The Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel.
Bioinformatics. 2010 Jun 15;26(12):i255-60. doi: 10.1093/bioinformatics/btq183.
The availability of modern sequencing techniques has led to a rapid increase in the amount of reconstructed metabolic networks. Using these models as a platform for the analysis of high throughput transcriptomic, proteomic and metabolomic data can provide valuable insight into conditional changes in the metabolic activity of an organism. While transcriptomics and proteomics provide important insights into the hierarchical regulation of metabolic flux, metabolomics shed light on the actual enzyme activity through metabolic regulation and mass action effects. Here we introduce a new method, termed integrative omics-metabolic analysis (IOMA) that quantitatively integrates proteomic and metabolomic data with genome-scale metabolic models, to more accurately predict metabolic flux distributions. The method is formulated as a quadratic programming (QP) problem that seeks a steady-state flux distribution in which flux through reactions with measured proteomic and metabolomic data, is as consistent as possible with kinetically derived flux estimations.
IOMA is shown to successfully predict the metabolic state of human erythrocytes (compared to kinetic model simulations), showing a significant advantage over the commonly used methods flux balance analysis and minimization of metabolic adjustment. Thereafter, IOMA is shown to correctly predict metabolic fluxes in Escherichia coli under different gene knockouts for which both metabolomic and proteomic data is available, achieving higher prediction accuracy over the extant methods. Considering the lack of high-throughput flux measurements, while high-throughput metabolomic and proteomic data are becoming readily available, we expect IOMA to significantly contribute to future research of cellular metabolism.
现代测序技术的出现使得重建代谢网络的数量迅速增加。利用这些模型作为分析高通量转录组、蛋白质组和代谢组学数据的平台，可以为生物体代谢活性的条件变化提供有价值的见解。虽然转录组学和蛋白质组学为代谢通量的层次调节提供了重要的见解，但代谢组学通过代谢调节和质量作用效应揭示了实际的酶活性。在这里，我们引入了一种新的方法，称为整合组学-代谢分析（IOMA），它将蛋白质组学和代谢组学数据与基因组规模的代谢模型定量整合，以更准确地预测代谢通量分布。该方法被表述为一个二次规划（QP）问题，该问题寻求一个稳态通量分布，其中通过具有测量的蛋白质组学和代谢组学数据的反应的通量，尽可能与从动力学推导的通量估计一致。
IOMA 成功地预测了人类红细胞的代谢状态（与动力学模型模拟相比），与常用的通量平衡分析和代谢调整最小化方法相比具有显著优势。此后，IOMA 被证明可以正确预测具有代谢组学和蛋白质组学数据的不同基因敲除的大肠杆菌中的代谢通量，在现有方法的基础上实现了更高的预测准确性。考虑到缺乏高通量通量测量，而高通量代谢组学和蛋白质组学数据变得易于获得，我们预计 IOMA 将为未来的细胞代谢研究做出重大贡献。
