Quantitative Modeling of Signal Transduction in Bacterial Chemotaxis

描述（由申请人提供）：本研究项目的长期目标是实现对大肠杆菌趋化性信号转导途径的定量，系统级理解。我们希望将不同（长度和时间）尺度上大肠杆菌趋化信号通路的知识整合到系统的数学描述（模型）中，该系统可用于定量解释和预测大肠杆菌对任何给定时间和空间信号（刺激）的趋化反应。这些模型将基于已知的信号传导途径的分子细节，并以与实验数据相当的适当分辨率构建。这些模型将通过统计物理方法、蒙特卡罗模拟和动力系统分析来研究。这些模型的结果将用于解释现有数据，做出可测试的预测，并与实验数据进行比较，以反馈改进/完善模型。在本提案中，我们将重点关注大肠杆菌趋化途径的两个基本方面：1)（快速）激酶反应中的信号放大。我们感兴趣的是找出观察到的信号放大的结构基础，例如，每个合作功能复合体包含多少受体。我们想了解在大肠杆菌趋化性中观察到的宽动态范围高灵敏度的分子机制。我们想了解细胞如何在可变（嘈杂）内部元件的情况下实现这些优异的性能（高增益，宽背景范围内的高灵敏度）。（较慢）适应过程的动力学。我们希望定量地了解适应动力学，例如，系统适应的速度有多快，适应时间如何取决于外部刺激强度。我们想要了解对时变刺激的适应动力学，例如不同斜坡速率的指数斜坡。最终，我们希望能够模拟和预测细胞在自然环境中运动时的信号通路动力学。

DESCRIPTION (provided by applicant): The long term goal of this research project is to achieve quantitative, systems level understanding of the signal transduction pathway in E. coli chemotaxis. We want to integrate the knowledge on the E. coli chemotaxis signaling pathway over different (length and time) scales into a mathematical description (model) of the system that can be used to explain and predict quantitatively the E. coli chemotaxis response to any given temporal and spatial signal (stimulus). The models will be constructed based on known molecular details of the signaling pathway and at the appropriate resolution comparable to experimental data. These models will be studied by using statistical physics methods, Monte Carlo simulation and dynamical systems analysis. The results from these models will be used to explain existing data, make testable predictions and the comparison with experimental data will feed back to improve/refine the models. In this proposal, we will focus on two essential aspects of the E. coli chemotaxis pathway: 1) Signal amplification in the (fast) kinase response. We are interested in finding out the structural basis for the observed signal amplification, e.g., how many receptors each cooperative functional complex contains. We want to understand the molecular mechanism for the wide dynamic range of high sensitivity observed in E. coli chemotaxis. We want to understand how cell achieve these excellent properties (high gain, high sensitivity over a wide range of backgrounds) with variable (noisy) internal components. 2) Kinetics of the (slower) adaptation process. We want to understand the adaptation kinetics quantitatively, e.g., how fast the system adapts and how the adaptation time depends on the external stimulus strength. We want to understand the adaptation kinetics to time varying stimulus, such as exponential ramps with different ramp rates. Eventually, we want to be able to model and predict the signaling pathway dynamics as the cell moves in its natural environment. The concepts and tools developed in the quantitative, systems level modeling of a complete sensory signal transduction pathway will be useful in understanding signaling pathways and sensory systems in higher organisms, including human. The molecular level understanding of the bacterial chemotaxis pathway is important to study the role of bacterial pathogens in human health.