线性动力学的随机集总分析及其在动力学网络涨落关系中的应用
Stochastic lumping analysis for linear kinetics and its application to the fluctuation relations between hierarchical kinetic networks
摘要：
传统的生物分子行为研究主要依赖于动力学模型的构建。由于这些网络的选择是不唯一的，提出了一个问题是否以及在何种条件下，分层方案可以揭示相同的实验测量的波动行为和独特的波动相关的物理性质。为了澄清这些问题，我们将随机性引入到传统的集总分析中，将其从速率方程推广到化学主方程和随机微分方程，并提取了动力学和动力学等价网络在内外噪声作用下的涨落关系.这些结果为合理使用低维模型研究大分子波动提供了理论基础，更一般地说，为探索化学和生物动力学系统中不同层次的收缩网络中的随机特征提供了理论基础。
英文摘要：
Conventional studies of biomolecular behaviors rely largely on the construction of kinetic schemes. Since the selection of these networks is not unique, a concern is raised whether and under which conditions hierarchical schemes can reveal the same experimentally measured fluctuating behaviors and unique fluctuation related physical properties. To clarify these questions, we introduce stochasticity into the traditional lumping analysis, generalize it from rate equations to chemical master equations and stochastic differential equations, and extract the fluctuation relations between kinetically and thermodynamically equivalent networks under intrinsic and extrinsic noises. The results provide a theoretical basis for the legitimate use of low-dimensional models in the studies of macromolecular fluctuations and, more generally, for exploring stochastic features in different levels of contracted networks in chemical and biological kinetic systems.
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