A standard approach to estimate intracellular fluxes on a genome-wide scale is flux balance analysis (FBA), which optimizes an objective function subject to constraints on (relations between) fluxes. The performance of FBA models heavily depends on the relevance of the formulated objective function and the completeness of the defined constraints. Previous studies indicated that FBA predictions can be improved by adding regulatory on/off constraints. These constraints were imposed based on either absolute (Shlomi2007a,Covert2004) or relative (Shlomi2008) gene expression values. We provide a new algorithm that directly uses regulatory up/down constraints based on gene expression data in FBA optimization (tFBA). Our assumption is that if the activity of a gene drastically changes from one condition to the other, the flux through the reaction controlled by that gene will change accordingly. The potential of the proposed method, tFBA, is demonstrated through the analysis of fluxes in yeast under nine different cultivation conditions. We illustrate that changes in gene expression are predictive for changes in fluxes. We compare tFBA and FBA predictions to show that our approach yields more biologically relevant results.