BioModels

This model of Corynebacterium glutamicum was initially published by Y. Zhang et al. (DOI: 10.1186/s13068-017-0856-3. PMID: 28680478; PMCID: PMC5493880) and published in Microsoft Excel Spreadsheet format. M. Feierabend and A. Renz et al. converted this model into SBML Level 3 Version 1 with the SBML extension packages for groups and flux-balance constraints using all information given in the original publication driven by the aim to make this model accessible to the research community. Subsequently, M. Feierabend and A. Renz et al. carefully curated and annotated the model following the MIRIAM guidelines and added specific SBO terms to it.

• High-quality genome-scale reconstruction of Corynebacterium glutamicum ATCC 13032
• Martina Feierabend, Alina Renz, Elisabeth Zelle, Katharina Nöh, Wolfgang Wiechert, Andreas Dräger
• Frontiers in Microbiology , 11/ 2021 , DOI: 10.3389/fmicb.2021.750206
Affiliation: 1. Department of Computer Science, Faculty of Sciences, University of Tübingen, Germany 2. Interfaculty Institute for Biomedical Informatics, University Hospital and Faculty of Medicine, University of Tübingen, Germany 3. Institut für Bio- und Geowissenschaften (IBG), Germany 4. Center for Computational Biomedicine, RWTH Aachen University, Germany
Abstract: Corynebacterium glutamicum belongs to the microbes of enormous biotechnological relevance. In particular, its strain ATCC 13032 is a widely used producer of L-amino acids at an industrial scale. Its apparent robustness also turns it into a favorable platform host for a wide range of further compounds, mainly because of emerging bio-based economies. A deep understanding of the biochemical processes in C. glutamicum is essential for a sustainable enhancement of the microbe’s productivity. Computational systems biology has the potential to provide a valuable basis for driving metabolic engineering and biotechnological advances, such as increased yields of healthy producer strains based on genome-scale metabolic models (GEMs). Advanced reconstruction pipelines are now available that facilitate the reconstruction of GEMs and support their manual curation. This article presents iCGB21FR, an updated and unified GEM of C. glutamicum ATCC 13032 with high quality regarding comprehensiveness and data standards, built with the latest modeling techniques and advanced reconstruction pipelines. It comprises 1,042 metabolites, 1,539 reactions, and 805 genes with detailed annotations and database cross-references. The model validation took place using different media and resulted in realistic growth rate predictions under aerobic and anaerobic conditions. The new GEM produces all canonical amino acids, and its phenotypic predictions are consistent with laboratory data. The in silico model proved fruitful in adding knowledge to the metabolism of C. glutamicum: iCGB21FR still produces L-glutamate with the knock-out of the enzyme pyruvate carboxylase, despite the common belief to be relevant for the amino acid’s production. We conclude that integrating high standards into the reconstruction of GEMs facilitates replicating validated knowledge, closing knowledge gaps, and making it a useful basis for metabolic engineering. The model is freely available from BioModels Database under identifier MODEL2102050001.