understanding should provide better therapeutic tools to counteract deregulated effector cells.

A MATHEMATICAL MODEL FOR THE EFFECT OF TOXICANT ON THE IMMUNE SYSTEM

2007 ◽  
Vol 15 (04) ◽  
pp. 473-493 ◽  
Author(s):  
UMA S. DUBEY ◽  
BALRAM DUBEY
Keyword(s):  
Mathematical Model ◽  
Immune Response ◽  
Immune System ◽  
Local Stability ◽  
Defense Mechanism ◽  
Oscillatory Behavior ◽  
The Body ◽  
Nonlinear Mathematical Model ◽  
Environmental Toxicant ◽  
Equilibrium Level

In this paper, a nonlinear mathematical model is proposed and analyzed to study the effect of environmental toxicant on the immune response of the body. Criteria for local stability, instability and global stability are obtained. It is shown that the immune response of the body decreases as the concentration of environmental toxicant increases, and certain criteria are obtained under which it settles down at its equilibrium level. In the absence of toxicant, an oscillatory behavior of immune system and pathogenic growth is observed. However, in the presence of toxicant, oscillatory behavior is not observed. These studies show that the toxicant may have a grave effect on our body's defense mechanism.

scholarly journals Mathematical Model of Antiviral Immune Response against the COVID-19 Virus

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1356
Author(s):  
Juan Carlos Chimal-Eguia
Keyword(s):  
Mathematical Model ◽  
Immune Response ◽  
Immune System ◽  
Adaptive Immune Response ◽  
Humoral Response ◽  
Antiviral Immune Response ◽  
Infection Dynamics ◽  
Current Outbreak ◽  
Delay Differential

This work presents a mathematical model to investigate the current outbreak of the coronavirus disease 2019 (COVID-19) worldwide. The model presents the infection dynamics and emphasizes the role of the immune system: both the humoral response as well as the adaptive immune response. We built a mathematical model of delay differential equations describing a simplified view of the mechanism between the COVID-19 virus infection and the immune system. We conduct an analysis of the model exploring different scenarios, and our numerical results indicate that some theoretical immunotherapies are successful in eradicating the COVID-19 virus.

scholarly journals Modeling Dendritic Cell Pulsed Immunotherapy for Mice with Melanoma—Protocols for Success and Recurrence

2021 ◽  
Vol 11 (7) ◽  
pp. 3199
Author(s):  
Juan Carlos Chimal-Eguia ◽  
Erandi Castillo-Montiel ◽  
Julio Cesar Rangel-Reyes ◽  
Ricardo Teodoro Paez-Hernández
Keyword(s):  
Mathematical Model ◽  
Immune Response ◽  
Dendritic Cells ◽  
Immune System ◽  
Dendritic Cell ◽  
Differential Equations ◽  
Nonlinear Analysis ◽  
Tumor Cells ◽  
Exhaustive Search

Nowadays, immunotherapy has become an important alternative to fight cancer. One way in which biologists and medics use immunotherapy is by injecting antigen-incubated Dendritic Cells (DCs) into mice to stimulate an immune response. The DCs optimal quantities and infusion times for a successful cancer eradication are often unknown to the therapists; usually, these quantities are obtained by testing various protocols. The article shows a model of five differential equations which represents some interactions between some cells of the immune system and tumor cells which is used to test different infusion protocols of Dendritic Cells. This study aims to find operation ranges to DCs quantities and injection times for which the therapy reduces the tumor significantly. To that end, an exhaustive search of operative protocols is performed using simulations of a mathematical model. Furthermore, nonlinear analysis of the model reveals that without the DC therapy tumor cells cannot stay under non-lethal bounds. Finally, we show that a pulsed periodic therapy can prevent tumor relapsing when the doses and period times lie within a certain range.

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