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MiStImm: an agent-based simulation tool to study the self-nonself discrimination of the adaptive immune response

BACKGROUND: There is an increasing need for complex computational models to perform in silico experiments as an adjunct to in vitro and in vivo experiments in immunology. We introduce Microscopic Stochastic Immune System Simulator (MiStImm), an agent-based simulation tool, that is designed to study...

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Autores principales: Kerepesi, Csaba, Bakács, Tibor, Szabados, Tamás
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6498635/
https://www.ncbi.nlm.nih.gov/pubmed/31046789
http://dx.doi.org/10.1186/s12976-019-0105-5
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author Kerepesi, Csaba
Bakács, Tibor
Szabados, Tamás
author_facet Kerepesi, Csaba
Bakács, Tibor
Szabados, Tamás
author_sort Kerepesi, Csaba
collection PubMed
description BACKGROUND: There is an increasing need for complex computational models to perform in silico experiments as an adjunct to in vitro and in vivo experiments in immunology. We introduce Microscopic Stochastic Immune System Simulator (MiStImm), an agent-based simulation tool, that is designed to study the self-nonself discrimination of the adaptive immune system. MiStImm can simulate some components of the humoral adaptive immune response, including T cells, B cells, antibodies, danger signals, interleukins, self cells, foreign antigens, and the interactions among them. The simulation starts after conception and progresses step by step (in time) driven by random simulation events. We also have provided tools to visualize and analyze the output of the simulation program. RESULTS: As the first application of MiStImm, we simulated two different immune models, and then we compared performances of them in the mean of self-nonself discrimination. The first model is a so-called conventional immune model, and the second model is based on our earlier T-cell model, called “one-signal model”, which is developed to resolve three important paradoxes of immunology. Our new T-cell model postulates that a dynamic steady state coupled system is formed through low-affinity complementary TCR–MHC interactions between T cells and host cells. The new model implies that a significant fraction of the naive polyclonal T cells is recruited into the first line of defense against an infection. Simulation experiments using MiStImm have shown that the computational realization of the new model shows real patterns. For example, the new model develops immune memory and it does not develop autoimmune reaction despite the hypothesized, enhanced TCR–MHC interaction between T cells and self cells. Simulations also demonstrated that our new model gives better results to overcome a critical primary infection answering the paradox “how can a tiny fraction of human genome effectively compete with a vastly larger pool of mutating pathogen DNA?” CONCLUSION: The outcomes of our in silico experiments, presented here, are supported by numerous clinical trial observations from the field of immunotherapy. We hope that our results will encourage investigations to make in vitro and in vivo experiments clarifying questions about self-nonself discrimination of the adaptive immune system. We also hope that MiStImm or some concept in it will be useful to other researchers who want to implement or compare other immune models. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12976-019-0105-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-64986352019-05-09 MiStImm: an agent-based simulation tool to study the self-nonself discrimination of the adaptive immune response Kerepesi, Csaba Bakács, Tibor Szabados, Tamás Theor Biol Med Model Software BACKGROUND: There is an increasing need for complex computational models to perform in silico experiments as an adjunct to in vitro and in vivo experiments in immunology. We introduce Microscopic Stochastic Immune System Simulator (MiStImm), an agent-based simulation tool, that is designed to study the self-nonself discrimination of the adaptive immune system. MiStImm can simulate some components of the humoral adaptive immune response, including T cells, B cells, antibodies, danger signals, interleukins, self cells, foreign antigens, and the interactions among them. The simulation starts after conception and progresses step by step (in time) driven by random simulation events. We also have provided tools to visualize and analyze the output of the simulation program. RESULTS: As the first application of MiStImm, we simulated two different immune models, and then we compared performances of them in the mean of self-nonself discrimination. The first model is a so-called conventional immune model, and the second model is based on our earlier T-cell model, called “one-signal model”, which is developed to resolve three important paradoxes of immunology. Our new T-cell model postulates that a dynamic steady state coupled system is formed through low-affinity complementary TCR–MHC interactions between T cells and host cells. The new model implies that a significant fraction of the naive polyclonal T cells is recruited into the first line of defense against an infection. Simulation experiments using MiStImm have shown that the computational realization of the new model shows real patterns. For example, the new model develops immune memory and it does not develop autoimmune reaction despite the hypothesized, enhanced TCR–MHC interaction between T cells and self cells. Simulations also demonstrated that our new model gives better results to overcome a critical primary infection answering the paradox “how can a tiny fraction of human genome effectively compete with a vastly larger pool of mutating pathogen DNA?” CONCLUSION: The outcomes of our in silico experiments, presented here, are supported by numerous clinical trial observations from the field of immunotherapy. We hope that our results will encourage investigations to make in vitro and in vivo experiments clarifying questions about self-nonself discrimination of the adaptive immune system. We also hope that MiStImm or some concept in it will be useful to other researchers who want to implement or compare other immune models. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12976-019-0105-5) contains supplementary material, which is available to authorized users. BioMed Central 2019-05-02 /pmc/articles/PMC6498635/ /pubmed/31046789 http://dx.doi.org/10.1186/s12976-019-0105-5 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Kerepesi, Csaba
Bakács, Tibor
Szabados, Tamás
MiStImm: an agent-based simulation tool to study the self-nonself discrimination of the adaptive immune response
title MiStImm: an agent-based simulation tool to study the self-nonself discrimination of the adaptive immune response
title_full MiStImm: an agent-based simulation tool to study the self-nonself discrimination of the adaptive immune response
title_fullStr MiStImm: an agent-based simulation tool to study the self-nonself discrimination of the adaptive immune response
title_full_unstemmed MiStImm: an agent-based simulation tool to study the self-nonself discrimination of the adaptive immune response
title_short MiStImm: an agent-based simulation tool to study the self-nonself discrimination of the adaptive immune response
title_sort mistimm: an agent-based simulation tool to study the self-nonself discrimination of the adaptive immune response
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6498635/
https://www.ncbi.nlm.nih.gov/pubmed/31046789
http://dx.doi.org/10.1186/s12976-019-0105-5
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