Acta Scientific Microbiology (ISSN: 2581-3226)

Research Article Volume 5 Issue 2

Significant Differences in Media Components and Predicted Growth Rates of 58 Escherichia coli Genome-scale Models

Felicia Leyi Tan, Zhi Jue Kuan, Nabil Amir-Hamzah, Xander Kng, Yik Yew Wee, Si Xian Sor and Maurice HT Ling*

School of Applied Sciences, Temasek Polytechnic, Singapore

*Corresponding Author: Maurice HT Ling, School of Applied Sciences, Temasek Polytechnic, Singapore.

Received: January 03, 2022; Published: January 27, 2022


Escherichia coli is a common host for metabolite production and genome-scale metabolic models (GSMs) is an important computational tool to aid in such experimental design. As of September 30, 2021; 58 GSMs have been registered with BiGG database. However, these GSMs had been built for different applications and no large-scale comparative study had been performed to-date. In this study, we examine the media components and predicted growth rates of these 58 GSMs using flux balance analysis across various glucose uptake rates. Only 5 out of 29 uptake rates (as proxy for media components) are common in all 58 GSMs; namely, proton, water, ammonium, oxygen, and phosphate. 74.25% (2370 of the 3192) pairwise comparisons of predicted growth rates show significant differences (p-value < 0.05) and 34 of 42 pairwise comparisons of predicted growth rates within the same strain are significantly different. Hence, our results demonstrated substantial differences in media components and significant differences in predicted growth rates between the GSMs and even within GSMs constructed for the same strain.

