Critical assessment of genome-scale metabolic models of Arabidopsis thaliana. (26th January 2022)

• Record Type: Journal Article
• Authors: Zamani Amirzakaria, Javad; Marashi, Sayed-Amir; Malboobi, Mohammad Ali; Lohrasebi, Tahmineh; Forouzan, Esmail
• Is Part Of: Molecular omics. Volume 18:Number 4(2022)
• Page Start: 328
• Page End: 335
• Publication Date: 2022-01-26
• Subjects: Molecular biology -- Periodicals; Biochemistry -- Periodicals; Biological systems -- Periodicals; Molecular Biology; Computational Biology; Biochemistry; Biological systems; Molecular biology; Periodicals; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals
• DOI: 10.1039/d1mo00351h
• Languages: English
• ISSNs: 2515-4184

Abstract

Abstract : The quality of a ale metabolic model is important for obtaining accurate predictions. In this study, we have evaluated the quality of available GSMMs for Arabidopsis thaliana . by inspecting some important properties. Abstract : Genome-scale metabolic models (GEMs) have enabled researchers to perform systems-level studies of living organisms. Flux balance analysis (FBA), as a constraint-based technique, enables computation of reaction fluxes and prediction of the metabolic phenotypes of a cell under a set of specified conditions. The quality of a GEM is important for obtaining accurate predictions. In this study, we evaluated the quality of five available GEMs for Arabidopsis thaliana from various points of views. To do this, we inspected some of their important features, including the number of reactions with well-defined gene–protein–reaction rules, number of blocked reactions, mass-unbalanced reactions, prediction accuracy in the simulation of key metabolic functions and existence of erroneous energy generating cycles (EGCs). All of the models were found to include some mass-unbalanced reactions. Moreover, four out of five models were found to include EGCs. However, Aracell includes the maximum number of blocked reactions, which suggests the presence of several incomplete pathways. These results clearly show that simulation by using these models may result in erroneous predictions and all of the publicly available GEMs for A. thaliana require extensive curations before being applied in practice.