Tracking clonal dynamics during hematopoiesis: mechanistic insight via modeling and data analysis

项目总结/摘要： 了解造血，血液的形成和维持以及许多适应性免疫 系统对于理解血液疾病和临床方法（如干细胞/基因治疗）至关重要 和骨髓移植。一个长期的目标是解开和量化的动态如何 造血干细胞（HSC）在血液中产生无数不同的细胞谱系。具体而言，我们希望 评估实验观察和数据，以推断不同的可能的干和祖生态位， 发展的“树”结构，推导出主要的调节机制，并估计时间尺度内 造血为了实现这些目标，并将观察和数据与机械洞察力联系起来，我们必须 首先实现以下具体目标：（I）开发随机和确定性细胞计数和克隆计数 条形码化的HSC的造血模型，（ii）将时间变异性的机制纳入模型， 模型，（iii）开发小血样中克隆分布的概率框架，以及（iv） 对我们的模型进行统计测试。我们的假设是条形码/标签实验 可以使用“中性”基本模型在最低阶近似下处理，在此基础上，可以 使其包含额外的复杂性，例如细胞异质性。战略意义重大 因为它使我们能够制定一个高维数学框架， 分析长期克隆跟踪实验的一个点，在该实验中， 追踪.该研究对于解决高维克隆跟踪数据和推进 简约，但知情的多尺度模型，允许造血机制的推理。

PROJECT SUMMARY/ABSTRACT: Understanding hematopoiesis, the formation and maintenance of blood and much of the adaptive immune system is essential for understanding blood diseases and clinical approaches such as stem cell/gene therapy and bone marrow transplantation. A long term goal is to disentangle and quantify the dynamics of how an hematopoietic stem cell (HSC) generates the myriad of different cell lineages in blood. Specifically, we wish to evaluate experimental observations and data to deduce the different possible stem and progenitor niches, infer the developmental “tree” structure, deduce the main regulation mechanisms, and estimate time scales within hematpoiesis. In order to achieve these goals and link observations and data to mechanistic insight, we must first achieve the specific aims of (I) developing both stochastic and deterministic cell count and clone count models for hematopoiesis of barcoded HSCs, (ii) incorporate mechanisms of temporal variability into the models, (iii) develop a probabilistic framework for clonal distributions in small blood samples, and (iv) implement statistical tests of our models against data. Our hypothesis is that barcoding/tagging experiments can be treated at the lowest order of approximation using a “neutral'' base model, upon which refinements can be made to incorporate additional complexities such as cellular heterogeneity. The strategy is significant because it allows us to formulate a high-dimensional mathematical framework that will be a general starting point for analyzing long term clonal tracking experiments in which up to thousands of clone lineages are tracked. The research is innovative for addressing high-dimensional clonal tracking data and advancing parsimonious, yet informed multiscale models that allow inference of hematopoietic mechanisms.