A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011

By Jeffrey D. Orth, Tom M. Conrad, Jessica Na, Joshua A. Lerman, Hojung Nam, Adam M. Feist, Bernhard O. Palsson. Molecular Systems Biology, 2011

1,090 citations recorded by the source corpus.

The initial genome‐scale reconstruction of the metabolic network of Escherichia coli K‐12 MG1655 was assembled in 2000. It has been updated and periodically released since then based on new and curated genomic and biochemical knowledge. An update has now been built, named iJO1366, which accounts for 1366 genes, 2251 metabolic reactions, and 1136 unique metabolites. iJO1366 was (1) updated in part using a new experimental screen of 1075 gene knockout strains, illuminating cases where alternative pathways and isozymes are yet to be discovered, (2) compared with its predecessor and to experimental data sets to confirm that it continues to make accurate phenotypic predictions of growth on different substrates and for gene knockout strains, and (3) mapped to the genomes of all available sequenced E. coli strains, including pathogens, leading to the identification of hundreds of unannotated genes in these organisms. Like its predecessors, the iJO1366 reconstruction is expected to be widely deployed for studying the systems biology of E. coli and for metabolic engineering applications.

Subfields: Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6fd00b069cc0f446f4318e7fc438f6011e061d46

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Citations in the corpus

Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways (2015, methodology): “To demonstrate the visualization of reaction fluxes, an in silico simulation of anaerobic growth was performed in the Escherichia coli COBRA model i JO1366 using parsi-monious flux balance analysis (pFBA) [26, 27].”
A Whole-Cell Computational Model Predicts Phenotype from Genotype (2012, neutral): “Models of biological processes have been increasing in complexity and scope (Covert et al., 2004; Orth et al., 2011; Thiele et al., 2009), but with efforts at increased inclusiveness of genes, parameters, and molecular functions come a number of challenges.”
A white-box machine learning approach for revealing antibiotic mechanisms of action (2019, positive): “Reaction bounds for oxygen exchange, glucose exchange and cobalamin exchange were as set to values of ‘18.5’, ‘10’ and ‘0.1’, respectively, as previously described (Orth et al., 2011).”
Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction (2013, neutral): “The first reaction network includes all known metabolic pathways as of late 2011 (Orth et al, 2011) and is referred to as the M-Model throughout.”
Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli (2013, methodology): “…(Majewski and Domach, 1990; Varma and Palsson, 1993; Pramanik and Keasling, 1997; Edwards and Palsson, 2000; Reed et al , 2003; Feist et al , 2007; Orth et al , 2011) in the bottom graph, the number of included reactions (blue diamonds), genes (green triangles) and metabolites (purple squares)…”
Fast automated reconstruction of genome-scale metabolic models for microbial species and communities (2018, methodology): “Since ∆Gr could not be determined for all reactions, additional heuristic rules were applied: 1) ATP-consuming reactions were not allowed to proceed in the reverse direction; 2) reactions present in thermodynamically curated models [41, 27] assumed the reversibility constraints adopted in these mode”
COBRApy: COnstraints-Based Reconstruction and Analysis for Python (2013, methodology): “COBRApy comes with variants of the published metabolic network models (M-Models) for Salmonella enterica Typhimurium LT2 [30] and Escherichia coli K-12 MG1655 [31].”
Adaptive laboratory evolution – principles and applications for biotechnology (2013, neutral): “Full-scale models of microorganisms are available [185,186] and we can simulate microbial growth.”