DFG-Research Center Matheon, Berlin, Germany.
BMC Bioinformatics. 2011 Jun 15;12:236. doi: 10.1186/1471-2105-12-236.
Flux coupling analysis (FCA) is a useful method for finding dependencies between fluxes of a metabolic network at steady-state. FCA classifies reactions into subsets (called coupled reaction sets) in which activity of one reaction implies activity of another reaction. Several approaches for FCA have been proposed in the literature.
We introduce a new FCA algorithm, FFCA (Feasibility-based Flux Coupling Analysis), which is based on checking the feasibility of a system of linear inequalities. We show on a set of benchmarks that for genome-scale networks FFCA is faster than other existing FCA methods.
We present FFCA as a new method for flux coupling analysis and prove it to be faster than existing approaches. A corresponding software tool is freely available for non-commercial use at http://www.bioinformatics.org/ffca/.
通量耦合分析（FCA）是一种在稳态下寻找代谢网络通量之间依赖关系的有用方法。FCA 将反应分类为子集（称为耦合反应集），其中一个反应的活性意味着另一个反应的活性。文献中已经提出了几种 FCA 方法。
我们引入了一种新的 FCA 算法，FFCA（基于可行性的通量耦合分析），它基于检查线性不等式系统的可行性。我们在一组基准测试中表明，对于基因组规模的网络，FFCA 比其他现有的 FCA 方法更快。
我们提出了 FFCA 作为一种新的通量耦合分析方法，并证明它比现有的方法更快。相应的软件工具可在 http://www.bioinformatics.org/ffca/ 上免费供非商业使用。
