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

最近的细胞生物学研究导致了一种新的观点，即染色体区域(CT)的三维排列和这些区域内基因的空间定位与基因组的功能和调节有关。尽管取得了这些进展，但目前尚不清楚CTs在细胞核内的空间组织模式是什么，以及这种模式在细胞周期以及细胞分化和癌症进展的不同阶段如何动态变化。为了便于更深入地研究这些重要的生物学问题，在这个项目中，PI将开发一套高效的算法技术来确定三个染色体组织问题的模式及其变化。首先，染色体在细胞核内的空间位置是怎样的？第二，染色体之间是如何相邻或关联的？第三，每个个体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是必不可少的，从而解决计算和生物学交界处的新兴科学。该项目将为研究生和本科生带来研究和教育机会。它将涉及来自计算机科学与工程系和生物科学系的几名博士生(包括两名女学生)和一到两名本科生。作为该项目的一部分，将开发一门新的生物医学成像课程，该课程将利用计算机科学知识解决生物医学问题。该项目将为所提出的问题产生一套有效的算法技术，并将被用作自动(或半自动)工具，准确地确定细胞核内染色体空间组织的模式，以及这些模式在正常细胞周期、角质形成细胞分化为皮肤细胞期间以及正常乳腺细胞向恶性肿瘤进展过程中如何动态变化。这可能为了解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。这套算法将使用随机生成的数据和真实的生物数据进行实施和测试。它们将被整合到我们之前对细胞核空间定位研究中开发的算法工具箱中，并将提供给研究界使用。(3)该项目的成果有可能应用于其他领域，并对其产生积极影响。例如，k-原型学习问题、色中值问题、色k-中值聚类问题、中值点集问题和射影聚类问题都是计算机科学中的基本问题，在机器学习、计算机视觉和数据挖掘等许多领域都有应用。其中一些解决方案可以作为信息集成工具。

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.