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Modelling the host–pathogen interactions of macrophages and Candida albicans using Game Theory and dynamic optimization
The release of fungal cells following macrophage phagocytosis, called non-lytic expulsion, is reported for several fungal pathogens. On one hand, non-lytic expulsion may benefit the fungus in escaping the microbicidal environment of the phagosome. On the other hand, the macrophage could profit in te...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5550964/ https://www.ncbi.nlm.nih.gov/pubmed/28701506 http://dx.doi.org/10.1098/rsif.2017.0095 |
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author | Dühring, Sybille Ewald, Jan Germerodt, Sebastian Kaleta, Christoph Dandekar, Thomas Schuster, Stefan |
author_facet | Dühring, Sybille Ewald, Jan Germerodt, Sebastian Kaleta, Christoph Dandekar, Thomas Schuster, Stefan |
author_sort | Dühring, Sybille |
collection | PubMed |
description | The release of fungal cells following macrophage phagocytosis, called non-lytic expulsion, is reported for several fungal pathogens. On one hand, non-lytic expulsion may benefit the fungus in escaping the microbicidal environment of the phagosome. On the other hand, the macrophage could profit in terms of avoiding its own lysis and being able to undergo proliferation. To analyse the causes of non-lytic expulsion and the relevance of macrophage proliferation in the macrophage–Candida albicans interaction, we employ Evolutionary Game Theory and dynamic optimization in a sequential manner. We establish a game-theoretical model describing the different strategies of the two players after phagocytosis. Depending on the parameter values, we find four different Nash equilibria and determine the influence of the systems state of the host upon the game. As our Nash equilibria are a direct consequence of the model parameterization, we can depict several biological scenarios. A parameter region, where the host response is robust against the fungal infection, is determined. We further apply dynamic optimization to analyse whether macrophage mitosis is relevant in the host–pathogen interaction of macrophages and C. albicans. For this, we study the population dynamics of the macrophage–C. albicans interactions and the corresponding optimal controls for the macrophages, indicating the best macrophage strategy of switching from proliferation to attacking fungal cells. |
format | Online Article Text |
id | pubmed-5550964 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-55509642017-08-11 Modelling the host–pathogen interactions of macrophages and Candida albicans using Game Theory and dynamic optimization Dühring, Sybille Ewald, Jan Germerodt, Sebastian Kaleta, Christoph Dandekar, Thomas Schuster, Stefan J R Soc Interface Life Sciences–Mathematics interface The release of fungal cells following macrophage phagocytosis, called non-lytic expulsion, is reported for several fungal pathogens. On one hand, non-lytic expulsion may benefit the fungus in escaping the microbicidal environment of the phagosome. On the other hand, the macrophage could profit in terms of avoiding its own lysis and being able to undergo proliferation. To analyse the causes of non-lytic expulsion and the relevance of macrophage proliferation in the macrophage–Candida albicans interaction, we employ Evolutionary Game Theory and dynamic optimization in a sequential manner. We establish a game-theoretical model describing the different strategies of the two players after phagocytosis. Depending on the parameter values, we find four different Nash equilibria and determine the influence of the systems state of the host upon the game. As our Nash equilibria are a direct consequence of the model parameterization, we can depict several biological scenarios. A parameter region, where the host response is robust against the fungal infection, is determined. We further apply dynamic optimization to analyse whether macrophage mitosis is relevant in the host–pathogen interaction of macrophages and C. albicans. For this, we study the population dynamics of the macrophage–C. albicans interactions and the corresponding optimal controls for the macrophages, indicating the best macrophage strategy of switching from proliferation to attacking fungal cells. The Royal Society 2017-07 2017-07-12 /pmc/articles/PMC5550964/ /pubmed/28701506 http://dx.doi.org/10.1098/rsif.2017.0095 Text en © 2017 The Author(s). http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Life Sciences–Mathematics interface Dühring, Sybille Ewald, Jan Germerodt, Sebastian Kaleta, Christoph Dandekar, Thomas Schuster, Stefan Modelling the host–pathogen interactions of macrophages and Candida albicans using Game Theory and dynamic optimization |
title | Modelling the host–pathogen interactions of macrophages and Candida albicans using Game Theory and dynamic optimization |
title_full | Modelling the host–pathogen interactions of macrophages and Candida albicans using Game Theory and dynamic optimization |
title_fullStr | Modelling the host–pathogen interactions of macrophages and Candida albicans using Game Theory and dynamic optimization |
title_full_unstemmed | Modelling the host–pathogen interactions of macrophages and Candida albicans using Game Theory and dynamic optimization |
title_short | Modelling the host–pathogen interactions of macrophages and Candida albicans using Game Theory and dynamic optimization |
title_sort | modelling the host–pathogen interactions of macrophages and candida albicans using game theory and dynamic optimization |
topic | Life Sciences–Mathematics interface |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5550964/ https://www.ncbi.nlm.nih.gov/pubmed/28701506 http://dx.doi.org/10.1098/rsif.2017.0095 |
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