论文标题
从观察到的统计数据推断出的熵产生的通用界限
Universal bounds on entropy production inferred from observed statistics
论文作者
论文摘要
非平衡过程打破了时间交流对称性并产生熵。生物系统是在分子电动机的微观水平上驱动平衡的,这些分子电动机利用化学势梯度将自由能转化为机械工作,同时消散能量。能量耗散的量或熵产生速率(EPR)在细胞过程上设定了热力学约束。实际上,由于时空分辨率有限,并且缺乏有关各种自由度的完整信息,因此计算实验系统中的总EPR具有挑战性。在这里,我们基于使用观察到的过渡和等待时间统计信息的优化方案,提出了一种对总EPR的紧密下限的新推理方法。我们介绍了依靠一阶和二阶过渡以及观察到的等待时间分布的时刻的层次结构,并将我们的方法应用于带有总状态的隐藏网络和一个分子电动机的两个通用系统。最后,我们表明,即使假设完整系统的更简单的网络拓扑,也可以获得总EPR的下限。
Nonequilibrium processes break time-reversal symmetry and generate entropy. Living systems are driven out-of-equilibrium at the microscopic level of molecular motors that exploit chemical potential gradients to transduce free energy to mechanical work, while dissipating energy. The amount of energy dissipation, or the entropy production rate (EPR), sets thermodynamic constraints on cellular processes. Practically, calculating the total EPR in experimental systems is challenging due to the limited spatiotemporal resolution and the lack of complete information on every degree of freedom. Here, we propose a new inference approach for a tight lower bound on the total EPR given partial information, based on an optimization scheme that uses the observed transitions and waiting times statistics. We introduce hierarchical bounds relying on the first- and second-order transitions, and the moments of the observed waiting time distributions, and apply our approach to two generic systems of a hidden network and a molecular motor, with lumped states. Finally, we show that a lower bound on the total EPR can be obtained even when assuming a simpler network topology of the full system.