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

细胞生物学的最新研究已经导致了一种新兴的观点，即染色体区域（CT）的3-D排列和这些区域内基因的空间定位与基因组功能和调控有关。尽管取得了这一进展，但尚不清楚细胞核内CT的空间组织模式是什么，以及这些模式在细胞周期期间以及在细胞分化和癌症进展的不同阶段如何动态变化。为了促进对这些重要生物学问题的更深入研究，在这个项目中，Pi将开发一套有效的算法技术，用于确定三个染色体组织问题的模式及其改变。 首先，染色体在细胞核内的空间位置是怎样的？ 第二，染色体是如何相邻或相互关联的？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，从而解决计算和生物学界面上的新兴科学。 该项目将为研究生和本科生带来研究和教育机会。这将涉及多个博士学位。学生（包括2名女生）和1至2名本科生，来自计算机科学与工程系和生物科学系。作为该项目的一个组成部分，将开发一门新的生物医学成像课程，使学生能够利用计算机科学知识解决生物医学问题。该项目将为提出的问题提供一套有效的算法技术，并将作为自动（或半自动）准确确定细胞核内染色体空间组织模式以及这些模式在正常细胞周期中如何动态变化的工具，在角质形成细胞分化成皮肤细胞期间以及在正常乳腺细胞进展为恶性肿瘤之后。这可以为细胞核中染色体的整体排列与恶性状态的关系提供新的见解。 将使用随机生成的数据和真实的生物数据实施和测试算法集。它们将被整合到我们以前关于细胞核空间定位的研究中开发的一个计算机程序中，并将提供给研究界。(3)这些项目的成果很可能用于其他领域，并对这些领域产生积极影响。例如，k-原型学习、色中值、色k-中值聚类、中值点集和投影聚类问题都是计算机科学中的基本问题，并且在许多其他领域中有应用，例如机器学习、计算机视觉和数据挖掘。有些解决方案可以用作信息集成工具。

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.