Systems biology of quiescence entry

摘要 这项提议旨在为候选人的长期职业计划提供关键的培训，以研究细胞如何 宁静是通过决策过程建立起来的。作为回应，决定进行沉默 应激或发育信号是生命系统的一个基本而未被研究的特性。未能做到 保持安静可能会导致人类细胞增殖障碍，如纤维化或癌症。 当多个营养和胁迫感应信号通路阻止细胞周期时，静止期进入被触发 机械设备。然而，协调应激反应途径与细胞周期的分子机制 静默期间的情况在很大程度上仍不清楚。这在一定程度上是由于同时量化多个 体内单细胞水平的应激途径。为了解决这一限制，候选人将使用微流体- 荧光成像系统可同时跟踪多达六条不同的路径 增殖进入静止状态。使用这种方法，应激反应和细胞周期之间的协调 在模式生物酿酒酵母中，机器可以以前所未有的时间分辨率进行量化。一个 将使用基于机器学习和时间序列分析的计算平台来处理大型 通过在单个细胞中同时跟踪六个生物标记物而获得的成像数据。这是它的初始版本 Frame发现，在静止开始期间，保守的DNA复制激酶的核水平 CDC7是动态调节的。这一方法还发现，应激激活的核水平 转录抑制因子XBP1定义了细胞周期在静止期是如何停止的。将这一点结合起来 结合生化技术的计算方法将确定 通过调节应激反应和细胞周期机制来建立细胞静止。 候选人将在本提案的K99阶段获得关键的计算生物学培训，以 补充他之前在生物化学、细胞生物学和酵母遗传学方面的培训。候选人将是 由计算生物学领域的领军人物Gaudenz Danuser博士指导，他的实验室开发了先进的机器 学习和时间序列分析来研究细胞信号转导。这项提议利用了承诺 整个生物信息学核心设施和UTSW世界级研究机构的培训环境。 建立独特的计算和成像框架，与生化方法相结合 研究的静默，将支持候选人过渡到独立研究的学术职位 将导致发现与静止和细胞周期调节相关的生物医学原理。

Abstract This proposal aims to provide crucial training for the candidate’s long-term career plan to study how cellular quiescence is established through decision-making processes. The decision to undergo quiescence in response to stress or developmental signals is a fundamental and understudied property of living systems. Failure to maintain quiescence can lead to cell proliferation disorders in humans, such as fibrosis or cancer. Quiescence entry is triggered when multiple nutrient- and stress-sensing signaling pathways arrest the cell cycle machinery. However, the molecular mechanisms that coordinate stress response pathways with the cell cycle during quiescence remain largely unclear. This is, in part, due to the difficulties to simultaneously quantify multiple stress pathways at the single cell level in vivo. To solve this limitation, the candidate will use a microfluidics- fluorescent imaging system that tracks up to six different pathways simultaneously during the transition from proliferation into quiescence. Using this approach, the coordination between stress responses and the cell cycle machinery can be quantified with unprecedented temporal resolution in the model organism S. cerevisiae. A computational platform based on machine learning and time series analysis will be used to process the large imaging data derived from tracking six biomarkers simultaneously in single cells. An initial version of this framework found that during the onset of quiescence the nuclear levels of the conserved DNA-replication kinase Cdc7 are dynamically regulated. This approach also identified that the nuclear levels of the stress-activated transcriptional repressor Xbp1 define how the cell cycle is stopped during quiescence entry. Combining this computational approach with biochemical techniques will determine the molecular mechanisms for the establishment of cellular quiescence by modulation of stress responses and the cell cycle machinery. The candidate is to acquire crucial training in computational biology during the K99 phase of this proposal to complement his previous training in biochemistry, cell biology and yeast genetics. The candidate will be mentored by a leader in computational biology Dr. Gaudenz Danuser, whose lab develops advanced machine learning and time series analysis to study cellular signal transduction. This proposal harnesses the commitment of an entire bioinformatics core facility and the training environment of a world-class research institution at UTSW. Establishing a unique computational and imaging framework, combined with biochemical approaches for the study of quiescence, will support the candidate’s transition to an independent research academic position and will lead to the discovery of biomedically relevant principles of quiescence and cell cycle regulation.