Evolution of cell cycle control: triangulating the last eukaryotic common ancestor

项目摘要 细胞周期控制在真核生物王国和发芽酵母模型系统中高度保守 一直是适用于人类发展和疾病问题的重大见解的来源。这项建议 使用该模型系统继续真核细胞周期的系统级分析。 细胞周期的时钟功能具有高可靠性和低噪音，即使单个组件和 组成时钟的电路经常被认为是高度可变的。例如，基因表达是 已知在单个细胞之间高度可变，但细胞周期调节的基因表达可能是 在定时和幅度方面高度可靠。对Cyclin-CDK活性水平上升的阈值反应可以 提供类似开关的行为，但这种开关经常以高度可变的起始时间为代价； 整个细胞周期控制电路避免了这种可变性。我们正在追求一种新兴的概念，即 独立振荡器有助于细胞周期控制；而非耦合振荡器导致高度可变 和不规则的细胞周期事件序列，我们认为 以独立振荡器为中心的Cyclin-CDK振荡器可以产生一个健壮而准确的整体系统。 这项提议延续了我们对多细胞定量延时荧光显微镜的创新使用 周期时间尺度，结合半自动图像分析和深入的遗传和定量 分析以推动对细胞周期控制的系统水平的理解。 我们正在开发新的数学建模方法。生物学上迫切需要一种简单但 可以揭示基本控制原理的实验约束模型。挑战是找到最多的 阐明了连接到生物现实所需的细节和模型简单性之间的平衡 透明度和洞察力所必需的。我们正在探索使用几何、低维的方法 细胞周期控制网络的表示仍然可以在实验上受到限制，这将产生 可验证的预测。

Project Summary Cell cycle control is highly conserved through the eukaryotic kingdom, and the budding yeast model system has been the source of major insights applicable to issues in human development and disease. This proposal continues systems-level analysis of the eukaryotic cell cycle using this model system. The cell cycle `clock' functions with high reliability and low noise, even though individual components and circuits making up the clock are frequently known to be highly variable. For example, gene expression is known to be highly variable between individual cells, and yet cell-cycle-regulated gene expression can be highly reliable with respect to timing and amplitude. Threshold responses to rising cyclin-Cdk activity levels can provide switch-like behavior, but such switches can frequently come at the cost of highly variable onset time; the overall cell cycle control circuitry avoids this variability. We are pursuing an emerging concept of multiple independent oscillators contributing to cell cycle control; while uncoupled oscillators result in highly variable and irregular sequences of cell cycle events, we propose that coupling (`phase-locking') of otherwise independent oscillators to the central cyclin-Cdk oscillator can yield a robust and accurate overall system. This proposal continues our innovative use of quantitative time-lapse fluorescence microscopy, over multi-cell cycle timescales, combined with semi-automated image analysis and in-depth genetic and quantitative analysis to drive systems-level understanding of cell cycle control. We are developing new methods of mathematical modeling. There is a pressing need in biology for simple but experimentally constrained models that can reveal basic control principles. The challenge is to find the most illuminating balance between the detail required for a connection to biological reality, and model simplicity required for transparency and insight. We are exploring methods to use geometrical, low-dimensionality representations of the cell cycle control network that can still be experimentally constrained, and that will yield testable predictions.