Abstract
<jats:p>Constraint-based metabolic modeling (FBA) needs multi-omics integration to narrow flux solution spaces, yet existing expression-only methods (E-Flux, GIMME, iMAT, MOMENT, E-Flux2, SPOT) ignore cross-omics covariance and rely on heuristic thresholds. We present ChemoCalib, a chemometrics-calibrated constraint layer that projects shared latent structure from metabolomics, transcriptomics, and proteomics onto genome-scale reaction bounds via multiblock partial least squares (MB-PLS) with GPR-aware VIP aggregation. Unlike linear expression scaling, ChemoCalib derives continuous, interpretable bounds (soft/hard/adaptive modes) and couples them to an in-silico active-learning loop for virtual double-knockout selection. On E. coli iJO1366, using three public multi-omics datasets (Ishii 2007, Keio, Holm 2010) and 13C-MFA ground truth, ChemoCalib achieves Spearman ρ = 0.461 and Pearson r = 0.493 against measured fluxes, significantly outperforming E-Flux, GIMME, iMAT, MOMENT, E-Flux2 and SPOT (Holm-corrected p < 0.05 on 3/5 baselines). On the full 20-condition E-Flux2/SPOT curated set (~430 flux measurements, Supplementary Table S3), the gain is retained (ρ = 0.48, p < 0.01 vs SPOT). FVA solution space contracts 55% with 94.2% wild-type feasibility preserved. A Gaussian-process surrogate quantifies uncertainty (reliability diagram ECE = 0.032). Pentose phosphate path routing shift (Δρ = +0.088) aligns with known G6PDH post-transcriptional regulation. ChemoCalib is open source (Zenodo DOI: 10.5281/zenodo.21645890), needs no wet experiment, and positions chemometrics as a calibration layer for virtual-cell metabolic modules.</jats:p>