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.

Contact: xiezhengwei@hsc.pku.edu.cn or qi@pku.edu.cn.

Supplementary information: Supplementary data are available at Bioinformatics online.

• PMID: 28968667
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Reference Type

Journal Article | Research Support, Non-U.S. Gov't

Authors

Xie Z, Zhang T, Ouyang Q

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