OBJECTIVE One of the long-expected goals of genome-scale metabolic modeling is to evaluate the influence of the perturbed enzymes to the yield of an expected end product.METHDOS Metabolic control analysis(MCA)performs such role to calculate the sensitivity of flux change upon that of enzymes under the framework of ordinary differential equation(ODE)models,which are restricted in small-scale networks and require explicit kinetic parameters.The constraint-based models,like flux balance analysis(FBA),lack of the room of performing MCA because they are parameters-free.In this study,we developed a hyper-cube shrink algorithm(HCSA)to incorporate the enzymatic properties to the FBA model by introducing a pair of parameters for each reaction.Our algorithm was able to handle not only prediction of knockout strains but also strains with an adjustment of expression level of certain enzymes.RESULTS We first showed the concept by applying HCSA to a simplest three-nodes network.Then we show the HCSA possesses Michaelis-Menten like behaviors characterized by steady state of ODE.We obtained good prediction of a synthetic network in Saccharomyces cerevisiae producing voilacein and analogues.Finally we showed its capability of predicting the flux distribution in genome-scale networks by applying it to sporulation in yeast.CONCLUSION We have developed an algorithm the impact on fluxes when certain enzymes were inhibited or activated.It provides us a powerful tool to evaluate the consequences of enzyme inhibitor or activator. OBJECTIVE One of the long-expected goals of genome-scale metabolic modeling is to evaluate the influence of the perturbed enzymes to the yield of an expected end product. METHDOS Metabolic control analysis (MCA) perform such role to calculate the sensitivity of flux change upon that of enzymes under the framework of ordinary differential equation (ODE) models, which are restricted in small-scale networks and require explicit kinetic parameters. constraint-based models, like flux balance analysis (FBA), lack of the room of performing MCA because they are parameters-free.In this study, we developed a hyper-cube shrink algorithm (HCSA) to incorporating the enzymatic properties to the FBA model by introducing a pair of parameters for each reaction. Our algorithm was able to handle not only prediction of knockout also but also with an adjustment of expression level of certain enzymes. RESULTS We first showed the concept by applying HCSA to a simplest three-nodes network. possesses Michaelis-Mentenlike behaviors stable by steady state of ODE. We obtained good prediction of a synthetic network in Saccharomyces cerevisiae producing voilacein and analogues. Finaally we showed its capability of predicting the flux distribution in genome-scale networks by applying it to sporulation in yeast. CONCLUSION We have developed an algorithm the impact on fluxes when certain enzymes were inhibited or activated. It provides us a powerful tool to evaluate the consequences of enzyme inhibitor or activator.