Keywords: Escherichia coli; Genome-scale Metabolic Models (GSMs); Ammonium


  1. Idalia V-MN and Bernardo F. “Escherichia coli as a Model Organism and Its Application in Biotechnology”. In: Samie A, editor. Escherichia coli - Recent Advances on Physiology, Pathogenesis and Biotechnological Applications. InTech (2017).
  2. Yang D., et al. “Metabolic Engineering of Escherichia coli for Natural Product Biosynthesis”. Trends in Biotechnology 7 (2020): 745-765.
  3. García-Granados R., et al. “Metabolic Engineering and Synthetic Biology: Synergies, Future, and Challenges”. Frontiers in Bioengineering and Biotechnology 7 (2019): 36.
  4. Murthy MV., et al. “UniKin1: A Universal, Non-Species-Specific Whole Cell Kinetic Model”. Acta Scientific Microbiology 3.10 (2020): 04-08.
  5. Cho JL and Ling MH. “Adaptation of Whole Cell Kinetic Model Template, UniKin1, to Escherichia coli Whole Cell Kinetic Model, ecoJC20”. EC Microbiology2 (2021): 254-260.
  6. Comba S., et al. “Emerging Engineering Principles for Yield Improvement in Microbial Cell Design”. Computational and Structural Biotechnology Journal4 (2012): e201210016.
  7. Cardoso JGR., et al. “Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories”. ACS Synthetic Biology 4 (2018): 1163-1166.
  8. Ling MH. “AdvanceSyn Toolkit: An Open Source Suite for Model Development and Analysis in Biological Engineering”. MOJ Proteomics Bioinformation4 (2020): 83-86.
  9. O’Brien EJ., et al. “Using Genome-scale Models to Predict Biological Capabilities”. Cell5 (2015): 971-987.
  10. Srinivasan S., et al. “Constructing Kinetic Models of Metabolism at Genome-Scales: A Review”. Biotechnology Journal9 (2015): 1345-59.
  11. Simeonidis E and Price ND. “Genome-Scale Modeling for Metabolic Engineering”. Journal of Industrial Microbiology and Biotechnology 3 (2015): 327-338.
  12. Gu C., et al. “Current Status and Applications of Genome-Scale Metabolic Models”. Genome Biology1 (2019): 121.
  13. Kim B., et al. “Applications of Genome-Scale Metabolic Network Model in Metabolic Engineering”. Journal of Industrial Microbiology and Biotechnology 3 (2015): 339-348.
  14. Kocabaş P., et al. “Analyses of Extracellular Protein Production in Bacillus subtilis - II: Responses of Reaction Network to Oxygen Transfer at Transcriptional Level”. Biochemical Engineering Journal 127 (2017): 242-261.
  15. Kavvas ES., et al. “Updated and Standardized Genome-Scale Reconstruction of Mycobacterium tuberculosis H37Rv, iEK1011, Simulates Flux States Indicative of Physiological Conditions”. BMC Systems Biology 1 (2018): 25.
  16. Edwards JS and Palsson BO. “The Escherichia coli MG1655 In Silico Metabolic Genotype: Its Definition, Characteristics, and Capabilities”. Proceedings of the National Academy of Sciences of the United States of America10 (2000): 5528-5533.
  17. Ye C., et al. “Improving Lysine Production Through Construction of an Escherichia coli Enzyme‐Constrained Model”. Biotechnology and Bioengineering11 (2020): 3533-3544.
  18. Cheong KC., et al. “A Simulation Study on the Effects of Media Composition on the Growth Rate of Escherichia coli MG1655 Using iAF1260 Model”. Acta Scientific Microbiology8 (2020): 40-44.
  19. Chang ED and Ling MH. “Explaining Monod in terms of Escherichia coli metabolism”. Acta Scientific Microbiology9 (2019): 66-71.
  20. Mienda BS. “Escherichia coli Genome-Scale Metabolic Gene Knockout of Lactate Dehydrogenase (ldhA), Increases Succinate Production from Glycerol”. Journal of Biomolecular Structure and Dynamics 14 (2018): 3680-3686.
  21. Immanuel SRC., et al. “Integrated Constraints Based Analysis of An Engineered Violacein Pathway in Escherichia coli”. Biosystems 171 (2018): 10-19.
  22. King ZA., et al. “BiGG Models: A Platform for Integrating, Standardizing and Sharing Genome-Scale Models”. Nucleic Acids ResearchD1 (2016): D515-522.
  23. Feist AM and Palsson BO. “The Biomass Objective Function”. Current Opinion in Microbiology 3 (2010): 344-349.
  24. Orth JD., et al. “What is Flux Balance Analysis?” Nature Biotechnology3 (2010): 245-248.
  25. Peterson JR., et al. “Parametric Studies of Metabolic Cooperativity in Escherichia coli Colonies: Strain and Geometric Confinement Effects”. PLoS ONE8 (2017): e0182570.
  26. Orth JD., et al. “A Comprehensive Genome‐Scale Reconstruction of Escherichia coli Metabolism — 2011”. Molecular Systems Biology 1 (2011): 535.
  27. Monk JM., et al. “Genome-Scale Metabolic Reconstructions of Multiple Escherichia coli Strains Highlight Strain-Specific Adaptations to Nutritional Environments”. Proceedings of the National Academy of Sciences of the United States of America50 (2013): 20338-20343.
