From Genome to Actionable Insights in Biotechnology

This chapter explores how biological networks, particularly metabolic networks, can be constructed, validated, and transformed into predictive models for biotechnology. Emphasizing the role of genome-scale metabolic models (GEMs), it highlights how omics data and machine learning approaches enhance accuracy, interpretability, and applicability of network analysis. By integrating high-throughput data with structured network models, researchers can generate actionable predictions, guide metabolic engineering, and optimize bioprocesses. Finally, opportunities to include other cellular functions and increase the applicability of GEMs to biotechnological applications are discussed, showcasing exemplar studies to date.