The paper by Spirin and Mirny explores the modular structure of molecular networks, focusing on protein-protein interactions. They developed algorithms to identify highly connected clusters (or modules) within these networks, which are densely connected internally but sparsely connected to the rest of the network. These modules were categorized into two types: protein complexes (such as transcription factors and splicing machinery) and dynamic functional units (such as signaling cascades and cell-cycle regulation). The identified modules were statistically significant and robust to noise in the data, supporting the network modularity principle. The study also compared the identified modules with experimental data and functional annotations, finding strong agreement with known protein complexes and functional modules. The authors suggest that their computational approach can help integrate various types of data and uncover novel functional modules, providing insights into the organization and function of molecular networks.The paper by Spirin and Mirny explores the modular structure of molecular networks, focusing on protein-protein interactions. They developed algorithms to identify highly connected clusters (or modules) within these networks, which are densely connected internally but sparsely connected to the rest of the network. These modules were categorized into two types: protein complexes (such as transcription factors and splicing machinery) and dynamic functional units (such as signaling cascades and cell-cycle regulation). The identified modules were statistically significant and robust to noise in the data, supporting the network modularity principle. The study also compared the identified modules with experimental data and functional annotations, finding strong agreement with known protein complexes and functional modules. The authors suggest that their computational approach can help integrate various types of data and uncover novel functional modules, providing insights into the organization and function of molecular networks.