  28. Wang C., et al. “An Aldolase-Catalyzed New Metabolic Pathway for the Assimilation of Formaldehyde and Methanol To Synthesize 2-Keto-4-hydroxybutyrate and 1,3-Propanediol in Escherichia coli”. ACS Synthetic Biology11 (2019): 2483-2493.
  29. Reed JL., et al. “An Expanded Genome-Scale Model of Escherichia coli K-12 (iJR904 GSM/GPR)”. Genome Biology9 (2003): R54.
  30. Costa RS and Vinga S. “Assessing Escherichia coli Metabolism Models and Simulation Approaches in Phenotype Predictions: Validation Against Experimental Data”. Biotechnology Progress6 (2018): 1344-1354.
  31. Liu L., et al. “DEF: An Automated Dead-End Filling Approach Based On Quasi-Endosymbiosis”. Bioinformatics3 (2017): 405-413.
  32. Feist AM., et al. “A Genome-Scale Metabolic Reconstruction for Escherichia coli K-12 MG1655 that Accounts for 1260 ORFs and Thermodynamic Information”. Molecular Systems Biology 3 (2007): 121.
  33. Tamura T. “Grid-Based Computational Methods for the Design of Constraint-Based Parsimonious Chemical Reaction Networks to Simulate Metabolite Production: GridProd”. BMC Bioinformatics1 (2018): 325.
  34. Orth J., et al. “Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide”. EcoSal Plus1 (2010): ecosalplus.10.2.1.
  35. de Arroyo Garcia L and Jones PR. “In Silico Co-Factor Balance Estimation Using Constraint-Based Modelling Informs Metabolic Engineering in Escherichia coli”. PLOS Computational Biology 8 (2020): e1008125.
  36. Apaydin M., et al. “Robust Mutant Strain Design by Pessimistic Optimization”. BMC GenomicsS6 (2017): 677.
  37. Feist AM., et al. “Model-Driven Evaluation of the Production Potential for Growth-Coupled Products of Escherichia coli”. Metabolic Engineering3 (2010): 173-186.
  38. Jenior ML., et al. “Transcriptome-Guided Parsimonious Flux Analysis Improves Predictions with Metabolic Networks in Complex Environments”. PLOS Computational Biology 4 (2020): e1007099.
  39. Bekiaris PS and Klamt S. “Automatic Construction of Metabolic Models with Enzyme Constraints”. BMC Bioinformatics 1 (2020): 19.
  40. Kim D., et al. “Development of a Genome-Scale Metabolic Model and Phenome Analysis of the Probiotic Escherichia coli Strain Nissle 1917”. International Journal of Molecular Sciences 4 (2021): 2122.
  41. Chu HY., et al. “Assessing the Benefits of Horizontal Gene Transfer by Laboratory Evolution and Genome Sequencing”. BMC Evolutionary Biology 1 (2018): 54.
  42. Hosseini S-R and Wagner A. “Genomic Organization Underlying Deletional Robustness in Bacterial Metabolic Systems”. Proceedings of the National Academy of Sciences of the United States of America27 (2018): 7075-7080.
  43. Kim H., et al. “Metabolic Network Reconstruction and Phenome Analysis of the Industrial Microbe, Escherichia coli BL21 (DE3)”. PLOS ONE9 (2018): e0204375.
  44. Zabeti H., et al. “A Duality-Based Method for Identifying Elemental Balance Violations in Metabolic Network Models”. In: 18th International Workshop on Algorithms in Bioinformatics (WABI 2018). Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, Wadern/Saarbruecken, Germany (2018): 1-13. (Leibniz International Proceedings in Informatics (LIPIcs)).
  45. Wu G. “Revelation of Yin-Yang Balance in Microbial Cell Factories by Data Mining, Flux Modeling, and Metabolic Engineering [Doctor of Philosophy]”. [School of Engineering and Applied Science]: Washington University (2016).
  46. Patané A., et al. “Multi-Objective Optimization of Genome-Scale Metabolic Models: The Case of Ethanol Production”. Annals of Operations Research1-2 (2019): 211-227.
  47. Gerstl MP., et al. “Exact Quantification of Cellular Robustness in Genome-Scale Metabolic Networks”. Bioinformatics5 (2016): 730-737.
  48. Monk JM., et al. “Multi-Omics Quantification of Species Variation of Escherichia coli Links Molecular Features with Strain Phenotypes”. Cell System3 (2016): 238-251.e12.
  49. Monk JM., et al. “iML1515, A Knowledgebase that Computes Escherichia coli Traits”. Nature Biotechnology10 (2017): 904-908.
  50. Amin SA., et al. “Towards Creating An Extended Metabolic Model (EMM) for E. coli Using Enzyme Promiscuity Prediction and Metabolomics Data”. Microbe Cell Factories1 (2019): 109.
  51. Hädicke O and Klamt S. “EColiCore2: A Reference Network Model of the Central Metabolism of Escherichia coli and Relationships to its Genome-Scale Parent Model”. Scientific Report 1 (2017): 39647.
  52. Clement TJ., et al. “Unlocking Elementary Conversion Modes: ecmtool Unveils All Capabilities of Metabolic Networks”. Patterns1 (2021): 100177.
  53. Heinonen M., et al. “Bayesian Metabolic Flux Analysis Reveals Intracellular Flux Couplings”. Bioinformatics 14 (2019): i548-557.
