Stochastic Start in the budding yeast cell cycle

芽殖酵母细胞周期的随机开始

• 批准号: 7155140
• 负责人: Jan Skotheim
• 金额: $ 4.6万
• 依托单位: ROCKEFELLER UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-15 至 2008-07-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7155140
• 关键词: Saccharomyces; biomarker; cell cycle; cell growth regulation; cell morphology; fluorescence microscopy; fluorescent dye /probe; genetic manipulation; mathematical model; messenger RNA; model design /development; mutant; postdoctoral investigator; protein localization; single cell analysis; time resolved data; yeasts

DESCRIPTION (provided by applicant): Variability in the budding yeast cell cycle: single cell experiments and stochastic network models. The variability within a clonal population of budding yeast is often larger than the variability associated with genetic differences. Fluctuations, presumably due to the low numbers of mRNA of several key cell cycle regulators, go unnoticed in datasets produced by studying aggregate contents of a population of cells. Only through single cell in vivo studies does such variation become apparent. Although the scale of variability on the single cell level and the fitness cost associated with disordered progression through the cell cycle suggest that cells are adapted to cope with molecular fluctuations, the mechanisms employed are currently unknown. With my mathematical expertise and the experimental capabilities of Fred Cross' yeast biology lab at the Rockefeller University we are well poised to tackle this question. I aim to use time series of fluorescent markers of key cell cycle regulators from single cell experiments to design a new stochastic network model of the G1/S transition responsible for size control in budding yeast. Close collaboration with experimental biologists will allow for rapid integration of new data and for theory to influence experimental design.

描述(由申请人提供)：发芽酵母细胞周期的可变性：单细胞实验和随机网络模型。发芽酵母克隆群体内的变异性通常大于与遗传差异相关的变异性。波动，可能是由于几个关键的细胞周期调节因子的mRNA数量较少，在通过研究一组细胞的聚集内容产生的数据集中没有被注意到。只有通过体内单细胞研究，这种变异才变得明显。尽管单细胞水平上的可变性程度和与细胞周期中无序进展相关的适应成本表明，细胞能够适应分子波动，但目前所采用的机制尚不清楚。凭借我的数学专业知识和洛克菲勒大学弗雷德·克罗斯的酵母生物学实验室的实验能力，我们已经做好了解决这个问题的准备。我的目的是利用单细胞实验中关键细胞周期调节因子的荧光标记的时间序列，设计一个新的负责芽期酵母大小控制的G1/S转变的随机网络模型。与实验生物学家的密切合作将允许快速整合新数据，并允许理论影响实验设计。