ArticleBMC systems biology2011
The evolution of metabolic networks of E. coli.
Article in BMC systems biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- Identification of specific metabolic capacities associated with major extraintestinal pathogenicJournal of bacteriology · 2026Article
- The EcoCyc database (2025).EcoSal Plus · 2025Review
- Use of genome-scale metabolic reconstructions of avian pathogenicMicrobial genomics · 2025Article
- The EcoCyc Database (2023).EcoSal Plus · 2023Review
- An integrated systems biology approach reveals differences in formate metabolism in the genusiScience · 2023Article
- Localization of Noise in Biochemical Networks.ACS omega · 2023Article
- Comparative Pathogenomics ofInfection and immunity · 2021Article
- Adaptations of Escherichia coli strains to oxidative stress are reflected in properties of their structural proteomes.BMC bioinformatics · 2020Article
- Genome-Scale Metabolic Modeling for Unraveling Molecular Mechanisms of High Threat Pathogens.Frontiers in cell and developmental biology · 2020Review
- Metabolic modelling of chronic wound microbiota predicts mutualistic interactions that drive community composition.Journal of applied microbiology · 2019Article
- Systems Metabolic Engineering of Escherichia coli Improves Coconversion of Lignocellulose-Derived Sugars.Biotechnology journal · 2019Article
- Influence of Plant Species, Tissue Type, and Temperature on the Capacity of Shiga-ToxigenicApplied and environmental microbiology · 2019Article
- Use of genome-scale models to get new insights into the marine actinomycete genus Salinispora.BMC systems biology · 2019Article
- The EcoCyc Database.EcoSal Plus · 2018Review
- Assessing the benefits of horizontal gene transfer by laboratory evolution and genome sequencing.BMC evolutionary biology · 2018Article
- Carbohydrate Utilization in Bacteria: Making the Most Out of Sugars with the Help of Small Regulatory RNAs.Microbiology spectrum · 2018Review
- Interaction and Regulation of Carbon, Nitrogen, and Phosphorus Metabolisms in Root Nodules of Legumes.Frontiers in plant science · 2018Review
- Article
- Using genome-scale metabolic models to compare serovars of the foodborne pathogen Listeria monocytogenes.PloS one · 2018Article
- Microbiota dysbiosis in inflammatory bowel diseases: in silico investigation of the oxygen hypothesis.BMC systems biology · 2017Article
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5 authors.
Funding
Abstract
backgroundDespite the availability of numerous complete genome sequences from E. coli strains, published genome-scale metabolic models exist only for two commensal E. coli strains. These models have proven useful for many applications, such as engineering strains for desired product formation, and we sought to explore how constructing and evaluating additional metabolic models for E. coli strains could enhance these efforts.
resultsWe used the genomic information from 16 E. coli strains to generate an E. coli pangenome metabolic network by evaluating their collective 76,990 ORFs. Each of these ORFs was assigned to one of 17,647 ortholog groups including ORFs associated with reactions in the most recent metabolic model for E. coli K-12. For orthologous groups that contain an ORF already represented in the MG1655 model, the gene to protein to reaction associations represented in this model could then be easily propagated to other E. coli strain models. All remaining orthologous groups were evaluated to see if new metabolic reactions could be added to generate a pangenome-scale metabolic model (iEco1712_pan). The pangenome model included reactions from a metabolic model update for E. coli K-12 MG1655 (iEco1339_MG1655) and enabled development of five additional strain-specific genome-scale metabolic models. These additional models include a second K-12 strain (iEco1335_W3110) and four pathogenic strains (two enterohemorrhagic E. coli O157:H7 and two uropathogens). When compared to the E. coli K-12 models, the metabolic models for the enterohemorrhagic (iEco1344_EDL933 and iEco1345_Sakai) and uropathogenic strains (iEco1288_CFT073 and iEco1301_UTI89) contained numerous lineage-specific gene and reaction differences. All six E. coli models were evaluated by comparing model predictions to carbon source utilization measurements under aerobic and anaerobic conditions, and to batch growth profiles in minimal media with 0.2% (w/v) glucose. An ancestral genome-scale metabolic model based on conserved ortholog groups in all 16 E. coli genomes was also constructed, reflecting the conserved ancestral core of E. coli metabolism (iEco1053_core). Comparative analysis of all six strain-specific E. coli models revealed that some of the pathogenic E. coli strains possess reactions in their metabolic networks enabling higher biomass yields on glucose. Finally the lineage-specific metabolic traits were compared to the ancestral core model predictions to derive new insight into the evolution of metabolism within this species.
conclusionOur findings demonstrate that a pangenome-scale metabolic model can be used to rapidly construct additional E. coli strain-specific models, and that quantitative models of different strains of E. coli can accurately predict strain-specific phenotypes. Such pangenome and strain-specific models can be further used to engineer metabolic phenotypes of interest, such as designing new industrial E. coli strains.
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