  54. Venayak N., et al. “MoVE Identifies Metabolic Valves to Switch Between Phenotypic States”. Nature Communication1 (2018): 5332.
  55. Serres MH., et al. “A Functional Update of the Escherichia coli K-12 Genome”. Genome Biology9 (2001): RESEARCH0035.
  56. Gyawali R., et al. “Antimicrobial Activity of Copper Alone and in Combination with Lactic Acid against Escherichia coli O157:H7 in Laboratory Medium and on the Surface of Lettuce and Tomatoes”. Journal of Pathogens 2011 (2011): 650968.
  57. Ranquet C., et al. “Cobalt Stress in Escherichia coli”. Journal of Biological Chemistry42 (2007): 30442-30451.
  58. Majtan T., et al. “Effect of Cobalt on Escherichia coli Metabolism and Metalloporphyrin Formation”. An International Journal on the Role of Metal Ions in Biology, Biochemistry and Medicine 24.2 (2011): 335-347.
  59. Ratledge C., et al. “Effect of Iron and Zinc on Growth Patterns of Escherichia coli in Iron-Deficient Medium”. Journal of Bacteriology 87 (1964): 823-827.
  60. Appenzeller BMR., et al. “Advantage Provided by Iron for Escherichia coli Growth and Cultivability in Drinking Water”. Applied and Environmental Microbiology 9 (2005): 5621-5623.
  61. McEwan AG. “New Insights into the Protective Effect of Manganese Against Oxidative Stress”. Molecular Microbiology4 (2009): 812-814.
  62. Baez A and Shiloach J. “Escherichia coli Avoids High Dissolved Oxygen Stress by Activation of SoxRS and Manganese-Superoxide Dismutase”. Microbe Cell Factories1 (2013): 23.
  63. Mardare CC., et al. “Growth Inhibition of Escherichia coli by Zinc Molybdate with Different Crystalline Structures: Growth Inhibition of Escherichia coli by Zinc Molybdate”. Physica Status Solidi 6 (2016): 1471-1478.
  64. Gates AJ., et al. “Properties of the Periplasmic Nitrate Reductases from Paracoccus pantotrophus and Escherichia coli after Growth in Tungsten-Supplemented Media”. FEMS Microbiology Letter2 (2003): 261-269.
  65. Durfee T., et al. “The Complete Genome Sequence of Escherichia coli DH10B: Insights into the Biology of a Laboratory Workhorse”. Journal of Bacteriology7 (2008): 2597-2606.
  66. Skovgaard O., et al. “Genome-Wide Detection of Chromosomal Rearrangements, Indels, and Mutations in Circular Chromosomes by Short Read Sequencing”. Genome Research8 (2011): 1388-1393.
  67. Anfora AT., et al. “Uropathogenic Escherichia coli CFT073 is Adapted to Acetatogenic Growth But Does Not Require Acetate During Murine Urinary Tract Infection”. Infection and Immunity12 (2008): 5760-5767.
  68. Fürnkranz J, ., et al. “Manhattan Distance”. In: Sammut C, Webb GI, editors. Encyclopedia of Machine Learning. Boston, MA: Springer US (2011): 639-639.
  69. Nouri H., et al. “A Reconciliation of Genome-Scale Metabolic Network Model of Zymomonas mobilis ZM4”. Scientific Report1 (2020): 7782.
  70. Shiratsubaki IS., et al. “Genome-Scale Metabolic Models Highlight Stage-Specific Differences in Essential Metabolic Pathways in Trypanosoma cruzi”. PLOS Neglected Tropical Diseases 10 (2020): e0008728.
  71. van Duuren JBJH., et al. “Reconciling In Vivo and In Silico Key Biological Parameters of Pseudomonas putida KT2440 During Growth on Glucose Under Carbon-Limited Condition”. BMC Biotechnology 13 (2013): 93.
  72. Contador CA., et al. “Use of Genome-Scale Models to Get New Insights into the Marine Actinomycete Genus Salinispora”. BMC Systems Biology1 (2019): 11.


Citation: Maurice HT Ling., et al. “Significant Differences in Media Components and Predicted Growth Rates of 58 Escherichia coli Genome-scale Models". Acta Scientific Microbiology 5.2 (2022): 56-68.


Copyright: © 2022 Maurice HT Ling., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Acceptance rate30%
Acceptance to publication20-30 days

Indexed In

News and Events

  • Certification for Review
    Acta Scientific certifies the Editors/reviewers for their review done towards the assigned articles of the respective journals.
  • Submission Timeline for Upcoming Issue
    The last date for submission of articles for regular Issues is June 25, 2024.
  • Publication Certificate
    Authors will be issued a "Publication Certificate" as a mark of appreciation for publishing their work.
  • Best Article of the Issue
    The Editors will elect one Best Article after each issue release. The authors of this article will be provided with a certificate of "Best Article of the Issue"
  • Welcoming Article Submission
    Acta Scientific delightfully welcomes active researchers for submission of articles towards the upcoming issue of respective journals.

Contact US