The article discusses the structural analysis of the metabolic network of *Escherichia coli*, focusing on its small-world properties. Using graph theory, the authors show that the metabolic network of *E. coli* exhibits characteristics of a small-world graph, which is distinct from regular and random networks. Small-world networks are highly clustered and have short characteristic path lengths, allowing for efficient information transfer. The study also reveals that the connectivity of metabolites follows a power law distribution, indicating that a few metabolites are highly connected, while most have fewer connections. This finding provides an objective basis for identifying the central role of the tricarboxylic acid cycle in metabolism. The small-world architecture is hypothesized to minimize transition times between metabolic states and offers insights into the evolutionary history of metabolic networks. The research highlights the importance of network topology in understanding metabolic function and evolution, suggesting that the observed structure may reflect evolutionary optimization. The study also emphasizes the need for mathematical frameworks to uncover global patterns in biological systems, which are not apparent at the local level.The article discusses the structural analysis of the metabolic network of *Escherichia coli*, focusing on its small-world properties. Using graph theory, the authors show that the metabolic network of *E. coli* exhibits characteristics of a small-world graph, which is distinct from regular and random networks. Small-world networks are highly clustered and have short characteristic path lengths, allowing for efficient information transfer. The study also reveals that the connectivity of metabolites follows a power law distribution, indicating that a few metabolites are highly connected, while most have fewer connections. This finding provides an objective basis for identifying the central role of the tricarboxylic acid cycle in metabolism. The small-world architecture is hypothesized to minimize transition times between metabolic states and offers insights into the evolutionary history of metabolic networks. The research highlights the importance of network topology in understanding metabolic function and evolution, suggesting that the observed structure may reflect evolutionary optimization. The study also emphasizes the need for mathematical frameworks to uncover global patterns in biological systems, which are not apparent at the local level.