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Comput Biol Med. 2019 Feb;105:64-71. doi: 10.1016/j.compbiomed.2018.12.010. Epub 2018 Dec 17.

A simplified metabolic network reconstruction to promote understanding and development of flux balance analysis tools.

Author information

1
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA.
2
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA; Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, 22908, USA.
3
Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, USA.
4
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA. Electronic address: papin@virginia.edu.

Abstract

GEnome-scale Network REconstructions (GENREs) mathematically describe metabolic reactions of an organism or a specific cell type. GENREs can be used with a number of constraint-based reconstruction and analysis (COBRA) methods to make computational predictions on how a system changes in different environments. We created a simplified GENRE (referred to as iSIM) that captures central energy metabolism with nine metabolic reactions to illustrate the use of and promote the understanding of GENREs and constraint-based methods. We demonstrate the simulation of single and double gene deletions, flux variability analysis (FVA), and test a number of metabolic tasks with the GENRE. Code to perform these analyses is provided in Python, R, and MATLAB. Finally, with iSIM as a guide, we demonstrate how inaccuracies in GENREs can limit their use in the interrogation of energy metabolism.

KEYWORDS:

Computational modeling; Flux balance analysis; Metabolic engineering; Metabolic networks; Systems biology

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