Quantitative Modeling of Bacterial Chemotaxis Signaling Pathway

 描述(由申请人提供) 这一研究项目的长期目标是实现对系统水平的大肠杆菌趋化行为及其潜在的分子水平机制的定量理解。我们将根据趋化信号通路的结构和生化细节，建立蛋白质相互作用网络及其动力学的数学模型。这些模型将采用解析分析和数值模拟的方法进行研究。这些模型的结果将被用来解释实验数据并做出可检验的预测。模型和实验数据之间的迭代比较将被用于改进/改进模型。结合定量实验，这些预测模型允许我们测试不同的假设，以了解紧急生物行为的潜在分子机制。在这个方案中，我们将重点研究细菌趋化途径的两个基本方面：1)化学感受器簇的结构-功能关系。细菌化学感受器与接头蛋白Chew和组氨酸激酶CheA形成极性簇。通过利用化学受体簇的最新结构信息和功能测量，我们将开发一个基于结构的模型来研究化学信号是如何通过异质蛋白质簇传播的，以及信号如何被大型扩展的化学受体阵列放大。2)细菌鞭毛马达的信号整合与适应。细菌鞭毛马达由大约20种不同类型的 蛋白质。它可以感知细胞内的化学信号(Chey-P)，并相应地切换其旋转方向(Cw和CCW)。它还能“感应”机械信号，即负载，并产生相应的扭矩，以驱动负载以一定的角速度旋转。我们将开发一个完整的模型，在热力学一致的框架内描述电机的机械运动(旋转)和开关动力学。我们将使用这个集成模型来研究鞭毛电机的开关动力学如何受到其机械环境(负载、扭矩)变化的影响。我们将在我们的模型中引入不同的反馈交互作用，以研究最近观察到的运动适应外部化学和机械信号的可能来源。模型预测将通过实验测量进行验证，以确定运动适应的分子机制。综上所述，我们计划调查和了解多组分蛋白质复合体(如化学感受器簇和鞭毛马达)中的不同蛋白质如何共同工作来感知、响应和适应不同的(化学和/或物理)信号。

 DESCRIPTION (provided by applicant) The long term goal of this research project is to achieve quantitative understandings of system-level E. coli chemotaxis behaviors and their underlying molecular level mechanisms. We will develop mathematical models of protein interaction network and its dynamics based on structural and biochemical details of the chemotaxis signaling pathway. These models will be studied by using analytical analysis and numerical simulation methods. The results from these models will be used to explain experimental data and make testable predictions. The iterative comparison between models and experimental data will be used to improve/refine the models. Taken together with quantitative experiments, these predictive models allow us to test different hypotheses in order to understand the underlying molecular mechanisms for emergent biological behaviors. In this proposal, we will focus on studying two essential aspects of the bacterial chemotaxis pathway: 1) The structure-function relationship for the chemoreceptor cluster. The bacterial chemoreceptors form polar clusters with the adaptor protein CheW and the histidine kinase CheA. By using the latest structure information of the chemoreceptor cluster and functional measurements, we will develop a structure-based model to investigate how chemical signal propogates through the heterogeneous protein cluster and how the signal can be amplified by the large extended chemoreceptor array. 2) Signal integration and adaption of the bacterial flagellar motor. The bacterial flaglellar motor is composed of ~20 different types of proteins. It can sense the intracellular chemical signal (CheY-P) and switch its rotational direction (CW and CCW) accordingly. It can also "sense" the mechanical signal, the load, and generates a corresponding torque to drive the load to rotate at a certain angular speed. We will develop an integrated model to describe both the mechanical motion (rotation) and the switching dynamics of the motor in a thermodynamically consistent framework. We will use this integrated model to investigate how the flagellar motor's switching dynamics can be affected by changes in its mechanical environment (load, torque). We will introduce different feedback interactions in our model to investigate the possible origins of the recently observed motor adaptation to external chemical and mechanical signals. The model predictions will be tested with experimental measurements to determine the molecular mechanism for motor adaptation. In summary, we plan to investigate and understand how different proteins in multi- component protein complexes (such as the chemoreceptor cluster and the flagellar motor) work together to sense, to respond, and to adapt to different (chemical and/or physical) signals.