Kahramanoğulları Ozan, Lynch James F
The Microsoft Research - University of Trento, Centre for Computational and Systems Biology, Trento, Italy.
BMC Syst Biol. 2013 Dec 7;7:133. doi: 10.1186/1752-0509-7-133.
Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes.
We describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology.
We provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network.
化学反应网络为生物学和生态学中的广泛模型提供了一种抽象方案。模拟这些网络的两种常见方法是确定性方法和随机方法。基于微分方程的传统确定性方法拥有丰富的分析技术，包括对反应通量的处理。然而，离散随机模拟虽然在某些情况下具有优势，但缺乏对网络通量的定量处理。
我们描述了一种用于化学反应网络通量分析的方法，其中通量由网络随机模拟中反应之间物质的流动给出。通过扩展离散事件模拟算法，我们的方法构建了几种数据结构，从而揭示了模拟过程中关于资源创建和消耗的各种统计信息。我们使用这些结构来量化任意时间间隔内反应相对于网络通量的因果相互依赖性和相对重要性。这使我们能够构建具有相同通量行为的简化网络，并比较这些网络，包括它们的时间序列。我们在基于已发表的同一网络（即 Rho GTP 结合蛋白）的常微分方程模型的扩展示例以及生物学和生态学的其他模型上展示了我们的方法。
我们提供了一种对通量分析的完全随机处理方法。与确定性分析一样，我们的方法根据物质转化来呈现网络行为。此外，我们的随机分析不仅可以应用于稳态，还可以应用于任意时间间隔，并用于识别特定反应之间特定物质的流动。我们对 Rho GTP 结合蛋白的案例研究揭示了循环反向通量在调节该网络行为中所起的作用。
