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.
