scholarly journals A workflow for generating multi-strain genome-scale metabolic models of prokaryotes

2019 ◽  
Vol 15 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Charles J. Norsigian ◽  
Xin Fang ◽  
Yara Seif ◽  
Jonathan M. Monk ◽  
Bernhard O. Palsson
Author(s):  
Charles J Norsigian ◽  
Neha Pusarla ◽  
John Luke McConn ◽  
James T Yurkovich ◽  
Andreas Dräger ◽  
...  

Abstract The BiGG Models knowledge base (http://bigg.ucsd.edu) is a centralized repository for high-quality genome-scale metabolic models. For the past 12 years, the website has allowed users to browse and search metabolic models. Within this update, we detail new content and features in the repository, continuing the original effort to connect each model to genome annotations and external databases as well as standardization of reactions and metabolites. We describe the addition of 31 new models that expand the portion of the phylogenetic tree covered by BiGG Models. We also describe new functionality for hosting multi-strain models, which have proven to be insightful in a variety of studies centered on comparisons of related strains. Finally, the models in the knowledge base have been benchmarked using Memote, a new community-developed validator for genome-scale models to demonstrate the improving quality and transparency of model content in BiGG Models.


2022 ◽  
Author(s):  
Javad Zamani ◽  
Sayed-Amir Marashi ◽  
Tahmineh Lohrasebi ◽  
Mohammad-Ali Malboobi ◽  
Esmail Foroozan

Genome-scale metabolic models (GSMMs) have enabled researchers to perform systems-level studies of living organisms. As a constraint-based technique, flux balance analysis (FBA) aids computation of reaction fluxes and prediction of...


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.


2013 ◽  
Vol 7 (1) ◽  
pp. 33 ◽  
Author(s):  
S Riemer ◽  
René Rex ◽  
Dietmar Schomburg

2018 ◽  
Author(s):  
Nhung Pham ◽  
Ruben Van Heck ◽  
Jesse van Dam ◽  
Peter Schaap ◽  
Edoardo Saccenti ◽  
...  

Genome scale metabolic models (GEMs) are manually curated repositories describing the metabolic capabilities of an organism. GEMs have been successfully used in different research areas, ranging from systems medicine to biotechnology. However, the different naming conventions (namespaces) of databases used to build GEMs limit model reusability and prevent the integration of existing models. This problem is known in the GEM community but its extent has not been analyzed in depth. In this study, we investigate the name ambiguity and the multiplicity of non-systematic identifiers and we highlight the (in)consistency in their use in eleven biochemical databases of biochemical reactions and the problems that arise when mapping between different namespaces and databases. We found that such inconsistencies can be as high as 83.1%, thus emphasizing the need for strategies to deal with these issues. Currently, manual verification of the mappings appears to be the only solution to remove inconsistencies when combining models. Finally, we discuss several possible approaches to facilitate (future) unambiguous mapping.


Microbiome ◽  
2017 ◽  
Vol 5 (1) ◽  
Author(s):  
Kees C. H. van der Ark ◽  
Ruben G. A. van Heck ◽  
Vitor A. P. Martins Dos Santos ◽  
Clara Belzer ◽  
Willem M. de Vos

2012 ◽  
Vol 23 (4) ◽  
pp. 617-623 ◽  
Author(s):  
Tae Yong Kim ◽  
Seung Bum Sohn ◽  
Yu Bin Kim ◽  
Won Jun Kim ◽  
Sang Yup Lee

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