Simulation and Reconstruction of Metabolite-Metabolite Association Networks Using a Metabolic Dynamic Model and Correlation Based Algorithms.,Journal of Proteome Research

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Simulation and Reconstruction of Metabolite-Metabolite Association Networks Using a Metabolic Dynamic Model and Correlation Based Algorithms.
Journal of Proteome Research ( IF 3.8 ) Pub Date : 2019-02-04 , DOI: 10.1021/acs.jproteome.8b00781
Sanjeevan Jahagirdar ₁ , Maria Suarez-Diez ₁ , Edoardo Saccenti ₁

Affiliation

Biological networks play a paramount role in our understanding of complex biological phenomena, and metabolite-metabolite association networks are now commonly used in metabolomics applications. In this study we evaluate the performance of several network inference algorithms (PCLRC, MRNET, GENIE3, TIGRESS, and modifications of the MRNET algorithm, together with standard Pearson's and Spearman's correlation) using as a test case data generated using a dynamic metabolic model describing the metabolism of arachidonic acid (consisting of 83 metabolites and 131 reactions) and simulation individual metabolic profiles of 550 subjects. The quality of the reconstructed metabolite-metabolite association networks was assessed against the original metabolic network taking into account different degrees of association among the metabolites and different sample sizes and noise levels. We found that inference algorithms based on resampling and bootstrapping perform better when correlations are used as indexes to measure the strength of metabolite-metabolite associations. We also advocate for the use of data generated using dynamic models to test the performance of algorithms for network inference since they produce correlation patterns that are more similar to those observed in real metabolomics data.

中文翻译：

使用代谢动力学模型和基于相关性的算法对代谢物-代谢物关联网络进行仿真和重构。

在我们对复杂的生物现象的理解中，生物网络起着至关重要的作用，现在，代谢组学应用中通常使用代谢物-代谢物关联网络。在这项研究中，我们使用动态代谢模型生成的测试数据作为测试案例，评估了几种网络推理算法（PCLRC，MRNET，GENIE3，TIGRESS以及MRNET算法的修改以及标准Pearson和Spearman的相关性）的性能。花生四烯酸的代谢（由83种代谢物和131个反应组成），并模拟550名受试者的个体代谢谱。考虑到代谢物之间的关联程度不同以及不同的样本量和噪声水平，针对原始代谢网络对重建的代谢物-代谢物关联网络的质量进行了评估。我们发现，当将相关性用作衡量代谢物-代谢物关联强度的指标时，基于重采样和自举的推理算法性能更好。我们还提倡使用通过动态模型生成的数据来测试网络推理算法的性能，因为它们产生的关联模式与实际代谢组学数据中观察到的更为相似。我们发现，当将相关性用作衡量代谢物-代谢物关联强度的指标时，基于重采样和自举的推理算法性能更好。我们还提倡使用通过动态模型生成的数据来测试网络推理算法的性能，因为它们产生的关联模式与实际代谢组学数据中观察到的更为相似。我们发现，当将相关性用作衡量代谢物-代谢物关联强度的指标时，基于重采样和自举的推理算法性能更好。我们还提倡使用通过动态模型生成的数据来测试网络推理算法的性能，因为它们产生的关联模式与实际代谢组学数据中观察到的更为相似。

更新日期：2019-02-07

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