Genome-Scale Metabolic Models in Industrial Microbiology: Advances, Applications, and Future Directions

Genome-scale metabolic models (GEMs) have become central tools for understanding and engineering microbial cell factories. By integrating genomic, biochemical, and physiological data into predictive frameworks, they allow researchers to rationally design strains, optimize yields, and probe the metabolic limits of industrially relevant organisms. As sequencing, omics integration, and machine learning mature, GEMs are shifting from descriptive reconstructions toward dynamic, condition-specific, and strain-specific predictive platforms.This Research Topic invites original research, reviews, and perspectives on the development and application of genome-scale metabolic models in industrial microbiology. We welcome contributions spanning methodological advances, model-guided strain engineering, and emerging integrations with data-driven approaches.Themes of interest include, but are not limited to:Reconstruction and curation of high-quality genome-scale models for industrial strainsModel-guided metabolic engineering and cell factory designIntegration of multi-omics data into constraint-based modelsDynamic and enzyme-constrained modelling approachesMachine learning and hybrid modelling for metabolic predictionCommunity and co-culture metabolic modelling for bioprocessesApplications in biofuels, biopolymers, specialty chemicals, and pharmaceuticals Topic editors: Mugesh Sankaranarayanan, Si-Yu Li, Sathya Narayanan Venkatesan Source: https://www.frontiersin.org/research-topics/84286/genome-scale-metabolic-models-in-industrial-microbiology-advances-applications-and-future-directions