Martegani E, Vanoni M, Delia D
Cytometry. 1984 Jan;5(1):81-5. doi: 10.1002/cyto.990050112.
Flow cytometry gives relevant data on cellular parameters such as DNA, RNA, and protein contents of individual cells and is therefore a powerful tool for analyzing microbial population dynamics. Relevant information about growth dynamics may be obtained from protein distribution. In fact, protein distribution is related to age distribution and depends on the law of growth of the population and the law of growth of the single cell. To extract the available information from protein distribution, we developed a computer algorithm starting from a model for growth of Saccharomyces cerevisiae. This algorithm quantitatively fits experimental protein distributions, allows a deconvolution of these distributions, and thus yields information about temporal parameters of the cell cycle and structure of yeast populations.
流式细胞术可提供有关单个细胞的DNA、RNA和蛋白质含量等细胞参数的相关数据,因此是分析微生物群体动态的有力工具。有关生长动态的相关信息可从蛋白质分布中获得。事实上,蛋白质分布与年龄分布相关,并且取决于群体的生长规律和单细胞的生长规律。为了从蛋白质分布中提取可用信息,我们从酿酒酵母生长模型出发开发了一种计算机算法。该算法能定量拟合实验蛋白质分布,对这些分布进行反卷积,从而得出有关细胞周期时间参数和酵母群体结构的信息。