The article by Réka Albert discusses the complex interactions within cells and how these interactions are represented and analyzed using graph theory. Cells coordinate multiple processes through signaling pathways and regulatory mechanisms, and the large number of components and their interconnectivity are becoming evident through integrated genomic and proteomic analyses. The understanding of cellular network properties requires integrated, theoretical descriptions of the relationships between different cellular components. Recent theoretical advances have allowed for the description of cellular network structure using graph concepts, revealing organizational features shared with non-biological networks. The article reviews recent advances in addressing questions such as how to quantitatively describe networks of hundreds or thousands of interacting components, whether the observed topology of cellular networks provides clues about their evolution, and how the organization of cellular networks influences their function and dynamical responses. It covers various graph concepts, including node degree, clustering coefficient, and graph distance, and discusses the properties of different types of networks, such as linear pathways, random graphs, scale-free networks, and growing network models. The article also explores specific cellular networks, including protein interaction networks, metabolic networks, and transcriptional regulatory networks, highlighting their topological properties and the importance of hubs, modularity, motifs, and path redundancy. It discusses the biological interpretation of graph properties, such as the role of hubs and modularity, and the impact of natural selection on the evolution of cellular network topologies. Finally, it emphasizes the importance of integrating interaction and activity information to understand the dynamical behavior of cellular networks and the potential of network analysis for predicting systems-level behavior.The article by Réka Albert discusses the complex interactions within cells and how these interactions are represented and analyzed using graph theory. Cells coordinate multiple processes through signaling pathways and regulatory mechanisms, and the large number of components and their interconnectivity are becoming evident through integrated genomic and proteomic analyses. The understanding of cellular network properties requires integrated, theoretical descriptions of the relationships between different cellular components. Recent theoretical advances have allowed for the description of cellular network structure using graph concepts, revealing organizational features shared with non-biological networks. The article reviews recent advances in addressing questions such as how to quantitatively describe networks of hundreds or thousands of interacting components, whether the observed topology of cellular networks provides clues about their evolution, and how the organization of cellular networks influences their function and dynamical responses. It covers various graph concepts, including node degree, clustering coefficient, and graph distance, and discusses the properties of different types of networks, such as linear pathways, random graphs, scale-free networks, and growing network models. The article also explores specific cellular networks, including protein interaction networks, metabolic networks, and transcriptional regulatory networks, highlighting their topological properties and the importance of hubs, modularity, motifs, and path redundancy. It discusses the biological interpretation of graph properties, such as the role of hubs and modularity, and the impact of natural selection on the evolution of cellular network topologies. Finally, it emphasizes the importance of integrating interaction and activity information to understand the dynamical behavior of cellular networks and the potential of network analysis for predicting systems-level behavior.