Mathematical modeling of homeostasis and oncogenesis in mature T-cells

维持终身t细胞多样性，即不同t细胞受体（TCR）的丰度，是适应性免疫防御的核心特征。了解导致健壮的多克隆t细胞库的机制是一个基本的生物学问题。然而，它也具有临床相关性，因为有证据表明t细胞稳态的多克隆性质可以阻止成熟t细胞白血病/淋巴瘤（MTCL）的生长。虽然已经确定了几个t细胞转化癌基因（如ALK， TCL1）和受影响的下游事件（如IL-2信号传导），但目前尚不清楚这些基因如何与健康的多克隆t细胞群体的控制相关。另一个可能具有重要调控影响的特征是淋巴结中tcr介导的刺激的异质性和不同刺激区域之间t细胞的循环。为了解决这些和其他悬而未决的问题，RP4将提供成熟t细胞组织的解释性数学建模框架，该框架将应用于稳态和恶性情况。具体来说，我们将结合不同的建模技术，目的是达到生理和恶性t细胞克隆的定量，多尺度描述。在制定具有生物学意义的模型假设、提供可测试的预测以及对理论结果进行实验验证/证伪的迭代过程中，RP4将通过整合不同的实验见解，在CONTROL-T联盟中发挥核心作用。该模型将极大地有助于对t细胞组织的更深入的系统理解，因此，也促进了MTCL新治疗策略的设计。

The maintenance of life-long T-cell diversity, i.e. the abundance of different T-cell receptors (TCR), is a central feature of the adaptive immune defense. Understanding the mechanisms that lead to a robust, polyclonal T-cell repertoire is a basic biological question. However, it is also clinically relevant, because there is evidence that the polyclonal nature of T-cell homeostasis prevents the outgrowth of mature T-cell leukemia/lymphoma (MTCL). Although several T-cell transforming oncogenes (e.g. ALK, TCL1) and effected downstream events (e.g. IL-2 signaling) have been identified, it is currently not resolved how these are related to the control of a healthy, polyclonal T-cell population. Another feature that might have an important regulatory impact is the heterogeneity of TCR-mediated stimuli in lymph nodes and the circulation of T-cells between different stimulatory regions. To address these and other open questions, RP4 will provide an explanatory mathematical modeling framework of mature T-cell organization, which will be applied to the homeostatic and the malignant situation. Specifically, we will combine different modeling techniques, with the aim to arrive at a quantitative, multiscale description of physiological and malignant T-cell clonality. In an iterative process of formulating biologically meaningful model assumptions, providing testable predictions, and experimental validation/falsification of the theoretical results, RP4 will play a central role within the CONTROL-T consortium, by integrating the different experimental insights. The modeling will substantially contribute to a deeper systemic understanding of T-cell organization and, therefore, also fostering the design of new therapeutic strategies for MTCL.