Evaluating E. coli genome-scale metabolic model accuracy with high-throughput mutant fitness data

Area of Science:

• Systems biology
• Metabolic modeling
• Computational biology

Background:

• Escherichia coli genome-scale metabolic models (GEMs) are crucial for simulating cellular metabolism.
• Experimental validation is essential for refining and ensuring the accuracy of these models.
• Previous assessments of GEM accuracy have limitations.

Purpose of the Study:

• To quantify the accuracy of four E. coli GEMs using extensive mutant fitness data.
• To evaluate the effectiveness of the area under the precision-recall curve as an accuracy metric.
• To identify sources of error and key determinants of model accuracy in E. coli GEMs.

Main Methods:

• Utilized published mutant fitness data across thousands of genes and 25 carbon sources.
• Compared the accuracy of four sequential E. coli GEMs.
• Employed a machine learning approach to identify factors influencing model accuracy.

Main Results:

• The area under the precision-recall curve proved a robust metric for GEM accuracy assessment.
• The latest iML1515 model showed inaccuracies linked to assumed vitamin/cofactor availability and isoenzyme mapping.
• Metabolic fluxes, particularly hydrogen ion exchange and central metabolism branch points, significantly impact model accuracy.

Conclusions:

• Established improved practices for assessing GEM accuracy with high-throughput mutant fitness data.
• Highlighted specific areas for future refinement of E. coli GEMs, including cofactor availability and gene-protein-reaction mappings.
• Provided a framework for enhancing the predictive power of metabolic models in microbial systems.