The SATvac model of CD8+ T cell expansion and contraction phases considering memory and effector cell differentiation

Primary immune responses induce CD8 + T cell responses characterized by activation via antigen presenting cells, expansion, differentiation into effector and memory phenotypes, and contraction resulting in long-term memory populations. In this study a mathematical stochastic agent-based model is developed to simulate all phases of the CD8 + T cell response following vaccination. Importantly, the model successfully captures the stochastic nature of T cell dynamics throughout the response. It predicts T cell population with high accuracy while addressing mouse-to-mouse variability, highlighting its robust predictive power. This predictive model aims to improve T cell vaccination strategies by both informing the biology of the T cell response and streamlining vaccine development.

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Publication Details

Journal
PLoS Computational Biology
Published
2026-08-18
DOI
https://doi.org/10.1371/journal.pcbi.1014702
Primary Topic
vaccines and immunoinformatics approaches
Type
article
Field-Weighted Citation Impact
0.00
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article

The SATvac model of CD8+ T cell expansion and contraction phases considering memory and effector cell differentiation

David A. Christian, Thomas A. Adams, Seyedeh Fatemeh Seyyedizadeh
PLoS Computational Biology
vaccines and immunoinformatics approaches
article

The SATvac model of CD8+ T cell expansion and contraction phases considering memory and effector cell differentiation

David A. Christian, Thomas A. Adams, Seyedeh Fatemeh Seyyedizadeh
article en

Abstract

Primary immune responses induce CD8 + T cell responses characterized by activation via antigen presenting cells, expansion, differentiation into effector and memory phenotypes, and contraction resulting in long-term memory populations. In this study a mathematical stochastic agent-based model is developed to simulate all phases of the CD8 + T cell response following vaccination. Importantly, the model successfully captures the stochastic nature of T cell dynamics throughout the response. It predicts T cell population with high accuracy while addressing mouse-to-mouse variability, highlighting its robust predictive power. This predictive model aims to improve T cell vaccination strategies by both informing the biology of the T cell response and streamlining vaccine development.

PLoS Computational BiologyVol. 22(8)
Good health and well-being
Openalex Percentile: Top 16%
vaccines and immunoinformatics approaches
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