  One of the long-expected goals of genome-scale metabolic modeling is to
evaluate the influence of the perturbed enzymes to the flux distribution. Both
ordinary differential equation (ODE) models and the constraint-based models,
like Flux balance analysis (FBA), lack of the room of performing metabolic
control analysis (MCA) for large-scale networks. In this study, we developed a
Hyper-Cube Shrink Algorithm (HCSA) to incorporate the enzymatic properties to
the FBA model by introducing a pseudo reaction constrained by enzymatic
parameters. Our algorithm was able to handle not only prediction of knockout
strains but also strains with quantitative adjustment of expression level or
activity. We first demonstrate the concept by applying HCSA to a simplest
three-node network. Then we validate its prediction by comparing with ODE and
with a synthetic network in Saccharomyces cerevisiae producing voilacein and
analogues. Finally we show its capability of predicting the flux distribution
in genome-scale networks by applying it to the sporulation in yeast.
