计算随机系统生物学的时间序乘积展开
Time-Ordered Product Expansions for Computational Stochastic Systems Biology
摘要：
量子场论的时间有序乘积框架也可以用来理解随机生物化学网络中的显著现象。它是用来在这里得到吉莱斯皮的随机模拟算法（SSA）的化学反应网络，因此，SSA可以解释的费曼图。它也被用来在这里获得其他更一般的模拟和参数学习算法，包括模拟算法的网络随机反应的过程中操作的参数化对象，也混合随机反应/微分方程模型，其中系统的常微分方程演变的参数的对象，也可以进行随机反应。因此，时间序乘积展开（TOPE）可以系统地用于推导随机系统的模拟和参数拟合算法。
英文摘要：
The time-ordered product framework of quantum field theory can also be used to understand salient phenomena in stochastic biochemical networks. It is used here to derive Gillespie's Stochastic Simulation Algorithm (SSA) for chemical reaction networks; consequently, the SSA can be interpreted in terms of Feynman diagrams. It is also used here to derive other, more general simulation and parameter-learning algorithms including simulation algorithms for networks of stochastic reaction-like processes operating on parameterized objects, and also hybrid stochastic reaction/differential equation models in which systems of ordinary differential equations evolve the parameters of objects that can also undergo stochastic reactions. Thus, the time-ordered product expansion (TOPE) can be used systematically to derive simulation and parameter-fitting algorithms for stochastic systems.
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