Cells of the mature αβ T cell repertoire arise from the development in the thymus of bone marrow precursors (thymocytes). αβ T cell maturation is characterized by the expression of thousands of copies of identical αβ T cell receptors and the CD4 and/or CD8 co-receptors on the surface of thymocytes. The maturation stages of a thymocyte are: (1) double negative (DN) (TCR−, CD4− and CD8−), (2) double positive (DP) (TCR+, CD4+ and CD8+), and (3) single positive (SP) (TCR+, CD4+ or CD8+). Thymic antigen presenting cells provide the appropriate micro-architecture for the maturation of thymocytes, which “sense” the signaling environment via their randomly generated TCRs. Thymic development is characterized by (i) an extremely low success rate, and (ii) the selection of a functional and self-tolerant T cell repertoire. In this paper, we combine recent experimental data and mathematical modeling to study the selection events that take place in the thymus after the DN stage. The stable steady state of the model for the pre-DP, post-DP, and SP populations is identified with the experimentally measured cell counts from 5.5- to 17-week-old mice. We make use of residence times in the cortex and the medulla for the different populations, as well as recently reported asymmetric death rates for CD4 and CD8 SP thymocytes. We estimate that 65.8% of pre-DP thymocytes undergo death by neglect. In the post-DP compartment, 91.7% undergo death by negative selection, 4.7% become CD4 SP, and 3.6% become CD8 SP. Death by negative selection in the medulla removes 8.6% of CD4 SP and 32.1% of CD8 SP thymocytes. Approximately 46.3% of CD4 SP and 27% of CD8 SP thymocytes divide before dying or exiting the thymus.

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The homeostasis of T cell populations depends on migration, division and death of individual cells (1). T cells migrate between spatial compartments (spleen, lymph nodes, lung, liver, etc.), where they may divide or differentiate, and eventually die (2). The kinetics of recirculation influences the speed at which local infections are detected and controlled (3). New experimental techniques have been developed to measure the lifespan of cells, and their migration dynamics; for example, fluorescence-activated cell sorting (4), in vitro time-lapse microscopy (5), or in vivo stable isotope labeling (e.g., deuterium) (6). When combined with mathematical and computational models, they allow estimation of rates of migration, division, differentiation and death (6, 7). In this work, we develop a stochastic model of a single cell migrating between spatial compartments, dividing and eventually dying. We calculate the number of division events during a T cell's journey, its lifespan, the probability of dying in each compartment and the number of progeny cells. A fast-migration approximation allows us to compute these quantities when migration rates are larger than division and death rates. Making use of published rates: (i) we analyse how perturbations in a given spatial compartment impact the dynamics of a T cell, (ii) we study the accuracy of the fast-migration approximation, and (iii) we quantify the role played by direct migration (not via the blood) between some compartments.

Assessment is an important tool for quantifying each student’s relative ability but when carefully designed can also be used to educate and empower students. Because the importance of statistical understanding is becoming increasingly recognised, there is a subsequent growth in non-specialist students taking statistics service courses. The assessments for such courses can define students’ personal course aims and level of engagement and set the tone for their future interactions with the subject. We have designed and implemented a modernised assessment pattern, with assessments structured to build upon each other and lead students from the foundations of probability to interpretation and communication of authentic statistical analyses. We discuss our experiences having implemented this new assessment pattern across three courses, totaling more than 600 students.