### Genome-scale fluxes predicted under the guidance of enzyme abundance using a novel hyper-cube shrink algorithm

Motivation： One of the long-expected goals of genome-scale metabolic modelling is to evaluate the influence of the
perturbed enzymes on flux distribution. Both ordinary differential equation (ODE) models and constraint-based models,
like Flux balance analysis (FBA), lack the capacity to perform metabolic control analysis (MCA) for large-scale networks.
Results： In this study, we developed a hyper-cube shrink algorithm (HCSA) to incorporate the enzymatic properties into
the FBA model by introducing a pseudo reaction V constrained by enzymatic parameters. Our algorithm uses the enzymatic
information quantitatively rather than qualitatively. We first demonstrate the concept by applying HCSA to a simple
three-node network, whereby we obtained a good correlation between flux and enzyme abundance. We then validate its
prediction by comparison with ODE and with a synthetic network producing voilacein and analogues in Saccharomyces cerevisiae.
We show that HCSA can mimic the state-state results of ODE. Finally, we show its capability of predicting the flux
distribution in genome-scale networks by applying it to sporulation in yeast. We show the ability of HCSA to operate
without biomass flux and perform MCA to determine rate-limiting reactions.
Availability and implementation：
Algorithm was implemented by Matlab and C ++. The code is available at https://github.com/kekegg/HCSA.

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