NIH Director's Pioneer Award

NIH 院长先锋奖

• 批准号: 7687520
• 负责人: Arup K. Chakraborty
• 金额: $ 82.5万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-28 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7687520
• 关键词: Autoimmunity; Award; Biochemical; Cellular biology; Collaborations; Complex; Data; Development; Diabetes Mellitus; Discipline; Disease; Environment; Event; Image; Immune response; Immunologist; Length; Mediating; Molecular; Multiple Sclerosis; Process; Statistical Mechanics; T-Lymphocyte; Time; Tissues; United States National Institutes of Health; Work; genetic technology; predictive modeling; research study; success; therapy development

The development of a scientific field is often punctuated by a period when diverse observations can be understood in terms of a set of unifying principles. This important moment in time occurs when a critical number (and type) of observations becomes available, and the mechanistic principles often result from an amalgamation of ideas drawn from different intellectual disciplines. This period also marks the emergence of predictive models. T lymphocytes (T cells) orchestrate (and can also misregulate) the adaptive immune response. I believe that T cell biology, especially T cell-mediated autoimmunity, is at a critical juncture where diverse data will soon be integrated in terms of overarching principles. Modern experiments are revealing, in unprecedented detail, the factors that are important in the emergence of T cell-mediated autoimmunity rather than tolerance to ?self?. However, general mechanistic principles necessary for predictive models have proven elusive. This is because T cell-mediated autoimmunity is characterized by cooperative dynamic processes that occur over a spectrum of length and time scales. Phenomena occurring on large scales (tissues) influence cooperative molecular events in a single T cell which, in turn, influences the tissue environment. This complex hierarchical cooperativity makes it difficult to intuit underlying mechanisms from experimental observations alone. I propose to develop the principles governing T cell-mediated autoimmunity by parsing the pertinent cooperative dynamics which occur in a complex space of molecular and cellular variables by integrating three great advances of the twentieth century: statistical mechanics, computational technology, and genetic, biochemical, and imaging experiments. A key to success will be collaborations with experimental immunologists, and I have recently demonstrated how such synergies can be fruitful. If successful, the work that I envisage will provide the principles that could guide the development of therapies for diseases such as multiple sclerosis and diabetes which afflict millions of people.

一个科学领域的发展经常被一个时期打断，在这个时期，可以进行各种各样的观察。 是用一套统一的原则来理解的。这个重要的时刻发生在一个关键的数字 (and类型）的观察变得可用，而机械原理往往是从合并产生的。 来自不同知识领域的思想。这一时期也标志着预测模型的出现。 T淋巴细胞（T细胞）协调（也可以失调）适应性免疫反应。我相信T 细胞生物学，特别是T细胞介导的自身免疫，正处于一个关键时刻，各种数据将很快 在总体原则方面进行整合。现代实验以前所未有的细节揭示了 因素是重要的T细胞介导的自身免疫的出现，而不是耐受？自我？ 然而，预测模型所需的一般机械原理已被证明是难以捉摸的。这是因为T 细胞介导的自身免疫的特征在于在一系列免疫反应中发生的协同动态过程。 长度和时间尺度。在大尺度（组织）上发生的现象影响了一个系统中的合作分子事件。 单个T细胞，这反过来又影响组织环境。这种复杂的等级合作性使得 仅从实验观察很难直观地了解潜在的机制。我建议发展 通过解析相关的合作动力学来管理T细胞介导的自身免疫的原则， 一个复杂的分子和细胞变量空间，通过整合二十世纪的三大进展： 统计力学、计算技术、遗传学、生物化学和成像实验。的关键 成功将是与实验免疫学家的合作，我最近已经证明了这种合作是如何成功的。 协同增效可以产生成果。如果成功的话，我设想的工作将提供指导 开发治疗疾病的方法，如多发性硬化症和糖尿病，这些疾病折磨着数百万人。