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You are here: Home / Teams / Systems Biology of Decision Making - O. Gandrillon / Publications (not up to date) / Predicting pathogen-specific CD8 T cell immune responses from a modeling approach.

Predicting pathogen-specific CD8 T cell immune responses from a modeling approach.

F Crauste, E Terry, I L Mercier, J Mafille, S Djebali, T Andrieu, B Mercier, G Kaneko, C Arpin, J Marvel, and O Gandrillon (2015)

J Theor Biol, 374:66-82.

The primary CD8 T cell immune response constitutes a major mechanism to fight aninfection by intra-cellular pathogens. We aim at assessing whether pathogen-specific dynamical parameters of the CD8 T cell response can be identified, based on measurements of CD8 T cell counts, using a modeling approach. We generated experimental data consisting in CD8 T cell counts kinetics during the response to three different live intra-cellular pathogens: two viruses (influenza, vaccinia) injected intranasally, and one bacteria (Listeria monocytogenes) injected intravenously. All pathogens harbor the same antigen (NP68), but differ in their interaction with the host. In parallel, we developeda mathematical model describing the evolution of CD8 T cell counts and pathogen amount during an immune response. This model is characterized by 9 parameters and includes relevant feedback controls. The model outputs were compared with the three data series and an exhaustive estimation of the parameter values was performed. By focusing on the ability of the model to fit experimental data and to produce a CD8 T cell population mainly composed of memory cells at the end ofthe response, critical parameters were identified. We show that a small number of parameters (2-4) define the main features of the CD8 T cell immune response and are characteristic of a given pathogen. Among these parameters, two are related to the effector CD8 T cell mediated control of cell and pathogen death. The parameter associated with memory cell death is shown to play no relevant role during the main phases of the CD8 T cell response, yet it becomes essential whenlooking at the predictions of the model several months after the infection.

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