Discrete Event Modeling of CD4+ Memory T Cell Generation

Abstract

Studies of memory T cell differentiation are hampered by a lack of quantitative models to test hypotheses in silico before in vivo experimentation. We created a stochastic computer model of CD4+ memory T cell generation that can simulate and track 101–108 individual lymphocytes over time. Parameters for the model were derived from experimental data using naive human CD4+ T cells stimulated in vitro. Using discrete event computer simulation, we identified two key variables that heavily influence effector burst size and the persistent memory pool size: the cell cycle dependent probability of apoptosis, and the postactivation mitosis at which memory T cells emerge. Multiple simulations were performed and varying critical parameters permitted estimates of how sensitive the model was to changes in all of the model parameters. We then compared two hypotheses of CD4+ memory T cell generation: maturation from activated naive to effector to memory cells (model I) vs direct progression from activated naive to memory cells (model II). We find that direct progression of naive to memory T cells does not explain published measurements of the memory cell mass unless postactivation expansion of the memory cell cohort occurs. We conclude that current models suggesting direct progression of activated naive cells to the persistent memory phenotype (model II) do not account for the experimentally measured size of the postactivation CD4+, Ag-specific, memory T cell cohort.

Quantifying T lymphocyte turnover

“…The activated cells die with a probability 0.13 per generation, they divide with a probability 0.66, and they revert to rest with a probability 0.21 per generation. The fact that about 20% of the cycling cells reverts to a quiescent state at each generation could account for the generation of separate lineages of memory cells and effector cells during clonal expansion [78, 115, 245]. The fact that cells die so rapidly suggests that the decision to die is made in preceding generations, which we would predict that sister cells are expected to die at similar times [115].…”

Modeling using Discrete Event Simulation: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-4

“…Applications in health care have increased over the last 40 years [8] and include biologic models [9,10], process redesign and optimization [11][12][13], geographic allocation of resources [14,15], trial design [16,17], and policy evaluation [18 -20].…”

An age-dependent branching process model for the analysis of CFSE-labeling experiments

“…Our analytic observation may also suggest a mechanism for memory CD8 T cell generation, namely that these "resting" cells may be memory T cells, and that they may arise in each generation after activation. Zand et al [41] have previously hypothesized a similar mechanism for generation of CD4 T cell memory, and Ganusov [42] hypothesized a similar mechanism for generation of memory CD8+ T cells in vivo during lymphocytic choriomeningitis virus infection.…”