III: Small: Algorithmic Techniques for Determining Alterations in the Patterns of Chromosome Spatial Organization inside the Cell Nucleus

III：小：确定细胞核内染色体空间组织模式改变的算法技术

• 批准号: 1422591
• 负责人: Jinhui Xu
• 金额: $ 50万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422591&HistoricalAwards=false
• 关键词: III; Small; Algorithmic; Techniques; Determining

Recent research in cell biology has led to the emerging view that the 3-D arrangement of chromosome territories (CT) and the spatial positioning of genes within these territories are linked to genomic function and regulation. Despite this progress, it is not clear what is the spatial organization patterns of CTs inside the cell nucleus and how such patterns dynamically change during the cell cycle and at different stages of cell differentiation and cancer progression. To facilitate more in-depth studies of these important biological problems, in this project, the Pi will develop a set of efficient algorithmic techniques for determining the patterns and their alterations of three chromosome organization problems. First, how are chromosomes spatially positioned inside the nucleus? Second, how are chromosomes neighboring or associating with each other? Third, what is the internal structure of each individual chromosome? The core of this project is to develop efficient algorithms for solving a set of challenging computational problems which are essential for the chromosome organization problems, thus addressing the emerging science at the interface of computing and biology. This project will bring research and educational opportunities to both graduate and undergraduate students. It will involve several Ph.D. students (including 2 female students), and one or two undergraduate students, from both the Computer Science and Engineering Department and Biological Sciences Department. As an integral part of this project, a new course in biomedical imaging will be developed that will enable the solving of biomedical problem with a knowledge of computer science.This project will yield a set of efficient algorithmic techniques for the proposed problems, and will be used as automatic (or semi- automatic) tools to accurately determine the patterns of chromosome spatial organization inside the cell nucleus and how such patterns dynamically change during the normal cell cycle, during differentiation of keratinocytes into skin cells and following progression of normal breast cells to malignant cancer. This could provide new insight into how the global arrangement of chromosomes in the cell nucleus is related to the malignant state. The set of algorithms will be implemented and tested using randomly generated data and real biological data. They will be integrated into an Algorithmic Toolbox developed in our previous research on the spatial positioning of the cell nucleus, and will be made available to the research community. (3) Results from this projects are likely to be used in other areas, and have a positive impact on them. For example, the problems of k-prototype learning, chromatic median, chromatic k-median clustering, median point-sets, and projective clustering are all fundamental problems in computer science and have applications in many other areas, such as machine learning, computer vision, and data mining. Some of the solutions can be used as information integration tools.

细胞生物学的最新研究导致了新兴观点，即染色体领土（CT）的3-D排列以及这些领土内基因的空间定位与基因组功能和调节有关。尽管取得了这种进步，但尚不清楚细胞核内CT的空间组织模式是什么，以及在细胞周期和细胞分化和癌症进展的不同阶段，这种模式如何动态变化。为了促进对这些重要生物学问题的更深入的研究，在该项目中，PI将开发一系列有效的算法技术来确定模式及其对三个染色体组织问题的改变。 首先，染色体如何在核内空间位置？ 其次，染色体如何相邻或相互关联？第三，每个染色体的内部结构是什么？该项目的核心是开发有效的算法来解决一系列具有挑战性的计算问题，这些问题对于染色体组织问题至关重要，从而解决了计算和生物学界面上新兴的科学。 该项目将为研究生和本科生带来研究和教育机会。它将涉及多个博士学位。来自计算机科学和工程系和生物科学系的学生（包括2名女学生）和一两个本科生。 As an integral part of this project, a new course in biomedical imaging will be developed that will enable the solving of biomedical problem with a knowledge of computer science.This project will yield a set of efficient algorithmic techniques for the proposed problems, and will be used as automatic (or semi- automatic) tools to accurately determine the patterns of chromosome spatial organization inside the cell nucleus and how such patterns dynamically change during the normal cell cycle,在分化角质形成细胞为皮肤细胞和正常乳腺细胞发展为恶性癌的过程中。这可以提供有关细胞核中染色体的整体排列如何与恶性状态有关的新见解。 该算法集将使用随机生成的数据和实际生物学数据实现和测试。它们将被整合到我们先前关于细胞核空间定位的研究中开发的算法工具箱，并将提供给研究社区。 （3）该项目的结果可能在其他领域使用，并对它们产生积极影响。例如，K-原型学习，色谱中位数，色度K-Median聚类，中值点集和投射聚类的问题都是计算机科学中的基本问题，并且在许多其他领域都有应用，例如机器学习，计算机视觉和数据挖掘。某些解决方案可以用作信息集成工具。