scholarly journals Erratum: Corrigendum: EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Oliver Hädicke ◽  
Steffen Klamt
2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Oliver Hädicke ◽  
Steffen Klamt

Abstract Genome-scale metabolic modeling has become an invaluable tool to analyze properties and capabilities of metabolic networks and has been particularly successful for the model organism Escherichia coli. However, for several applications, smaller metabolic (core) models are needed. Using a recently introduced reduction algorithm and the latest E. coli genome-scale reconstruction iJO1366, we derived EColiCore2, a model of the central metabolism of E. coli. EColiCore2 is a subnetwork of iJO1366 and preserves predefined phenotypes including optimal growth on different substrates. The network comprises 486 metabolites and 499 reactions, is accessible for elementary-modes analysis and can, if required, be further compressed to a network with 82 reactions and 54 metabolites having an identical solution space as EColiCore2. A systematic comparison of EColiCore2 with its genome-scale parent model iJO1366 reveals that several key properties (flux ranges, reaction essentialities, production envelopes) of the central metabolism are preserved in EColiCore2 while it neglects redundancies along biosynthetic routes. We also compare calculated metabolic engineering strategies in both models and demonstrate, as a general result, how intervention strategies found in a core model allow the identification of valid strategies in a genome-scale model. Overall, EColiCore2 holds promise to become a reference model of E. coli’s central metabolism.


2009 ◽  
Vol 5 (6) ◽  
pp. e1000403 ◽  
Author(s):  
Erwin P. Gianchandani ◽  
Andrew R. Joyce ◽  
Bernhard Ø. Palsson ◽  
Jason A. Papin

2017 ◽  
Vol 9 (10) ◽  
pp. 830-835 ◽  
Author(s):  
Xingxing Jian ◽  
Ningchuan Li ◽  
Qian Chen ◽  
Qiang Hua

Reconstruction and application of genome-scale metabolic models (GEMs) have facilitated metabolic engineering by providing a platform on which systematic computational analysis of metabolic networks can be performed.


PLoS ONE ◽  
2018 ◽  
Vol 13 (8) ◽  
pp. e0202565 ◽  
Author(s):  
Ignace L. M. M. Tack ◽  
Philippe Nimmegeers ◽  
Simen Akkermans ◽  
Filip Logist ◽  
Jan F. M. Van Impe

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