Simulation and reconstruction of metabolite-metabolite association networks using a metabolic dynamic model and correlation based-algorithms

Sanjeevan Jahagirdar, Maria Suarez-diez, Edoardo Saccenti

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)1099-1113
JournalJournal of Proteome Research
Volume18
Issue number3
DOIs
Publication statusPublished - 21 Jan 2019

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Metabolites
Dynamic models
Metabolomics
Biological Phenomena
Metabolome
Metabolic Networks and Pathways
Arachidonic Acid
Metabolism
Sample Size
Noise

Cite this

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title = "Simulation and reconstruction of metabolite-metabolite association networks using a metabolic dynamic model and correlation based-algorithms",
abstract = "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.",
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Simulation and reconstruction of metabolite-metabolite association networks using a metabolic dynamic model and correlation based-algorithms. / Jahagirdar, Sanjeevan; Suarez-diez, Maria; Saccenti, Edoardo.

In: Journal of Proteome Research, Vol. 18, No. 3, 21.01.2019, p. 1099-1113.

Research output: Contribution to journalArticleAcademicpeer-review

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AB - 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.

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