Flux coupling analysis of metabolic networks is sensitive to missing reactions.
Genome-scale metabolic reconstructions are comprehensive, yet incomplete, models of real-world metabolic networks. While flux coupling analysis (FCA) has proved an appropriate method for analyzing metabolic relationships and for detecting functionally related reactions in such models, little is known about the impact of missing reactions on the accuracy of FCA. Based on an alternative characterization of flux coupling relations using elementary flux modes, this paper studies the changes that flux coupling relations may undergo due to missing reactions. In particular, we show that two uncoupled reactions in a metabolic network may be detected as directionally, partially or fully coupled in an incomplete version of the same network. Even a single missing reaction can cause significant changes in flux coupling relations. In case of two consecutive Escherichia coli genome-scale networks many fully coupled reaction pairs in the incomplete network become directionally coupled or even uncoupled in the more complete reconstruction. In this context, we found gene expression correlation values being significantly higher for the pairs that remained fully coupled than for the uncoupled or directionally coupled pairs. Our study clearly suggests that FCA results are indeed sensitive to missing reactions. Since the currently available genome-scale metabolic models are incomplete, we advise to use FCA results with care.
Marashi
Sayed-Amir
SA
International Max Planck Research School for Computational Biology and Scientific Computing, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, Berlin, Germany. marashi@molgen.mpg.de
Bockmayr
Alexander
A
D019295:Computational Biology / Q000379:methods; D004269:DNA, Bacterial / Q000235:genetics; D004734:Energy Metabolism; D004926:Escherichia coli / Q000235:genetics / Q000254:growth & development / Q000378:metabolism*; D015870:Gene Expression; D016680:Genome, Bacterial; D053858:Metabolic Networks and Pathways; D008954:Models, Biological
D004269:DNA, Bacterial