The hypoxic colorectal cancer (CRC) microenvironment is a complex niche. Hence, in vivo, the metabolism occurring in the cancer cell is not fully known due to difficulties in estimating metabolic fluxes and metabolite exchanges. Genome-scale metabolic modeling helps estimate such metabolic fluxes to gain insights into the metabolic behavior of individual cancer cell types under various tumor microenvironments (TME). We developed a simplified approach to apply proteomics data-based enzyme usage constraints and integrated reactive species (RS) reactions in a context-specific genome-scale metabolic model (GSMM) of HCT116, a CRC cell line. The combined modeling approach reproduced several phenotypes of HCT116 under hypoxia such as the Warburg effect. The integration of the RS module with the hypoxic HCT116 context-specific GSMM highlighted the hypoxia-mediated dysregulation occurring in important metabolic pathways such as hyaluronan metabolism in which 80% of the reactions from the total reactions corresponding to this metabolic pathway were dysregulated. Similarly, 23% of reactions in the urea cycle, 26% of reactions in eicosanoid metabolism and 38% of reactions in glyoxylate and dicarboxylate metabolism were dysregulated.