基于因果关系的过程模型通量分析
Flux Analysis in Process Models via Causality
摘要：
提出了一种生物系统进程代数模型的流量分析方法。我们认为通量是随机模拟中的资源流动。我们诉诸于事件结构之间建立的对应关系，一个广泛认可的并发模型，和状态转换的过程模型，被视为Petri网。我们表明，我们可以通过这种方式提取的因果资源依赖性，在模拟个别状态转换的事件的偏序之间。我们提出的部分订单，提供进一步分析的手段上的变换，并介绍了一个软件工具，实现这些想法。通过一个例子的Rho GTP-结合蛋白的一个已发表的模型，我们认为，这种方法可以提供替代通量分析技术的常微分方程模型内的随机设置的过程代数。
英文摘要：
We present an approach for flux analysis in process algebra models of biological systems. We perceive flux as the flow of resources in stochastic simulations. We resort to an established correspondence between event structures, a broadly recognised model of concurrency, and state transitions of process models, seen as Petri nets. We show that we can this way extract the causal resource dependencies in simulations between individual state transitions as partial orders of events. We propose transformations on the partial orders that provide means for further analysis, and introduce a software tool, which implements these ideas. By means of an example of a published model of the Rho GTP-binding proteins, we argue that this approach can provide the substitute for flux analysis techniques on ordinary differential equation models within the stochastic setting of process algebras.
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