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, 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.