Center for Quantitative Biology

描述(由申请人提供)：拟议的数量生物学中心的首要目标仍然是在普林斯顿建立一个充分满足计算和基因组学进步带来的挑战和机遇的研究和教学环境，以实践有用的定量生物学，有时被称为“系统生物学”。该中心的计划和基础设施增加了来自不同学科和部门(包括分子生物学、生态学和进化生物学、计算机科学、化学和物理学)的研究人员之间的交流带宽。在过去四年中，中心支持中心发表的论文中，有四分之一(54/217)是两名或更多中心教职员工联合发表的。对于本科生和研究生，该中心提供了一个多学科的教和学的重点；从一开始就整合了定量和生物学的思想，结果是学生在生物和定量思维方面获得了几乎同等的能力。该中心的具体目标是：(1)开发现实和定量的生物过程模型；(2)收集全面描述生物过程的大规模数据集；(3)设计新的和改进的方法，用于复杂模型、结构和数据的计算分析和显示，并在出版后公开所有潜在的数据、算法和分析系统；(4)为本科生、研究生和更大的科学界设计和支持新的定量生物学教育课程和课程；以及(5)将这些想法简化为在几个协作和多学科项目中的实践，每个项目都针对(I)细胞内信号、(Ii)多细胞生物体中的模式和细胞信号、(Iii)宿主-病原体相互作用和(Iv)生物信息学和数据可视化等主题领域的具体系统级问题。这些项目共享共同的量化目标，需要共同的基础设施(即计算、微阵列、成像和代谢组学核心设施)，所有这些项目都受益于中心提供的智力协同和多学科合作。 公共卫生相关声明：基因组学和计算技术的进步导致了思考、理解和研究生物的新方法。革命性的诊断和治疗癌症等复杂疾病的可能性已经成为可能。实现这些可能性需要普林斯顿中心提供的多学科环境、技术资源和完全定量的生物学教育。

DESCRIPTION (provided by applicant): The overarching goal of the proposed Center for Quantitative Biology remains to instantiate at Princeton a research and teaching environment that fully meets the challenge and opportunity, presented by advances in computation and genomics, to practice a usefully quantitative biological science, sometimes referred to as "systems biology". The programs and infrastructure of the Center increase the bandwidth of communication among researchers from different disciplines and departments (including Molecular Biology, Ecology and Evolutionary Biology, Computer Science, Chemistry and Physics). One in four (54/217) papers published with Center support in the last four years is a joint publication between two or more Center faculty. For both undergraduate and graduate students, the Center provides a focus for multidisciplinary teaching and learning; quantitative and biological ideas are integrated from the beginning, with the result that students acquire nearly equal facility in biological and quantitative thinking. The Center's specific aims are: (1) to develop realistic and quantitative models of biological processes; (2) to collect large-scale data sets that comprehensively describe biological processes; (3) to devise new and improved methods for computational analysis and display of complex models, structures and data and to make, upon publication, all underlying data, algorithms and analytical systems publicly accessible; (4) to devise and support new curricula and courses of quantitative biology education for undergraduates, graduate students, and the larger scientific community; and (5) to reduce these ideas to practice in several collaborative and multi-disciplinary projects, each aimed at specific system-level questions in the subject areas of (i) intracellular signaling, (ii) pattern and cell signaling in multicellular organisms, (iii) host-pathogen interactions and (iv) bioinformatics and data visualization. These projects share common quantitative goals, require a common infrastructure (i.e. computation, microarray, imaging and metabolomics core facilities) and all of them benefit from the intellectual synergy and multi-disciplinary cooperation that the Center provides. Public Health Relevance Statement: Advancing technology in genomics and computation has led to new ways of thinking, understanding, and studying living things. Revolutionary diagnostic and therapeutic possibilities for complex diseases like cancer have become possible. Realizing these possibilities requires the multidisciplinary environment, technical resources and a fully quantitative biological education that the Princeton Center provides.