Understanding Experimental LCMV Infection of Mice: The Role of Mathematical Models
Virus infections represent complex biological systems governed by multiple-level regulatory processes of virus replication and host immune responses. Understanding of the infection means an ability to predict the systems behaviour under various conditions. Such predictions can only rely upon quantit...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Journal of Immunology Research |
| Online Access: | http://dx.doi.org/10.1155/2015/739706 |
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| author | Gennady Bocharov Jordi Argilaguet Andreas Meyerhans |
| author_facet | Gennady Bocharov Jordi Argilaguet Andreas Meyerhans |
| author_sort | Gennady Bocharov |
| collection | DOAJ |
| description | Virus infections represent complex biological systems governed by multiple-level regulatory processes of virus replication and host immune responses. Understanding of the infection means an ability to predict the systems behaviour under various conditions. Such predictions can only rely upon quantitative mathematical models. The model formulations should be tightly linked to a fundamental step called “coordinatization” (Hermann Weyl), that is, the definition of observables, parameters, and structures that enable the link with a biological phenotype. In this review, we analyse the mathematical modelling approaches to LCMV infection in mice that resulted in quantification of some fundamental parameters of the CTL-mediated virus control including the rates of T cell turnover, infected target cell elimination, and precursor frequencies. We show how the modelling approaches can be implemented to address diverse aspects of immune system functioning under normal conditions and in response to LCMV and, importantly, make quantitative predictions of the outcomes of immune system perturbations. This may highlight the notion that data-driven applications of meaningful mathematical models in infection biology remain a challenge. |
| format | Article |
| id | doaj-art-158cd8bf5e1e4f43911d1f4826510258 |
| institution | Kabale University |
| issn | 2314-8861 2314-7156 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Immunology Research |
| spelling | doaj-art-158cd8bf5e1e4f43911d1f48265102582025-02-03T01:27:10ZengWileyJournal of Immunology Research2314-88612314-71562015-01-01201510.1155/2015/739706739706Understanding Experimental LCMV Infection of Mice: The Role of Mathematical ModelsGennady Bocharov0Jordi Argilaguet1Andreas Meyerhans2Institute of Numerical Mathematics, Russian Academy of Sciences, Gubkina Street 8, Moscow 119333, RussiaInfection Biology Laboratory, Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, SpainInfection Biology Laboratory, Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, SpainVirus infections represent complex biological systems governed by multiple-level regulatory processes of virus replication and host immune responses. Understanding of the infection means an ability to predict the systems behaviour under various conditions. Such predictions can only rely upon quantitative mathematical models. The model formulations should be tightly linked to a fundamental step called “coordinatization” (Hermann Weyl), that is, the definition of observables, parameters, and structures that enable the link with a biological phenotype. In this review, we analyse the mathematical modelling approaches to LCMV infection in mice that resulted in quantification of some fundamental parameters of the CTL-mediated virus control including the rates of T cell turnover, infected target cell elimination, and precursor frequencies. We show how the modelling approaches can be implemented to address diverse aspects of immune system functioning under normal conditions and in response to LCMV and, importantly, make quantitative predictions of the outcomes of immune system perturbations. This may highlight the notion that data-driven applications of meaningful mathematical models in infection biology remain a challenge.http://dx.doi.org/10.1155/2015/739706 |
| spellingShingle | Gennady Bocharov Jordi Argilaguet Andreas Meyerhans Understanding Experimental LCMV Infection of Mice: The Role of Mathematical Models Journal of Immunology Research |
| title | Understanding Experimental LCMV Infection of Mice: The Role of Mathematical Models |
| title_full | Understanding Experimental LCMV Infection of Mice: The Role of Mathematical Models |
| title_fullStr | Understanding Experimental LCMV Infection of Mice: The Role of Mathematical Models |
| title_full_unstemmed | Understanding Experimental LCMV Infection of Mice: The Role of Mathematical Models |
| title_short | Understanding Experimental LCMV Infection of Mice: The Role of Mathematical Models |
| title_sort | understanding experimental lcmv infection of mice the role of mathematical models |
| url | http://dx.doi.org/10.1155/2015/739706 |
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