Next-generation metabolic models: nonlinear resource allocation beyond the proteome

Recent years have seen a convergence between metabolic and whole-cell models, driven by advances that incorporate intracellular component concentrations into metabolic modeling frameworks. Constraint-based models have been extended to include enzyme levels and gene expression machinery, while kinetic models explicitly account for enzyme and metabolite concentrations through nonlinear rate laws. In parallel, coarse-grained self-replicator models have unified these elements within nonlinear optimization frameworks that also capture dilution by growth. Different streams of metabolic modeling are thus converging toward a new class of nonlinear resource allocation models, integrating kinetic rate laws, protein synthesis, and dilution by growth of all model components. These next-generation models are potentially closer to whole-cell models in predictive capacity, while remaining substantially simpler and therefore more readily applicable in research and engineering because of their lower curation demands. In this review, we summarize recent developments in metabolic modeling and outline the prospects for a next generation of metabolic models that integrates these advances.