Biological systems are characterized by extraordinary complexity, underpinned by intricate molecular interaction networks that drive essential life processes. Understanding these networks is pivotal to modern biology, offering critical insights into the organization, dynamics, and functionality of living organisms. This chapter provides a comprehensive overview of biological networks, including metabolic, gene regulatory, protein-protein interaction (PPI), and signaling networks, each fulfilling distinct cellular roles. Fundamental principles of network analysis, such as topology, modularity, and dynamics, are discussed, emphasizing their importance in decoding biological complexity. The proliferation of high-throughput omics technologies has generated extensive datasets, necessitating robust models, efficient algorithms, and effective data storage solutions. Biological pathways and interactomics data are often represented as graphs, enabling advanced comparative analyses across species. In these graphical representations, nodes signify biomolecules such as proteins, RNAs, or lipids, while edges represent interactions or relationships. PPI networks model biochemical interactions between proteins, and pathway networks outline biochemical reaction cascades within cells or tissues. This chapter explores various modeling frameworks for representing pathways and PPI networks, alongside data-sharing standards and key databases that house interaction data. It also delves into alignment algorithms that facilitate comparative analyses of pathways and PPI networks across organisms. Finally, challenges and limitations in biological network representation and analysis are addressed, with particular focus on network alignment—a complex and evolving area of research critical to advancing our understanding of biological systems.