应用最大熵原理通过酶控制通量预测突变体的通量分布

Prediction of Flux Distribution of Mutants by eNZYME Control Fluxes with Maximum Entropy Principle

摘要:

代谢通量分析中整合组学数据对研究后基因组时代的细胞机制至关重要。酶活性数据可以通过酶控制通量(ECF)整合到基元模式(EMs)中, 用于预测突变体的通量分布。本研究提出一种新的ECF算法—ECFMEP, 用于分析中等规模的代谢网络。为了有效估计大量的EM系数, 将Larange系数法使用于最大熵原理的优化问题, 从而显著降低了要研究变量的数目。ECFMEP用于预测厌氧条件下大肠杆菌(Escherichia coli)的4种突变体的通量分布。这些细胞包括102个反应和28 555种Ems。研究结果表明, ECFMEP有效利用了酶活性数据, 与代谢平衡分析(FBA)以及最小代谢调节分析(MOMA)等方法相比, 提高了预测的准确性。

Abstract:

The integration of omics data in metabolic flux analysis is critical to explore the cellular mechanisms in the post-genomic era. Enzyme activity data can be integrated into elementary modes (EMs) by Enzyme Control Flux (ECF) for the prediction of flux distributions of mutants. A new ECF algorithm, named as ECFMEP, is proposed for a moderate scale of metabolic networks. To efficiently estimate a large number of EM coefficients (EMCs), the Lagrange multiplier is applied to the optimization problem under maximum entropy principle (MEP), thereby remarkably reducing the number of the variables to be explored. ECFMEP is employed to predict the flux distribution of four mutants of Escherichia coli under anaerobic conditions. These cells consist of 102 reactions and 28,555 EMs. We demonstrate that ECFMEP effectively makes use of enzyme activity data for enhanced prediction accuracy in comparison with that by Flux Balance Analysis (FBA) and Minimization of Metabolic Adjustment (MOMA).