The contribution of phenotypic variability to chemotactic performance

表型变异对趋化性能的贡献

• 批准号: 8482056
• 负责人: Thierry Emonet
• 金额: $ 26.72万
• 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 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8482056
• 关键词: Accounting; Architecture; Bacteria; Behavior; Behavioral; Biological Models; Biological Process; Cell Extracts; Cell model; Cell physiology; Cells; Characteristics; Chemicals; Chemotaxis; Complex; Computer Architectures; Controlled Environment; Development; Diffuse; Diffusion; Disease Progression; Ensure; Environment; Escherichia coli; Exploratory Behavior; Gene Expression; Goals; Growth; 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; Simulate; Structure; Swimming; System; Time; Work; base; biological systems; cell growth; computer framework; design; disorder prevention; fitness; predictive modeling; protein distribution; protein expression; protein function; public health relevance; 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）： 表型变异性是所有生物系统中同基因细胞群的基本特性，它给医学带来了挑战，因为它通过引入存活并恢复疾病进展的离群值来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控途径的背景下研究的。该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在连续统中调整复杂的动力学功能。本项目将建立一个定量的理解的机制和功能作用的表型变异的细胞分散和导航使用良好的特点趋化系统的大肠杆菌。大肠杆菌和沙门氏菌作为模型系统。核心假设是，细胞间变异性可以通过创建具有不同能力的单个细胞来解决单个信号传导途径的性能权衡，确保细胞亚群在各种环境和任务中表现最佳。实验计划建立在以前的作品中建立的理论和计算框架。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荧光标记蛋白质在同一个活个体中的丰度，因为它们在受控环境中航行。初步结果表明，蛋白质的数量，行为和趋化性能之间的定量关系，可以建立在整个人口的单细胞水平。迭代预测建模和实验将扩展当前的定量模型，以捕获细胞间差异对趋化行为和群体结构的原因和后果。目的1将趋化蛋白水平的分布映射到个体扩散行为和个体在探索性或侵入性任务中的表现。目标2将映射趋化性蛋白质丰度的趋化性能在静态和随时间变化的化学梯度，以揭示细胞到细胞的变化跟踪各种梯度的后果。Ai 3将描述单个细胞在执行趋化性时所面临的权衡，并检查细胞间的变异性是否可以在群体水平上减轻这些权衡。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的影响：超越平均信号网络性能的表征，并预测分子参数波动对单细胞动力学行为的影响。