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

跟踪造血过程中的克隆动态：通过建模和数据分析了解机制

基本信息

批准号：
10368986
负责人：
TOM CHOU
金额：
$ 18.52万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-04-15 至 2024-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10368986
关键词：
Adaptive Immune System Address Affect Aging Animals Bar Codes Blood Blood Cells Blood specimen Bone Marrow Transplantation Cell Count Cell Lineage Cells Clinical Clonal Hematopoietic Stem Cell DNA Integration Data Data Analyses Development Dimensions Disease Extinction (Psychology)Goals Hematological Disease Hematopoiesis Hematopoietic Hematopoietic Neoplasms Hematopoietic Stem Cell heterogeneity Hematopoietic stem cells Heterogeneity Hybrids Immunologics Individual Lead Lentivirus Vector Link Longevity Macaca mulatta Maintenance Mathematics Modeling Mus Noise Organism Pathway interactions Phylogeny Population Process Regenerative Medicine Regulation Research Resolution Sampling Series Stem cell transplant Structure T-Cell Depletion T-Lymphocyte Testing Time Tissues Transplantation Trees Variant Vertebrates Virus Integration base blood treatment cell type experimental study gene therapy high dimensionality innovation insight integration site interest mathematical model multi-scale modeling nonhuman primate peripheral blood progenitor statistics stem stem cell gene therapy stem cells tool

项目摘要

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.

项目总结/文摘:

项目成果

期刊论文数量（31）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Machine learning and statistical prediction of patient quality-of-life after prostate radiation therapy.

前列腺放射治疗后患者生活质量的机器学习和统计预测。

DOI：
10.1016/j.compbiomed.2020.104127
发表时间：
2021
期刊：
Computers in biology and medicine
影响因子：
7.7
作者：
Yang,Zhijian;Olszewski,Daniel;He,Chujun;Pintea,Giulia;Lian,Jun;Chou,Tom;Chen,RonaldC;Shtylla,Blerta
通讯作者：
Shtylla,Blerta

Nonspecific probe binding and automatic gating in flow cytometry and fluorescence activated cell sorting (FACS).

流式细胞术和荧光激活细胞分选 (FACS) 中的非特异性探针结合和自动门控。