Genome-scale metabolic models enable predictive strain and process design for sustainable biomanufacturing

Genome-scale metabolic models (GEMs) have emerged as powerful tools for optimizing microbial fermentation. This review traces the application of GEMs from model reconstruction through to industrial implementation. Automated reconstruction tools including CarveMe, ModelSEED, and RAVEN are discussed alongside quality-control frameworks such as MEMOTE. Biological validation of these models ultimately depends on comparing predicted phenotypes against experimental data. The review then focuses on how GEMs guide strain engineering and process optimization, enabling rational target identification, improved substrate utilization, and enhanced fermentation performance. The predictive accuracy and experimental validation of these applications vary considerably across microbial hosts and production contexts. Recent methodological advances, including machine learning-assisted model refinement, synthetic pathway design, and community-scale metabolic modeling, have extended the scope and predictive power of GEMs still further. Persistent challenges in model accuracy, data integration, and scale-up validation remain, yet continued progress in computational and experimental approaches is expected to solidify the role of GEMs in fermentation research and bioprocess development.