Simulation and Reconstruction of Metabolite-Metabolite Association Networks Using a Metabolic Dynamic Model and Correlation Based Algorithms

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,...

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Vydáno v:Journal of proteome research Ročník 18; číslo 3; s. 1099
Hlavní autoři: Jahagirdar, Sanjeevan, Suarez-Diez, Maria, Saccenti, Edoardo
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.03.2019
Témata:
Algorithms
Computer Simulation
Humans
Metabolic Networks and Pathways - genetics
Metabolome - genetics
Metabolomics - statistics & numerical data
Models, Biological
Sample Size
arachidonic acid
network inference
arachidonic acid metabolism
top-down approach
metabolic modeling
ISSN:1535-3907, 1535-3907
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:1535-3907
1535-3907
DOI:10.1021/acs.jproteome.8b00781