Robust moiety model selection using mass spectrometry measured isotopologues

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Stable isotope resolved metabolomics (SIRM) experiments use stable isotope tracers to provide superior metabolomics datasets for metabolic flux analysis and metabolic modeling. Since assumptions of model correctness can seriously compromise interpretation of metabolic flux results, we have developed a metabolic modeling software package specifically designed for moiety model comparison and selection based on the metabolomics data provided. Here, we tested the effectiveness of model selection with two time-series mass spectrometry (MS) isotopologue datasets for uridine diphosphate N-acetyl-d-glucosamine (UDP-GlcNAc) generated from different platforms utilizing direct infusion nanoelectrospray and liquid chromatography. Analysis results demonstrate the robustness of our model selection methods by the successful selection of the optimal model from over 40 models provided. Moreover, the effects of specific optimization methods, degree of optimization, selection criteria, and specific objective functions on model selection are illustrated. Overall, these results indicate that over-optimization can lead to model selection failure, but combining multiple datasets can help control this overfitting effect. The implication is that SIRM datasets in public repositories of reasonable quality can be combined with newly acquired datasets to improve model selection. Furthermore, curation efforts of public metabolomics repositories to maintain high data quality could have a huge impact on future metabolic modeling efforts.

Original languageEnglish
Article number118
Issue number3
StatePublished - Mar 2020

Bibliographical note

Funding Information:
Funding: The work was supported in part by grant NSF 1419282 (PI Moseley).

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.


  • Isotopologue deconvolution
  • Model selection
  • Moiety modeling
  • Nonlinear inverse problem
  • Overfitting
  • Stable isotope resolved metabolomics (SIRM)

ASJC Scopus subject areas

  • Endocrinology, Diabetes and Metabolism
  • Biochemistry
  • Molecular Biology


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