Systems biology of quiescence entry

进入静止的系统生物学

基本信息

批准号：
10661143
负责人：
Orlando Argüello-Miranda
金额：
$ 24.9万
依托单位：
NORTH CAROLINA STATE UNIVERSITY RALEIGH
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-01 至 2025-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10661143
关键词：
Algorithms Animal Model Binding Biochemical Biochemistry Bioinformatics Biological Assay Biological Markers Carbon Cell Cycle Cell Cycle Arrest Cell Cycle Progression Cell Cycle Regulation Cell Proliferation Cell physiology Cells Cellular Structures Cellular biology Color Complement Computational Biology Computing Methodologies Core Facility Cyclic AMP Cyclic AMP-Dependent Protein Kinases DNA biosynthesis Data Decision Making Development Disease Environment Failure Fibrosis Goals Histone Acetylation Human Image Immunoprecipitation In complete remission Institution Label Lead Machine Learning Malignant Neoplasms Mass Spectrum Analysis Measures Meiosis Mentors Metabolic Methods Microfluidics Microscopy Molecular Nitrogen Nuclear Nutrient Nutrient Depletion Organ Organism Pathway interactions Pattern Phase Phosphotransferases Positioning Attribute Problem Solving Process Property Protein Biosynthesis Proteins Publishing Reporter Research Role Saccharomyces cerevisiae Signal Pathway Signal Transduction Source Starvation Stress Systems Biology Techniques Testing Time Series Analysis Tissues Training Transcription Repressor Work XBP1 gene anaphase-promoting complex base biological adaptation to stress career cellular imaging computational platform computer framework design detection of nutrient experimental study fluorescence imaging gene repression genetic approach imaging approach imaging system in vivo prevent response single cell analysis temporal measurement tool yeast genetics

项目摘要

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.

抽象的该建议旨在为候选人的长期职业计划提供关键的培训，以研究细胞静止是通过决策过程建立的。决定静止的决定压力或发育信号是生活系统的基本且知识的特性。无法保持静止会导致人类（例如纤维化或癌症）的细胞增殖疾病。当多个营养和应力感应信号通路阻止细胞周期时，静止进入机械。但是，与细胞周期协调应力响应途径的分子机制在静止期间，在很大程度上不清楚。这部分是由于难以简单地量化多个体内单细胞水平的应力途径。为了解决这一限制，候选人将使用微流体 - 荧光成像系统，该系统仅在从扩散到静止。使用这种方法，应力反应与细胞周期之间的协调在模型生物体中，可以用前所未有的临时分辨率来量化机械。一个基于机器学习和时间序列分析的计算平台将用于处理大型仅在单个细胞中仅跟踪六个生物标志物而得出的成像数据。这个的初始版本框架发现，在静止的开始期间，配置的DNA复制激酶的核水平 CDC7受动态调节。这种方法还确定了应力激活的核水平转录复制XBP1定义了在静止输入期间如何停止细胞周期。结合这个使用生化技术的计算方法将确定分子机制通过调节应力反应和细胞周期机制来建立细胞静止。候选人是在本提案的K99阶段获得计算生物学的关键培训，以补充他以前在生物化学，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导者Gaudenz Danuser博士的指导，他的实验室开发高级机器学习和时间序列分析以研究细胞信号转导。该提议利用承诺整个生物信息学核心设施和UTSW世界一流研究机构的培训环境。建立独特的计算和成像框架，并结合生化方法静止研究将支持候选人向独立研究学术职位的过渡，并将导致发现静止和细胞周期调节的生物医学相关原理。