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 MR-NET algorithm, together with standard Pearsons and Spearmans 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 size and noise levels. We found that inference algorithms based on resampling and bootstrapping to 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 which are more similar to those observed in real metabolomics data.