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Related Experiment Video

Updated: May 24, 2026

Single-Cell RNA Sequencing of Mutant Whole Mouse Embryos: From the Epiblast to the End of Gastrulation
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Selecting methods for draft GEM generation in multicellular eukaryotes: a comparative analysis.

Natalia E Jiménez1,2, Mikael Espinoza3,4, Sebastián Mejías3,4

  • 1Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile. natalia.jimenez@uc.cl.

BMC Bioinformatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Automated genome-scale metabolic model (GEM) reconstruction tools show varied performance in multicellular eukaryotes. No single tool excels at both model functionality and eukaryotic-specific features, necessitating careful selection based on research goals.

Keywords:
Automated reconstructionGenome-scale modelsMetabolic modelsMulticellular eukaryotes

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Area of Science:

  • Systems Biology
  • Metabolic Engineering
  • Computational Biology

Background:

  • Genome-scale metabolic models (GEMs) are crucial for biological discovery and metabolic engineering.
  • Existing automated GEM reconstruction methods are often not optimized for multicellular eukaryotes, with unclear performance.
  • Evaluating these tools for eukaryotic systems is essential for advancing research.

Purpose of the Study:

  • To comparatively analyze seven automated reconstruction tools for generating draft GEMs in multicellular eukaryotes.
  • To assess tool performance based on network properties, functional representation, and annotation quality.
  • To guide researchers in selecting appropriate tools for eukaryotic GEM reconstruction.

Main Methods:

  • Seven automated reconstruction tools (AuReMe, CarveMe, merlin, ModelSEED, Pathway Tools, RAVEN, Reconstructor) were applied.
  • The tools were tested on three multicellular eukaryotes: Aedes aegypti, CHO cell line (Cricetulus griseus), and Ectocarpus siliculosus.
  • Evaluation metrics included network size, functionality, consistency, eukaryotic features, annotation quality, and execution time.

Main Results:

  • Significant differences were observed among the tools in terms of functionality and representation of eukaryotic features.
  • A trade-off exists between model functionality and the accurate representation of compartmentalization and organism-specific metabolism.
  • No single tool demonstrated superiority across all evaluated metrics for eukaryotic GEM reconstruction.

Conclusions:

  • The performance of automated GEM reconstruction tools varies considerably for multicellular eukaryotes.
  • Researchers must consider a trade-off between model comprehensiveness and the accurate depiction of eukaryotic-specific biological processes.
  • This comparative analysis provides a resource for selecting the most suitable tool based on specific research objectives and organism characteristics.