The contribution of phenotypic variability to chemotactic performance

表型变异对趋化性能的贡献

• 批准号: 8827380
• 负责人: Thierry Emonet
• 金额: $ 23.67万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8827380
• 关键词: Accounting; Architecture; Bacteria; Behavior; Behavioral; Biological Models; Biological Process; Cell Extracts; Cell model; Cell physiology; Cells; Characteristics; Chemicals; Chemotaxis; Complex; Controlled Environment; Development; Diffuse; Diffusion; Disease Progression; Ensure; Environment; Escherichia coli; Exploratory Behavior; Gene Expression; Geometry; Goals; Growth; Health; Heterogeneity; Immune response; Individual; Infection; Investigation; Label; Length; Life; Malignant Neoplasms; Maps; Measurement; Measures; Medicine; Mining; Modeling; Molecular; Noise; Outcome; Pathway interactions; Performance; Phenotype; Population; Population Distributions; Positioning Attribute; Prevention strategy; Probability; Property; Proteins; Regulatory Pathway; Relative (related person); Research; Role; Running; Salmonella; Shapes; Signal Pathway; Signal Transduction; Structure; Swimming; System; Time; Work; base; biological systems; cell growth; computer framework; design; disorder prevention; fitness; network architecture; predictive modeling; protein distribution; protein expression; protein function; research study; response; simulation; statistics; success; theories; trait; treatment strategy

DESCRIPTION (provided by applicant): Phenotypic variability, a fundamental property of isogenic cell populations across all biological systems, creates challenges for medicine because it diversifies individual cell responses to treatments by introducing outliers that survive and resume the progression of the disease. So far, the mechanisms of phenotypic variability have been primarily studied in the context of developmental or regulatory pathways that produce discrete outcomes. The long-term goal of this project is to understand how complex dynamical functions can be tuned over a continuum by taking advantage of fluctuations in protein abundance. This project will build a quantitative understanding of the mechanisms and functional role of phenotypic variability for cellular dispersion and navigation using the well-characterized chemotaxis systems of E. coli and Salmonella as model systems. The central hypothesis is that cell-to-cell variability can resolve performance trade-offs of a single signaling pathway by creating individual cells with different capabilities, ensuring that subpopulations of cells will perform optimally in various environments and tasks. The experimental plan builds on a theoretical and computational framework established in previous works. An experimental platform recently developed in our lab allows for measurement of the trajectories of swimming cells and the abundance of fluorescently labeled proteins in the same live individual as they navigate controlled environments. Preliminary results indicate that quantitative relationships between protein quantity, behaviors, and chemotactic performance can be established at the single-cell level throughout the population. Iterative predictive modeling and experiments will extend current quantitative models to capture the cause and consequences of cell-to-cell variability on chemotactic behavior and population structure. Aim 1 will map distributions of chemotaxis protein levels onto distributions of individul diffusive behaviors and individual performance in exploratory or invasive tasks. Aim 2 will map chemotaxis protein abundance to chemotactic performance in static and time-varying chemical gradients to reveal the consequences of cell-to-cell variability for tracking various gradients. Ai 3 will characterize the trade-offs faced by individual cells in performing chemotaxis and examine whether cell-to-cell variability can alleviate these trade-offs at the population level. The experimental and theoretical framework developed for this project will have a broad impact on a fundamental challenge: to go beyond the characterization of average signaling network performance and to predict the consequences of fluctuations in molecular parameters on single-cell dynamical behaviors.

描述(由申请人提供)： 表型可变性是所有生物系统中同基因细胞群体的一个基本属性，它给医学带来了挑战，因为它通过引入存活并恢复疾病发展的异常值，使个体细胞对治疗的反应多样化。到目前为止，表型变异的机制主要是在产生离散结果的发育或调节途径的背景下进行研究的。这个项目的长期目标是了解如何通过利用蛋白质丰度的波动来调整复杂的动力学功能。这个项目将使用特征良好的大肠杆菌和沙门氏菌趋化系统作为模型系统，建立对表型变异对细胞扩散和导航的机制和功能作用的定量理解。中心假设是，细胞间的可变性可以通过创造具有不同能力的单个细胞来解决单一信号通路的性能权衡，确保细胞亚群在各种环境和任务中表现最佳。该实验计划建立在以前工作建立的理论和计算框架的基础上。我们实验室最近开发的一个实验平台允许测量游泳细胞的轨迹和同一个活着的个体在受控环境中导航时荧光标记的蛋白质的丰度。初步结果表明，可以在整个种群的单细胞水平上建立蛋白质数量、行为和趋化性能之间的定量关系。迭代预测建模和实验将扩展当前的定量模型，以捕捉细胞间差异对趋化行为和种群结构的原因和后果。目的1将趋化蛋白水平的分布映射到个体扩散行为的分布和个体在探索性或侵入性任务中的表现。目标2将在静态和时变的化学梯度中将趋化蛋白丰度映射到趋化性能，以揭示细胞间差异对跟踪不同梯度的影响。AI 3将描述单个细胞在执行趋化作用时所面临的权衡，并检查细胞间的可变性是否可以在种群水平上缓解这些权衡。为该项目开发的实验和理论框架将对一个基本挑战产生广泛影响：超越平均信令网络性能的表征，并预测分子参数波动对单细胞动力学行为的影响。