Multi-omics technologies enable parallel quantification of proteomic and metabolomic layers, yet enzyme abundance often shows weak or nonlinear correspondence under diverse biological conditions. This apparent discordance has been attributed to both technical limitations—such as dynamic range compression in LC-MS/MS, metabolite derivatization artifacts, and missing values in proteomic measurements—as well as intrinsic biological properties of metabolic network architecture. While technical factors contribute to cross-omic mismatch, accumulating evidence suggests that constraint-driven network behavior plays a major role in shaping this decoupling. Enzyme abundance constrains catalytic capacity; however, realized flux is selected within this capacity under distributed flux control, as formalized by flux control coefficients in metabolic control analysis, and is further modulated by enzyme kinetics (e.g., km and Vmax), post-translational modifications, substrate availability, and thermodynamic constraints. Metabolite pools, in turn, reflect the physicochemical state of the system, while specific metabolites can also act as regulatory effectors that modulate enzymatic activity and cellular signaling. Because metabolic networks are underdetermined, multiple flux configurations can satisfy identical protein abundance and metabolite concentration data. Static cross-layer correlation is therefore insufficient for mechanistic inference. We synthesize biological mechanisms—including post-translational regulation, allostery, thermodynamic buffering, spatial compartmentalization, feedback amplification, and redox gating—that weaken linear abundance–metabolite expectations. We further outline a constraint-based interpretation framework in which proteomics imposes capacity bounds, metabolomics informs reaction directionality and metabolite pool constraints, and flux-informed approaches reduce solution degeneracy by providing additional information on pathway activity. Moving beyond correlation requires integrating perturbation, temporal resolution, and constraint-aware modeling. Proteome–metabolome discordance should therefore be interpreted not as inconsistency, but as indicative of constraint-driven state selection within high-dimensional biochemical systems.
Kim et al. (Wed,) studied this question.