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

Zhengwei Xie | Zendy; Tianyu Zhang | Zendy; Qi Ouyang | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

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

Author(s) -

Zhengwei Xie,

Tianyu Zhang,

Qi Ouyang

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx574

Subject(s) - cube (algebra) , scale (ratio) , abundance (ecology) , algorithm , computer science , computational biology , mathematics , biology , combinatorics , cartography , ecology , geography

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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research