Abstract
<jats:p>Genome-scale metabolic models (GEMs) are widely used to relate genotype to phenotype and to study how organisms respond to their environment, but conventional flux-balance analysis relies on simplifying assumptions that limit its predictive power. Enzyme-constrained models (ecModels) address this by bounding each reaction by the abundance and turnover number of its catalyzing enzyme. Here we reconstruct ecModels of Synechocystis sp. PCC 6803 at light-limited, light-saturated and photoinhibited intensities (27.5, 440 and 1100 umol photons m-2 s-1) to study how light shapes its metabolism and enzyme usage. Using the GECKO framework, we constrained the model first by a total protein pool and then by condition-specific quantitative proteomics. The pool-constrained ecModel reproduced the decline in growth at high light as a consequence of a finite proteome, whereas the unconstrained GEM predicted growth to continue rising. Integrating proteomics reduced the median flux variability across reactions by up to two orders of magnitude relative to the conventional GEM. The enzyme budget was dominated by four subsystems (oxidative phosphorylation, transport, photosynthesis and carbon fixation), which together accounted for close to 70% of the minimum enzyme mass in every condition, and the total mass required tracked growth rate rather than light intensity. Enzyme-usage variability analysis found that only about 40% of usages were uniquely determined, a fraction stable across the light gradient. Enzyme constraints thus improve the model's description of light-dependent cyanobacterial metabolism and identify the functional sectors that carry the metabolic protein budget.</jats:p>