A protocol for generating a high-quality genome-scale metabolic reconstruction

By Ines Thiele, Bernhard O. Palsson. Nature Protocols, 2010

1,755 citations recorded by the source corpus.

Network reconstructions are a common denominator in systems biology. Bottom–up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.

Subfields: Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/03b7c925659556098db3825fe7ccad19958f8863

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Citations in the corpus

BiGG Models: A platform for integrating, standardizing and sharing genome-scale models (2015, background): “The workflow recommends that metabolites be linked against existing databases (16), but this is not a formal requirement in the models.”
Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli (2013, background): “…detailed biochemical and enzymatic data (e.g., bibliomic data) on microbial metabolism has led to the reconstruction of metabolic networks at the genome scale (so-called, GENREs or genome-scale reconstructions; Price et al , 2004; Feist et al , 2009; Henry et al , 2010; Thiele and Palsson, 2010).”
The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease (2018, methodology): “High-quality reconstructions go through an intensive manual curation process that follows established protocols to ensure high standards and coverage of the information available on the organism (3).”
Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota (2016, methodology): “New content can be added to an AGORA reconstruction manually or automatically, for example, using rBioNet15, which is compatible with the COBRA toolbox8 and ensures all QC/QA measures defined by the community as described by Thiele and Palsson (2010)9.”
Predicting Network Activity from High Throughput Metabolomics (2013, background): “The reconstruction of microbial metabolic network models is an established process [13,14].”
Using Genome-Scale Models to Predict Biological Capabilities (2015, positive): “This process has been described in the form of a 96-step standard operating procedure (Thiele and Palsson, 2010).”
Recon3D: A Resource Enabling A Three-Dimensional View of Gene Variation in Human Metabolism (2018, positive): “Using a recently established approach 26 , the metabolic network content of Recon3D was expanded to include three-dimensional protein structures from the Protein Data Bank (PDB) 27 as well as homology models (Figure 2(a); Supplementary Figure 5; Supplementary Data Files 11–13; Online Methods).”
A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011 (2011, methodology): “A network reconstruction represents a biochemically, genetically, and genomically structured knowledgebase that contains detailed information about an organism in a structured format (Thiele and Palsson, 2010).”
Mechanisms and consequences of intestinal dysbiosis (2017, background): “The determination of biochemical parameters beyond the classical survey of SCFA profiles adds further dimensions to the characterization of metabolic pathways at play in microbial communities [244].”
The Biomass Objective Function (2010, neutral): “Workflows for how a biomass objective function is formulated have appeared [5,9].”
RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor (2018, background): “Introduction Genome-scale metabolic models (GEMs) are comprehensive in silico representations of the complete set of metabolic reactions that take place in a cell [1].”
Fast automated reconstruction of genome-scale metabolic models for microbial species and communities (2018, background): “…step includes several tasks, such as adding missing reactions required to generate biomass pre-cursors (gap-filling), correcting elemental balance and di-rectionality of reactions, detecting futile cycles, and removing blocked reactions and dead-end metabolites (see (27) for a detailed protocol).”
Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods (2012, background): “…condition-specific, mathematically described, computable derivative of a metabolic reconstruction, containing comprehensive knowledge of metabolism. from careful biochemical and genetic studies or inferred from related organisms, and then catalogued in metabolic reconstruction knowledge bases 1,2 .”
The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum (2013, background): “[1] homocitrate synthase (r0683); [2] homocitrate dehydrase (r0684); [3] homoaconitate hydrase (r0685); [4] homoisocitrate dehydrogenase (r0688); [5] a-aminoadipate aminotransferase (r0689); [6] homoserine transacetylase (r0600); [7] O-acetylhomoserine sulfhydrylase (r0601); [8] cystathione-b-syntha”
High-throughput generation, optimization and analysis of genome-scale metabolic models (2010, methodology): “(4) ONLINE METhODS The Model SEED pipeline consists of seven consecutively applied steps (Fig.”