基于Optimization的代谢网络相关基本流模的动态生成
On Dynamically Generating Relevant Elementary Flux Modes in a Metabolic Network using Optimization
摘要：
基本通量模式（EFM）是通过代谢反应网络将外部底物连接到产物的途径。使用EFM，可以将代谢网络转换为其宏观对应物，其中内部代谢物已被消除，仅保留外部代谢物。在基于EFM的代谢通量分析（MFA）中，实验确定的外部通量被用来估计每个EFM的通量。一般来说，枚举复杂网络的所有EFM是不允许的，因为EFM的数量随着网络的复杂性而迅速增加。在这项工作中，我们提出了一种基于优化的方法，动态生成一个子集的EFM和解决基于EFM的MFA问题的同时。得到的子集包含的EFMs，有助于最优解的EFMs为基础的MFA问题。我们的方法的有用性进行了检查，在一个案例研究中使用的数据从中国仓鼠卵巢细胞培养和两个网络的不同的复杂性。结果表明，基于EFMs的MFA问题可以在较低的计算成本，即使是更复杂的网络解决。此外，只需要一小部分的EFM总数计算的最优解。
英文摘要：
Elementary flux modes (EFMs) are pathways through a metabolic reaction network that connect external substrates to products. Using EFMs, a metabolic network can be transformed into its macroscopic counterpart, in which the internal metabolites have been eliminated and only external metabolites remain. In EFMs-based metabolic flux analysis (MFA) experimentally determined external fluxes are used to estimate the flux of each EFM. It is in general prohibitive to enumerate all EFMs for complex networks, since the number of EFMs increases rapidly with network complexity. In this work we present an optimization-based method that dynamically generates a subset of EFMs and solves the EFMs-based MFA problem simultaneously. The obtained subset contains EFMs that contribute to the optimal solution of the EFMs-based MFA problem. The usefulness of our method was examined in a case-study using data from a Chinese hamster ovary cell culture and two networks of varied complexity. It was demonstrated that the EFMs-based MFA problem could be solved at a low computational cost, even for the more complex network. Additionally, only a fraction of the total number of EFMs was needed to compute the optimal solution.
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