Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Computational Methods for Estimation of Cell Cycle Phase Distributions of Yeast Cells | Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2007 Article ID 46150 9 pages doi 2007 46150 Research Article Computational Methods for Estimation of Cell Cycle Phase Distributions of Yeast Cells Antti Niemisto 1 Matti Nykter 1 Tommi Aho 1 Henna Jalovaara 2 Kalle Marjanen 1 Miika Ahdesmaki 1 Pekka Ruusuvuori 1 Mikko Tiainen 2 Marja-Leena Linne 1 and Olli Yli-Harja1 1 Institute of Signal Processing Tampere University of Technology . Box 553 33101 Tampere Finland 2 MediCel Ltd. Haartmaninkatu 8 00290 Helsinki Finland Received 30 June 2006 Revised 5 March 2007 Accepted 17 June 2007 Recommended by Yidong Chen Two computational methods for estimating the cell cycle phase distribution of a budding yeast Saccharomyces cerevisiae cell population are presented. The first one is a nonparametric method that is based on the analysis of DNA content in the individual cells of the population. The DNA content is measured with a fluorescence-activated cell sorter FACS . The second method is based on budding index analysis. An automated image analysis method is presented for the task of detecting the cells and buds. The proposed methods can be used to obtain quantitative information on the cell cycle phase distribution of a budding yeast S. cerevisiae population. They therefore provide a solid basis for obtaining the complementary information needed in deconvolution of gene expression data. As a case study both methods are tested with data that were obtained in a time series experiment with S. cerevisiae. The details of the time series experiment as well as the image and FACS data obtained in the experiment can be found in the online additional material at http sgn csb yeastdistrib . Copyright 2007 Antti Niemisto et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is .
