Quantitative Modeling of Bacterial Chemotaxis Signaling Pathway

细菌趋化信号通路的定量建模

• 批准号: 9147598
• 负责人: Yuhai Tu
• 金额: $ 26.52万
• 依托单位: IBM THOMAS J. WATSON RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-24 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9147598
• 关键词: Adaptor Signaling Protein; Affect; Automobile Driving; Bacterial Model; Behavior; Binding; Biochemical; Biological; Cell Wall; Cell membrane; Cells; Chemicals; Chemoreceptors; Chemotaxis; Complex; Coupled; Coupling; Data; Dependence; Dose; Environment; Escherichia coli; Experimental Models; Feedback; Future; Goals; Health; Human; Measurement; Mechanics; Membrane; Methods; Modeling; Molecular; Motion; Motor; Mutation; Organism; Pathway interactions; Phosphotransferases; Proteins; Proton-Motive Force; Research; Research Project Grants; Role; Rotation; Sensory; Signal Pathway; Signal Transduction; Signal Transduction Pathway; Speed; Structure; Structure-Activity Relationship; System; Testing; Thermodynamics; Torque; Work; base; behavior measurement; biological systems; cell motility; dimer; falls; improved; mathematical model; pathogen; predictive modeling; programs; protein complex; protein-histidine kinase; receptor; research study; response; sensory system; simulation; tomography; tool

 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.

 描述（由申请人提供） 本研究的长期目标是实现系统级E。大肠杆菌的趋化行为及其分子水平的机制。我们将建立基于趋化性信号通路的结构和生化细节的蛋白质相互作用网络及其动力学的数学模型。这些模型将使用解析分析和数值模拟方法进行研究。这些模型的结果将被用来解释实验数据，并作出可检验的预测。模型和实验数据之间的迭代比较将用于改进/细化模型。结合定量实验，这些预测模型使我们能够测试不同的假设，以了解新兴生物行为的潜在分子机制。在本研究中，我们将重点研究细菌趋化性途径的两个基本方面：1）化学感受器簇的结构-功能关系。细菌化学感受器与衔接蛋白CheW和组氨酸激酶CheA形成极性簇。通过使用最新的化学感受器簇的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如何通过异质蛋白质簇传播以及信号如何被大型扩展化学感受器阵列放大。2)细菌鞭毛马达的信号整合与适应。细菌鞭毛马达由约20种不同类型的 proteins.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）。它还可以“感知”机械信号，负载，并产生相应的扭矩，以驱动负载以一定的角速度旋转。我们将开发一个综合模型来描述机械运动（旋转）和电机的开关动力学在一个物理上一致的框架。我们将使用这个综合模型来研究鞭毛电机的开关动态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相互作用，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起源。模型预测将与实验测量进行测试，以确定运动适应的分子机制。总之，我们计划研究和理解多组分蛋白质复合物中的不同蛋白质（如化学感受器簇和鞭毛马达）如何一起工作以感知、响应和适应不同的（化学和/或物理）信号。