Abstract

Media compositions are important determinants of growth rate and genome-scale models (GSMs) had been used for optimizing media for metabolite production and growth. Recently, iAF1260, a GSM based on Escherichia coli MG1655, was used to study the effects varying glucose concentration in media on growth rate and metabolic fluxes. In this study, the effects of other media components in the presence of varying glucose concentrations on the predicted growth rate of E. coli MG1655 were examined. Our results show that 10 media components (ammonium, calcium, chloride, copper, glucose, manganese, magnesium, molybdate, phosphate, and potassium) demonstrate substantial impact on the predicted growth rate of E. coli MG1655. Of which, 4 components (glucose, ammonium, magnesium, and phosphate) have the most impact. However, our results also demonstrate the limitations of iAF1260 as media components that had been shown to affect E. coli growth rate were not reflected by the model.

Keywords: Growth Rate; Genome-Scale Models; Media Optimization

References

1. Miyashira CH., et al. “Comparison of radial growth rate of the mutualistic fungus of Atta sexdens rubropilosa forel in two culture media”. Brazilian Journal of Microbiology 2 (2010): 506-511.
2. Bich GA., et al. “Isolation of the symbiotic fungus of Acromyrmex pubescens and phylogeny of Leucoagaricus gongylophorus from leaf-cutting ants”. Saudi Journal of Biological Science 4 (2017): 851-856.
3. Haugan MS., et al. “Comparative Activity of Ceftriaxone, Ciprofloxacin, and Gentamicin as a Function of Bacterial Growth Rate Probed by Escherichia coli Chromosome Replication in the Mouse Peritonitis Model”. Antimicrobe Agents Chemotherapy2 (2019): e02133-02118.
4. Greulich P., et al. “Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics”. Molecular Systems Biology 3 (2015): 796.
5. De Leersnyder I., et al. “Influence of growth media components on the antibacterial effect of silver ions on Bacillus subtilis in a liquid growth medium”. Science Report1 (2018): 9325.
6. Ratiu I-A., et al. “The effect of growth medium on an Escherichia coli pathway mirrored into GC/MS profiles”. Journal of Breath Research3 (2017): 036012.
7. Tavafi H., et al. “Screening of Alginate Lyase-Producing Bacteria and Optimization of Media Compositions for Extracellular Alginate Lyase Production”. Iran Biomed Journal1 (2017): 48-56.
8. Pustake SO., et al. “Statistical media optimization for the production of clinical uricase from Bacillus subtilis strain SP6”. Heliyon5 (2019): e01756.
9. Podleśny M., et al. “Media optimization for economic succinic acid production by Enterobacter LU1”. AMB Express 7.1 (2017): 126.
10. Kamzolova SV., et al. “Fermentation Conditions and Media Optimization for Isocitric Acid Production from Ethanol by Yarrowia lipolytica”. BioMed Research International (2018): 2543210.
11. McCloskey D., et al. “Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli”. Molecular Systems Biology 9 (2013): 661.
12. Mishra P., et al. “Genome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolytica”. BMC System Biology 12 (2018): 12.
13. Iranmanesh E., et al. “Improving l-phenylacetylcarbinol production in Saccharomyces cerevisiae by in silico aided metabolic engineering”. Journal of Biotechnology 308 (2020): 27-34.
14. Kim M., et al. “In silico identification of metabolic engineering strategies for improved lipid production in Yarrowia lipolytica by genome-scale metabolic modelling”. Biotechnology and Biofuels 12 (2019): 187.
15. Ashino K., et al. “Predicting the decision making chemicals used for bacterial growth”. Science Report1 (2019): 7251.
16. O’Brien EJ., et al. “Using Genome-scale Models to Predict Biological Capabilities”. Cell 5 (2015): 971-987.
17. Chen Y., et al. “An unconventional uptake rate objective function approach enhances applicability of genome-scale models for mammalian cells”. npj Systems Biology and Applications 5 (2019): 25.
18. 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.
19. Chang ED and Ling MH. “Explaining Monod in terms of Escherichia coli metabolism”. Acta Scientific Microbiology9 (2019): 66-71.
20. Cronan JE. “Escherichia coli as an Experimental Organism”. In: John Wiley and Sons Ltd, editor. eLS [Internet]. Chichester, UK: John Wiley and Sons, Ltd (2014).
21. Cardoso JGR., et al. “Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories”. ACS Synthetic Biology4 (2018): 1163-1166.
22. King ZA., et al. “BiGG Models: A platform for integrating, standardizing and sharing genome-scale models”. Nucleic Acids Research 44 (2016): D515-522.
23. Lewis NE., et al. “Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models”. Molecular Systems Biology 6 (2010): 390.
24. Abou-Taleb KA and Galal GF. “A comparative study between one-factor-at-a-time and minimum runs resolution-IV methods for enhancing the production of polysaccharide by Stenotrophomonas daejeonensis and Pseudomonas geniculate”. Annuals of Agriculture Science2 (2018): 173-180.
25. Razavi S and Gupta HV. “What do we mean by sensitivity analysis? The need for comprehensive characterization of “global” sensitivity in Earth and Environmental systems models: A Critical Look at Sensitivity Analysis”. Water Resource Research5 (2015): 3070-3092.
26. Akaike H. “Information theory and an extension of the maximum likelihood principle”. In: Selected Papers of Hirotugu Akaike. Springer (1998): 199-213.
27. Ripley B., et al. “Package ‘mass’”. Cran R (2013)
28. Viana FAC. “A Tutorial on Latin Hypercube Design of Experiments”. Quality and Reliability Engineering International 5 (2016): 1975-85.
29. M9 minimal medium (standard). Cold Spring Harbor Protocol (2010): pdb.rec12295.
30. Peterson CN., et al. “Escherichia coli Starvation Diets: Essential Nutrients Weigh in Distinctly”. Journal of Bacteriology22 (2005): 7549-7553.
31. McCarthy BJ. “The effects of magnesium starvation on the ribosome content of Escherichia coli”. 55.6 (1962): 880-889.
32. Lusk JE., et al. “Magnesium and the growth of Escherichia coli”. Journal of Biological Chemistry10 (1968): 2618-2624.
33. Kertesz MA., et al. “Proteins induced by sulfate limitation in Escherichia coli, Pseudomonas putida, or Staphylococcus aureus”. Journal of Bacteriology4 (1993): 1187-1190.
34. Omotoyinbo O and Omotoyinbo B. “Effect of varying NaCl concentrations on the growth curve of Escherichia coli and Staphylococcus aureus”. Cell Biology5 (2016): 31-34.
35. Goh DJ., et al. “Gradual and Step-wise Halophilization Enables Escherichia coli ATCC 8739 to Adapt to 11% NaCl”. Electron Physician3 (2012): 527-535.
36. How JA., et al. “Adaptation of Escherichia coli ATCC 8739 to 11% NaCl”. Dataset Papers in Biology (2013).
37. Dupree DE., et al. “Effects of Sodium Chloride or Calcium Chloride Concentration on the Growth and Survival of Escherichia coli O157:H7 in Model Vegetable Fermentations”. Journal of Food Protection4 (2019): 570-578.
38. Khodayari A and Maranas CD. “A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains”. Nature Communication 7 (2016): 13806.

Citation

Citation: Maurice HT Ling., et al. “A Simulation Study on the Effects of Media Composition on the Growth Rate of Escherichia coli MG1655 Using iAF1260 Model". Acta Scientific Microbiology 3.8 (2020): 40-44.

Copyright

Copyright: © 2020 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.