GEMsembler: consensus model assembly and structural comparison of genome-scale metabolic models across tools improve...

Area of Science:

• Systems biology
• Metabolic engineering
• Computational biology

Background:

• Genome-scale metabolic models (GEMs) are crucial for understanding cellular metabolism and predicting responses to perturbations.
• Automated GEM reconstruction tools yield diverse models with varying properties and predictive power.
• Combining multiple GEMs can enhance metabolic network certainty and overall model performance.

Purpose of the Study:

• Introduce GEMsembler, a Python package for comparing, analyzing, and integrating cross-tool GEMs.
• Facilitate the construction of consensus GEMs by combining subsets of input models.
• Improve the accuracy and biological relevance of GEMs for systems biology applications.

Main Methods:

• Developed GEMsembler for comparing and tracking features across different GEMs.
• Implemented functionality for building consensus models from selected input models.
• Incorporated analysis tools for pathway identification, growth assessment, and GPR optimization.
• Utilized an agreement-based curation workflow.

Main Results:

• GEMsembler-curated consensus models for *Lactiplantibacillus plantarum* and *Escherichia coli* outperformed gold-standard models in auxotrophy and gene essentiality predictions.
• Optimizing gene-protein-reaction (GPR) combinations within consensus models further improved gene essentiality predictions.
• GEMsembler identified key metabolic pathways and GPR alternatives, aiding in the resolution of model uncertainty.

Conclusions:

• GEMsembler enables the creation of more accurate and biologically informed metabolic models.
• The tool facilitates the synthesis of information from diverse GEMs, accelerating model development.
• GEMsembler aids in identifying knowledge gaps and prioritizing experiments for advancing systems biology research.