In silico modelling of CD8 T cell immune response links genetic regulation to population dynamics
The CD8 T cell immune response operates at multiple temporal and spatial scales, including all the early complex biochemical and biomechanical processes, up to long term cell population behavior.In order to model this response, we devised a multiscale agent-based approach using Simuscale software. W...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-09-01
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| Series: | ImmunoInformatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667119024000132 |
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| _version_ | 1846091582512562176 |
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| author | Thi Nhu Thao Nguyen Madge Martin Christophe Arpin Samuel Bernard Olivier Gandrillon Fabien Crauste |
| author_facet | Thi Nhu Thao Nguyen Madge Martin Christophe Arpin Samuel Bernard Olivier Gandrillon Fabien Crauste |
| author_sort | Thi Nhu Thao Nguyen |
| collection | DOAJ |
| description | The CD8 T cell immune response operates at multiple temporal and spatial scales, including all the early complex biochemical and biomechanical processes, up to long term cell population behavior.In order to model this response, we devised a multiscale agent-based approach using Simuscale software. Within each agent (cell) of our model, we introduced a gene regulatory network (GRN) based upon a piecewise deterministic Markov process formalism. Cell fate – differentiation, proliferation, death – was coupled to the state of the GRN through rule-based mechanisms. Cells interact in a 3D computational domain and signal to each other via cell–cell contacts, influencing the GRN behavior.Results show the ability of the model to correctly capture both population behavior and molecular time-dependent evolution. We examined the impact of several parameters on molecular and population dynamics, and demonstrated the add-on value of using a multiscale approach by showing the influence of molecular parameters, particularly protein degradation rates, on the outcome of the response, such as effector and memory cell counts. |
| format | Article |
| id | doaj-art-723f23237fc345ca8565728c8ff74cde |
| institution | Kabale University |
| issn | 2667-1190 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | ImmunoInformatics |
| spelling | doaj-art-723f23237fc345ca8565728c8ff74cde2025-01-10T04:38:28ZengElsevierImmunoInformatics2667-11902024-09-0115100043In silico modelling of CD8 T cell immune response links genetic regulation to population dynamicsThi Nhu Thao Nguyen0Madge Martin1Christophe Arpin2Samuel Bernard3Olivier Gandrillon4Fabien Crauste5Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d’Italie Site Jacques Monod, Lyon, F-69007, France; Inria, Villeurbanne, 69693, FranceCNRS, Univ Paris Est Creteil, Univ Gustave Eiffel, UMR 8208, MSME, Creteil, F-94010, FranceUniv Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d’Italie Site Jacques Monod, Lyon, F-69007, France; Centre International de Recherche en Infectiologie, Université de Lyon, ENS de Lyon, Université Claude Bernard, CNRS UMR 5308, INSERM U1111, Lyon, FranceInria, Villeurbanne, 69693, France; Univ Lyon, Université Lyon 1, CNRS UMR5208, Institut Camille Jordan, 43 Blvd du 11 Novembre 1918, Villeurbanne-Cedex, F-69622, FranceUniv Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d’Italie Site Jacques Monod, Lyon, F-69007, France; Inria, Villeurbanne, 69693, FranceUniversité Paris Cité, CNRS, MAP5, Paris, F-75006, France; Corresponding author.The CD8 T cell immune response operates at multiple temporal and spatial scales, including all the early complex biochemical and biomechanical processes, up to long term cell population behavior.In order to model this response, we devised a multiscale agent-based approach using Simuscale software. Within each agent (cell) of our model, we introduced a gene regulatory network (GRN) based upon a piecewise deterministic Markov process formalism. Cell fate – differentiation, proliferation, death – was coupled to the state of the GRN through rule-based mechanisms. Cells interact in a 3D computational domain and signal to each other via cell–cell contacts, influencing the GRN behavior.Results show the ability of the model to correctly capture both population behavior and molecular time-dependent evolution. We examined the impact of several parameters on molecular and population dynamics, and demonstrated the add-on value of using a multiscale approach by showing the influence of molecular parameters, particularly protein degradation rates, on the outcome of the response, such as effector and memory cell counts.http://www.sciencedirect.com/science/article/pii/S2667119024000132Gene regulatory networksCell population dynamicsCD8 T cell immune responseStochastic gene expressionMultiscale modeling |
| spellingShingle | Thi Nhu Thao Nguyen Madge Martin Christophe Arpin Samuel Bernard Olivier Gandrillon Fabien Crauste In silico modelling of CD8 T cell immune response links genetic regulation to population dynamics ImmunoInformatics Gene regulatory networks Cell population dynamics CD8 T cell immune response Stochastic gene expression Multiscale modeling |
| title | In silico modelling of CD8 T cell immune response links genetic regulation to population dynamics |
| title_full | In silico modelling of CD8 T cell immune response links genetic regulation to population dynamics |
| title_fullStr | In silico modelling of CD8 T cell immune response links genetic regulation to population dynamics |
| title_full_unstemmed | In silico modelling of CD8 T cell immune response links genetic regulation to population dynamics |
| title_short | In silico modelling of CD8 T cell immune response links genetic regulation to population dynamics |
| title_sort | in silico modelling of cd8 t cell immune response links genetic regulation to population dynamics |
| topic | Gene regulatory networks Cell population dynamics CD8 T cell immune response Stochastic gene expression Multiscale modeling |
| url | http://www.sciencedirect.com/science/article/pii/S2667119024000132 |
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