Most proteins function by forming complexes within a dynamic interconnected network that underlies various biological mechanisms. To systematically investigate such interactomes, high-throughput techniques including CF-MS have been developed to capture, identify, and quantify protein-protein interactions (PPIs) in large-scale. Compared to other techniques, CF-MS allows the global identification and quantification of native protein complexes in one setting, without genetic manipulation and overexpression. Furthermore, quantitative CF-MS can potentially elucidate the distribution of a protein in multiple co-elution features, informing the stoichiometries and dynamics of a target protein complex. In this issue, Youssef et al. (Proteomics 2023, XX, XXXX-XXXX) combined multiplex CF-MS and an in-house algorithm to study the dynamics of the PPI network for Escherichia coli grown under ten different conditions. While the results demonstrated that while most proteins remained stable, the authors were able to detect disrupted interactions that were growth condition-specific. Further bioinformatics analyses also revealed biophysical properties and structural patterns that govern such a response.