Quantitative Modeling of Bacterial Chemotaxis Signaling Pathway

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 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.