BackgroundGillespie's stochastic simulation algorithm (SSA) for chemical reactions admits three kinds of elementary processes, namely, mass action reactions of 0th, 1st or 2nd order. All other types of reaction processes, for instance those containing non-integer kinetic orders or following other types of kinetic laws, are assumed to be convertible to one of the three elementary kinds, so that SSA can validly be applied. However, the conversion to elementary reactions is often difficult, if not impossible. Within deterministic contexts, a strategy of model reduction is often used. Such a reduction simplifies the actual system of reactions by merging or approximating intermediate steps and omitting reactants such as transient complexes. It would be valuable to adopt a similar reduction strategy to stochastic modelling. Indeed, efforts have been devoted to manipulating the chemical master equation (CME) in order to achieve a proper propensity function for a reduced stochastic system. However, manipulations of CME are almost always complicated, and successes have been limited to relative simple cases.
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机译：背景吉列斯皮（Gillespie）用于化学反应的随机模拟算法（SSA）接受三种基本过程，即0 th ，1 st 或2 nd 命令。假定所有其他类型的反应过程，例如那些包含非整数动力学阶数或遵循其他类型的动力学定律的反应过程，都可以转换为三种基本类型之一，从而可以有效地应用SSA。但是，转化为基本反应通常很困难，即使不是不可能的。在确定性上下文中，通常使用模型简化策略。通过合并或近似中间步骤并省略反应物（例如过渡配合物），这种还原简化了实际的反应系统。采用类似的减少策略进行随机建模将很有价值。确实，已经致力于操纵化学主方程（CME），以便为减少的随机系统获得适当的倾向函数。但是，CME的操作几乎总是很复杂，并且成功仅限于相对简单的情况。
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