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Objective Many of the reactions in a genome-scale reconstruction may not be active under particular conditions or in a particular tissue. To analyze the altered metabolism under different conditions or tissues may help us to understand the tissue specificity. Methods We describe a constraint-based method to predict altered metabolism by integrating gene expression differences under physiological conditions with a genome-scale metabolic network. Applying the proposed method, we successfully predict the altered metabolism of liver compared with heart. Results Integrated the gene expression data in heart tissue and liver tissue, the method gives a more accurate prediction of altered metabolism on the metabolic reaction level obtained through integration of the genome-scale metabolic network compared with an existing method. Conclusions This method provides an efficient computational approach for the genome-wide study of altered metabolism under pairs of tissues in multicellular organisms.