Understand the Bacterial Flagellar Motor Switching Kinetics by Quantitative Modeling

Ibm Thomas J Watson Research Center, Yorktown Heights NY

Investigators

Abstract

Bacterial chemotaxis is one of the most fascinating sensory systems in biology. At the molecular level, most of the relevant proteins and their interactions in the underlying biochemical network have been identified. At the cellular (systems) level, there has been a recent burst of experimental effort, enabled by state-of-the-art fluorescence microscopy technologies, aimed at delineating some of the systems level behavior, such as signal amplification, wide dynamic range of ultra-sensitivity, and flagellar motor assemblage and function. These two factors, knowledge of the underlying signaling network and availability of systems level data, make bacterial chemotaxis one of the most suitable biological systems for quantitative modeling directed at understanding at the systems level, i.e., at the central goal of systems biology. The bacterial chemotaxis machinery can be divided into two parts: the signaling pathway and the flagellar motor. The motor has two possible states, spinning either clockwise (CW) or counter clockwise (CCW), which leads to cell's tumbling and run motions respectively. The switching of the motor between CW and CCW states is regulated by an intracellular response regulator CheY-P. Despite much experimental and modeling effort, detailed molecular level understanding of the switching mechanism is still missing. In this proposal, we plan to address three problems related to flagellar motor switching. (1) Switching Mechanism: We propose to develop a detailed kinetic model of motor switching that is consistent with both the observed ultra-sensitivity for the steady state behavior and the recently observed distributions of the CW and CCW duration times. Both the CW and CCW duration time distributions exhibit well defined peaks whose positions depend systematically on the motor bias, or CheY-P level. Current models have failed to explain these two experimental observations consistently. (2) Effect of Noise: There are two sources of noise affecting the switching of a single flagellar motor. The intrinsic fluctuations are due to discreteness of CheY-P/FliM binding and the switching stochasticity, and the extrinsic noise is caused by temporal fluctuations in CheY-P level in a wild type cell. We want to understand the effects of these two types of biological noise in flagellar motor switching, in particular, how they give rise to the observed motor switching pattern and its power spectrum. (3) Signal Decoding: We want to develop a theoretical framework and computational method to "decode" the binary flagella motor switching time series and determine the temporal fluctuations of the response regulator CheY-P at the single cell level, by separating it from the other noise sources using the model developed in (1) and (2). The measurement of CheY-P at the single cell level is crucial in understanding the upstream signaling pathway, so far direct single cell in vivo measurement of the long time CheY-P dynamics is still not available, our approach represents one of the most promising ways of obtaining this important information. Intellectual Merit: The proposed research represents one of the first systematic efforts to explain the recent single cell switching time distribution measurements through computational modeling. The project, if successful, will elucidate the molecular level mechanism of flagellar motor switching behavior. The use of a kinetic modeling framework is novel and should be generally applicable for other non-equilibrium biological systems. Broader Impact: Cooperativity and presence of noise are general phenomena occurring in many biological systems. The project, though aimed at understanding the switching kinetics of the bacterial flagellar motor, will develop a general modeling framework that is applicable for understanding cooperativity and effects of noise in other biological systems. The grant will enable the training of a postdoctoral fellow in a multidisciplinary environment, and enhance cross-disciplinary collaboration between researchers from computational and biological sciences.