Sun Sean X
Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.
Phys Rev Lett. 2006 Jun 2;96(21):210602. doi: 10.1103/PhysRevLett.96.210602. Epub 2006 Jun 1.
Markovian dynamics, modeled by the kinetic master equation, has wide ranging applications in chemistry, physics, and biology. We derive an exact expression for the probability of a Markovian path in discrete state space for an arbitrary number of states and path length. The total probability of paths repeatedly visiting a set of states can be explicitly summed. The transition probability between states can be expressed as a sum over all possible paths connecting the states. The derived path probabilities satisfy the fluctuation theorem. The paths can be the starting point for a path space Monte Carlo procedure which can serve as an alternative algorithm to analyze pathways in a complex reaction network.
由动力学主方程建模的马尔可夫动力学在化学、物理和生物学中有着广泛的应用。我们推导出了离散状态空间中任意数量状态和路径长度的马尔可夫路径概率的精确表达式。重复访问一组状态的路径的总概率可以明确求和。状态之间的转移概率可以表示为连接这些状态的所有可能路径的总和。推导得到的路径概率满足涨落定理。这些路径可以作为路径空间蒙特卡罗方法的起点,该方法可作为分析复杂反应网络中路径的替代算法。