分子马达的统计物理研究

分子马达是将化学能直接转化成力学能的酶蛋白，其力学-化学耦合机制一直是分子马达理论研究的焦点之一，本项目拟从单分子实验数据中提取涨落信息（例如随机度）研究分子马达的力学化学耦合机制；进而构建包含每个马达化学态的集体马达模型，用随机化学动力学模拟典型集体马达体系（如多驱动马达体系，驱动马达-动力马达杂合体系），重点研究各马达化学态之间的相位关系对集体马达运动协同性的影响。

Protein motors are enzymes that convert chemical energy into mechanical work. The mechanism of mechanochemical coupling of these motors has been a focus of theoretical studies. In this project, we will attempt to utilize the fluctuation information (e.g., randomness) contained in the single-molecule experimental data to investigate this issue. Furthermore, we will build new models of multi-motor transport systems, including the micro-states (i.e., the internal chemical states) of each single motor. By using stochastic simulations, we will investigate several typical multi-motor systems such as multi-kinesin system and kinesin-dynein hybrid system, putting our focus on how the chemical states of different motors interplay (e.g., in-phase or out-of-phase) to influence the collective transport.