Mathematically modeling dynamics of T cell responses: Predictions concerning the generation of memory cells

Kohler, Brynja

doi:10.1016/j.jtbi.2006.10.017

Cited by 10 publications

(13 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different models have been proposed to describe the dynamics of CD8 ϩ T cells after viral infections (8,9,12,15 ϩ T cell responses during acute and chronic LCMV infection. The model considers clones of activated (A) and effector/ memory (M) cells.…”

Section: Methodsmentioning

confidence: 99%

Dynamics of CD8+ T Cell Responses during Acute and Chronic Lymphocytic Choriomeningitis Virus Infection

Althaus

Ganusov

Boer

2007

The Journal of Immunology

View full text Add to dashboard Cite

Infection of mice with lymphocytic choriomeningitis virus (LCMV) is frequently used to study the underlying principles of viral infections and immune responses. We fit a mathematical model to recently published data characterizing Ag-specific CD8+ T cell responses during acute (Armstrong) and chronic (clone 13) LCMV infection. This allows us to analyze the differences in the dynamics of CD8+ T cell responses against different types of LCMV infections. For the four CD8+ T cell responses studied, we find that, compared with the responses against acute infection, responses against chronic infection are generally characterized by an earlier peak and a faster contraction phase thereafter. Furthermore, the model allows us to give a new interpretation of the effect of thymectomy on the dynamics of CD8+ T cell responses during chronic LCMV infection: a smaller number of naive precursor cells is sufficient to account for the observed differences in the responses in thymectomized mice. Finally, we compare data characterizing LCMV-specific CD8+ T cell responses from different laboratories. Although the data were derived from the same experimental model, we find quantitative differences that can be solved by introducing a scaling factor. Also, we find kinetic differences that are at least partly due to the infrequent measurements of CD8+ T cells in the different laboratories.

show abstract

Section: Methodsmentioning

confidence: 99%

Dynamics of CD8+ T Cell Responses during Acute and Chronic Lymphocytic Choriomeningitis Virus Infection

Althaus

Ganusov

Boer

2007

The Journal of Immunology

View full text Add to dashboard Cite

show abstract

“…However, Antia et al [5], Kohler [128] and Ganusov [74] have developed and evaluated a set of mathematical models on the basis of how well they explain various data sets on T cell memory. For instance, the LCMV data discussed above were also modeled assuming that both memory and effector cells can be formed beginning at the time the cells are triggered into clonal expansion, and that both cell types expand at the same rate p [128]. Two variants of this alternative model were considered, one in which memory cells become effector cells, and another where effectors cells become memory cells (like in Eq.…”

Section: General Models For the Immune Responsementioning

confidence: 99%

Quantifying T lymphocyte turnover

Boer

Perelson

2013

Journal of Theoretical Biology

228

275

View full text Add to dashboard Cite

Peripheral T cell populations are maintained by production of naive T cells in the thymus, clonal expansion of activated cells, cellular self-renewal (or homeostatic proliferation), and density dependent cell life spans. A variety of experimental techniques have been employed to quantify the relative contributions of these processes. In modern studies lymphocytes are typically labeled with 5-bromo-2′-deoxyuridine (BrdU), deuterium, or the fluorescent dye carboxy-fluorescein diacetate succinimidyl ester (CFSE), their division history has been studied by monitoring telomere shortening and the dilution of T cell receptor excision circles (TRECs) or the dye CFSE, and clonal expansion has been documented by recording changes in the population densities of antigen specific cells. Proper interpretation of such data in terms of the underlying rates of T cell production, division, and death has proven to be notoriously difficult and involves mathematical modeling. We review the various models that have been developed for each of these techniques, discuss which models seem most appropriate for what type of data, reveal open problems that require better models, and pinpoint how the assumptions underlying a mathematical model may influence the interpretation of data. Elaborating various successful cases where modeling has delivered new insights in T cell population dynamics, this review provides quantitative estimates of several processes involved in the maintenance of naive and memory, CD4+ and CD8+ T cell pools in mice and men.

show abstract

“…(1a) and (1b) mainly assumes that CD4 + T cells progress along the differentiation pathway naïve / activated → effector → memory , an alternative model proposed by Lanzavecchia and Sallusto (30) suggests that memory cells directly develop out of naïve lymphocytes and effector cell represent the final differentiation step. Based on this alternative differentiation pathway, Kohler (24) proposed a model that considered two types of cells, memory ( M ) and effector ( E ) cells. Analogously to the model given in Eqs.…”

Section: Methodsmentioning

confidence: 99%

“…Using previously published mathematical models and extensions thereof to study the proliferation dynamics of T lymphocytes (22–24), we examine the dynamics of LLO118 and LLO56 CD4 + T cells during L. monocytogenes infection of mice. Our analysis reveals that LLO118 CD4 + T cells have a higher proliferation rate than LLO56.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of CD4+ T Cell Responses against Listeria monocytogenes

Graw

Weber

Allen

et al. 2012

The Journal of Immunology

View full text Add to dashboard Cite

The generation of CD4+ T cell memory cells is poorly understood. Recently, two different murine CD4+ TCR transgenic T cell lines, LLO118 and LLO56, both specific for the same epitope but differing in their expression level of the cell surface protein CD5, were generated. Interestingly, these cell lines showed different behavior upon primary and secondary exposure to Listeria monocytogenes. While LLO118 showed a stronger primary response and generated more robust CD8+ T cell help upon secondary exposure, LLO56 CD4+ T cells had a dramatically better recall response. Using different mathematical models, we analyzed the dynamics of the two CD4+ T cell lines in mice during infection with L. monocytogenes. Our models allowed the quantitative comparison of the two T cell lines and provided predictions for the conversion of naïve T cells into memory cells. LLO118 CD4+ T cells are estimated to have a higher proliferation rate than LLO56 upon primary exposure. This difference can be explained by the lower expression level of CD5 on LLO118 CD4+ T cells. Furthermore, LLO56 memory cells are predicted to have a three-fold longer half-life than LLO118 memory cells ( t1/2LLO118≈4.3-5days and t1/2LLO56≈11.5-13.9days). Although both cell lines differ in their memory capabilities, our analysis indicates no difference in the rate at which memory cells are generated. Our results show that different CD5 expression levels influence the proliferation dynamics of activated naïve CD4+T cells while leaving the conversion rate of those cells into memory cells unaffected.

show abstract

Mathematically modeling dynamics of T cell responses: Predictions concerning the generation of memory cells

Cited by 10 publications

References 23 publications

Dynamics of CD8+ T Cell Responses during Acute and Chronic Lymphocytic Choriomeningitis Virus Infection

Dynamics of CD8+ T Cell Responses during Acute and Chronic Lymphocytic Choriomeningitis Virus Infection

Quantifying T lymphocyte turnover

Dynamics of CD4+ T Cell Responses against Listeria monocytogenes

Contact Info

Product

Resources

About