The expanding generation of dynamic biological data requires approaches that integrate and analyze information from different types of cellular processes –metabolism, regulation, and signaling–, and ultimately increase our insights into the cell behavior upon perturbation. In the analysis of cellular processes, metabolism appears as the best scaffold to link the topology and crosstalk between regulation and signaling. Multiple methods for the integration of omics data into metabolic networks have been developed, but the dynamic and integrative analyses of cellular processes remain a challenge. Herein, we review the latest approaches to design, integrate and analyze metabolic, regulatory and signaling networks in static and dynamic fashions. We focus on the current challenges in applying these methods, and we highlight kinetic modeling as the promising approach for understanding the interactions and behavior of biological systems.