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

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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.