Cooperative stator assembly of bacterial flagellar motor mediated by rotation

Cooperativity has a central place in biological regulation, providing robust and highly-sensitive regulation. The bacterial flagellar motor implements autonomous torque regulation based on the stator’s dynamic structure; the stator units bind to and dissociate from the motor dynamically in response to environmental changes. However, the mechanism of this dynamic assembly is not fully understood. Here, we demonstrate the cooperativity in the stator assembly dynamics. The binding is slow at the stalled state, but externally forced rotation as well as driving by motor torque in either direction boosts the stator binding. Hence, once a stator unit binds, it drives the rotor and triggers the avalanche of succeeding bindings. This cooperative mechanism based on nonequilibrium allostery accords with the recently-proposed gear-type coupling between the rotor and stator.

Cooperative stator assembly of bacterial flagellar motor for autonomous torque regulation

ABSTRACTCooperativity has a central place in biological regulation, providing robust and highly-sensitive regulation. The bacterial flagellar motor (BFM) implements autonomous torque regulation by the nonequilibrium structure of the stators; the stators assemble at high load and disperse at low load. It would be natural to suppose that the stator packing is affected by stator-stator interaction. However, the cooperativity among the stators has rarely been explored. Here, we evaluated the energetics and kinetics of the stator assembly by combining dynamic load control of a single motor and the trajectory analysis based on statistical mechanics. We demonstrate that the BFM exploits the dynamic cooperativity of the stator binding for the autonomous torque regulation. The cooperative assembly leads to a discontinuous phase transition and hysteresis, which may implement torque regulation with high sensitivity and robustness.

Hybrid adaptive control for variable-speed variable-pitch wind energy systems using general regression neural network

This paper presents a novel hybrid adaptive control approach for the VS-VP half-direct driven WECS by combining pitch control with variable generator torque regulation in different operating regions. A general regression neural network (GRNN) is employed to derive the reference commands of generator torque and pitch angle from the real-time signals of generator power and speed. Furthermore, a fast and effective nonlinear PID pitch controller is presented to track the reference command of pitch angle in the full load region. The proposed GRNN based hybrid adaptive control strategies have been developed and validated using simulations. This study shows that the proposed method is much faster, more accurate and effective than conventional linear control approach.