Muñoz-Cobo José-Luis, Berna Cesar
Department of Chemical and Nuclear Engineering, Universitat Politècnica de València, 46022 Valencia, Spain.
Instituto Universitario de Ingeniería Energética, Universitat Politècnica de València, 46022 Valencia, Spain.
Entropy (Basel). 2019 Feb 14;21(2):181. doi: 10.3390/e21020181.
In this paper first, we review the physical root bases of chemical reaction networks as a Markov process in multidimensional vector space. Then we study the chemical reactions from a microscopic point of view, to obtain the expression for the propensities for the different reactions that can happen in the network. These chemical propensities, at a given time, depend on the system state at that time, and do not depend on the state at an earlier time indicating that we are dealing with Markov processes. Then the Chemical Master Equation (CME) is deduced for an arbitrary chemical network from a probability balance and it is expressed in terms of the reaction propensities. This CME governs the dynamics of the chemical system. Due to the difficulty to solve this equation two methods are studied, the first one is the probability generating function method or z-transform, which permits to obtain the evolution of the factorial moment of the system with time in an easiest way or after some manipulation the evolution of the polynomial moments. The second method studied is the expansion of the CME in terms of an order parameter (system volume). In this case we study first the expansion of the CME using the propensities obtained previously and splitting the molecular concentration into a deterministic part and a random part. An expression in terms of multinomial coefficients is obtained for the evolution of the probability of the random part. Then we study how to reconstruct the probability distribution from the moments using the maximum entropy principle. Finally, the previous methods are applied to simple chemical networks and the consistency of these methods is studied.
在本文中,首先,我们回顾化学反应网络作为多维向量空间中的马尔可夫过程的物理根源基础。然后我们从微观角度研究化学反应,以获得网络中可能发生的不同反应的倾向表达式。这些化学倾向在给定时间取决于当时的系统状态,而不取决于更早时间的状态,这表明我们正在处理马尔可夫过程。然后从概率平衡推导出任意化学网络的化学主方程(CME),并根据反应倾向来表示它。这个CME支配着化学系统的动力学。由于求解这个方程存在困难,我们研究了两种方法,第一种是概率生成函数法或z变换,它允许以最简单的方式获得系统阶乘矩随时间的演化,或者经过一些处理后获得多项式矩的演化。研究的第二种方法是根据一个序参量(系统体积)对CME进行展开。在这种情况下,我们首先使用先前获得的倾向并将分子浓度分为确定性部分和随机部分来对CME进行展开。得到了关于随机部分概率演化的一个用多项系数表示的表达式。然后我们研究如何使用最大熵原理从矩重建概率分布。最后,将先前的方法应用于简单的化学网络,并研究这些方法的一致性。