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Systems Biology of In Vivo Human Blood Cell Populations

体内人类血细胞群的系统生物学

基本信息

批准号：
8354901
负责人：
John Matthew Higgins
金额：
$ 261.13万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-09-28 至 2017-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8354901
关键词：
Age Area Autoimmune Diseases Autoimmunity Biological Birth Blood Cells Blood Platelets Cell Cycle Kinetics Cell Lineage Cell Size Cells Cessation of life Characteristics Clinical Computer Simulation Data Data Set Diagnosis Diagnostic Disease Erythrocytes Functional disorder Genomics HIV Hemoglobin Human Human Biology Idiopathic Thrombocytopenic Purpura Individual Infection Intervention Kinetics Knowledge Laboratories Leukocytes Lymphocyte Malignant Neoplasms Measurement Measures Medical Medicine Methods Modeling Molecular Monitor Nuclear Pathologic Patients Population Population Characteristics Process Proteomics Research Personnel Sepsis Structure Systems Biology Testing abstracting base biological systems computer framework diabetic patient glycation human disease in vivo insight leukemia mathematical model metabolomics neutrophil prognostic public health relevance research study response success

项目摘要

DESCRIPTION (Provided by the applicant) Abstract: Modern molecular methods enable the simultaneous measurement of thousands and thousands of biological states. These newly available genomic, proteomic, and metabolomic data sets are valuable in that they reveal new aspects of biological systems that can be directly measured. But the true potential of this new data to enable fundamental advances in our understanding of human biology and medicine may lie in its use inferring the dynamics of biological systems: how quickly are these biological states changing, and what conditions or interventions control these rates of change? An understanding of biological dynamics has proven essential to disparate areas of medical diagnosis and treatment. For example, experiments revealing white blood cell kinetics guided early success in HIV treatments, and knowledge of hemoglobin glycation rates and blood cell turnover is crucial to current best practices for managing diabetic patients. Kinetics and dynamics cannot be measured directly and must be inferred using computational and mathematical modeling. Because very few clinically-informed investigators have necessary mathematical and computational expertise, most dynamic aspects of human biology and disease remain poorly understood, and patients are unable to benefit from the fundamental diagnostic and prognostic insights this dynamical understanding would enable. I will develop a clinically-informed mathematical and computational framework to infer the dynamics of cellular pathophysiologic processes in humans in vivo using routinely available ensemble measurements of cellular population characteristics. I will apply the modeling framework to all blood cell lineages including lymphocytes, neutrophils, erythrocytes, and platelets and will reveal insights and applications for representative types of disease including cancer (leukemia), infection (sepsis), and autoimmune disease (idiopathic thrombocytopenic purpura). I will synthesize existing scientific and clinical knowledge of cellular pathophysiology into mathematical models describing rates of cellular birth, death, influx, and efflux, as well as how these rates vary among individual patients and within patient cell populations as a function of cell size, age, nuclear complexity, and other single-cell characteristics. I will then compare model parameter trajectories for healthy individuals and patients with disease to reveal new details of disease mechanisms and the pathologic responses they and their treatments elicit. Because the structure of the mathematical models is informed by current knowledge of pathophysiology, model parameters represent personalized quantification of important homeostatic processes and provide new conceptual insights into human pathophysiology. Because models are built with routinely available clinical measurements, these insights will often be immediately translatable. Public Health Relevance: The proposal develops a new mechanism-based modeling framework that will use existing clinical laboratory tests to provide earlier, more accurate, and personalized diagnosis and treatment monitoring for a range of diseases including cancer, infection, and autoimmunity.

描述（由申请人提供）翻译后摘要：现代分子方法使成千上万的生物状态的同时测量。这些新获得的基因组、蛋白质组和代谢组数据集很有价值，因为它们揭示了可以直接测量的生物系统的新方面。但是，这些新数据的真正潜力，使我们对人类生物学和医学的理解取得根本性进展，可能在于它的使用推断生物系统的动态：这些生物状态变化的速度有多快，以及什么条件或干预措施控制这些变化率？生物动力学的理解已被证明是必不可少的医疗诊断和治疗的不同领域。例如，揭示白色血细胞动力学的实验指导了艾滋病毒治疗的早期成功，而血红蛋白糖化率和血细胞周转率的知识对于目前管理糖尿病患者的最佳实践至关重要。动力学和动力学不能直接测量，必须使用计算和数学建模来推断。由于很少有临床知情的研究人员具有必要的数学和计算专业知识，人类生物学和疾病的大多数动态方面仍然知之甚少，患者无法从这种动态理解所能实现的基本诊断和预后见解中获益。我将开发一个临床知情的数学和计算框架，以推断在人体内使用常规可用的细胞群体特征的整体测量细胞病理生理过程的动态。我将把建模框架应用于所有血细胞谱系，包括淋巴细胞、中性粒细胞、红细胞和血小板，并将揭示对代表性疾病类型的见解和应用，包括癌症（白血病）、感染（败血症）和自身免疫性疾病（特发性血小板减少性紫癜）。我将综合现有的科学和临床知识，将病理生理学转化为数学模型，描述细胞出生、死亡、流入和流出的速率，以及这些速率如何在个体患者之间和患者细胞群体内作为细胞大小、年龄、核复杂性和其他单细胞特征的函数而变化。然后，我将比较健康个体和疾病患者的模型参数轨迹，以揭示疾病机制的新细节以及它们及其治疗引起的病理反应。由于数学模型的结构是由目前的病理生理学知识，模型参数代表个性化的量化重要的稳态过程，并提供新的概念见解人类病理生理学。由于模型是使用常规可用的临床测量值构建的，因此这些见解通常可以立即翻译。公共卫生相关性：该提案开发了一个新的基于机制的建模框架，该框架将使用现有的临床实验室测试为包括癌症，感染和自身免疫在内的一系列疾病提供更早，更准确和个性化的诊断和治疗监测。