Background: Constraint-based metabolic network models have been widely used in phenotypic
prediction and metabolic engineering design. In recent years, researchers have attempted to improve
prediction accuracy by integrating regulatory information and multiple types of “omics” data
into this constraint-based model. The transcriptome is the most commonly used data type in integration,
and a large number of FBA (flux balance analysis)-based integrated algorithms have been developed.
Methods and Results: We mapped the Kcat values to the tree structure of GO terms and found that the
Kcat values under the same GO term have a higher similarity. Based on this observation, we developed
a new method, called iMTBGO, to predict metabolic flux distributions by constraining reaction
boundaries based on gene expression ratios normalized by marker genes under the same GO term. We
applied this method to previously published data and compared the prediction results with other metabolic
flux analysis methods which also utilize gene expression data. The prediction errors of iMTBGO
for both growth rates and fluxes in the central metabolic pathways were smaller than those of earlier
Conclusion: Considering the fact that reaction rates are not only determined by genes/expression levels,
but also by the specific activities of enzymes, the iMTBGO method allows us to make more precise
predictions of metabolic fluxes by using expression values normalized based on GO.