Metabolic Network

Metabolic networks represent the complex web of biochemical reactions within cells, and research focuses on accurately modeling these networks to understand and engineer biological systems. Current efforts leverage machine learning, including deep learning and logic-based approaches like Boolean Matrix Logic Programming, to improve the accuracy and completeness of genome-scale metabolic models (GEMs), often addressing challenges like identifying missing reactions and predicting cellular behavior. These advancements enable more efficient drug design, metabolic engineering for producing valuable compounds, and a deeper understanding of disease processes like primary sclerosing cholangitis, ultimately accelerating progress in systems biology and synthetic biology.