Acta Scientific Microbiology (ASMI) (ISSN: 2581-3226)

Research Article Volume 3 Issue 10

UniKin1: A Universal, Non-Species-Specific Whole Cell Kinetic Model

Madhurya V Murthy1,2, Dakshahini Balan1,2, Nur Jannah Kamarudin1,2, Victor CC Wang1,2, Xue Ting Tan1,2, Avettra Ramesh1,2, Shermaine SM Chew1,2, Nikita V Yablochkin1,2, Karthiga Mathivanan1,2 and Maurice HT Ling1,2,3*

1Department of Applied Sciences, Northumbria University, United Kingdom
2School of Life Sciences, Management Development Institute of Singapore, Singapore
3HOHY PTE LTD, Singapore

*Corresponding Author: Maurice HT Ling, School of Life Sciences, Management Development Institute of Singapore, Singapore.

Received: August 01, 2020; Published: September 16, 2020

×

Abstract

  Mathematical models of metabolism can be a useful tool for metabolic engineering. Genome-scale models (GSMs) and kinetic models (KMs) are the two main types of models. GSMs provide steady-state fluxes while KMs provide time-course profile of metabolites, which has more advantage in identifying metabolic bottlenecks. However, KMs require greater degree of accuracy for parameters than GSMs resulting in fewer large-scale KMs than GSMs. Recently, large-scale KMs have been developed but are not based on standard enzymatic rate equations resulting in difficulty in interpreting results in terms of enzyme kinetics. Here, we construct a universal, non-species-specific KM of core metabolism, based on Michaelis-Menten Equation, from glucose to the 20 amino acids and 5 nucleotides based on reactions listed in Kyoto Encyclopaedia of Genes and Genomes (KEGG). Non-species specificity is achieved by using the same Michaelis-Menten constant (Km), turnover number (Vmax), and concentration for each metabolite and enzyme for each equation. This forms a base model for developing species-specific whole cell KMs. The resulting model consists of 566 reactions, 306 metabolites, and 310 enzymes, involving in 1284 metabolite productions, and 1249 metabolite usages. Sensitivity analysis shows that 85% of the metabolite concentration changes with the change of one enzyme kinetic parameter. This forms a base model for developing species-specific whole cell KMs.

Keywords: Kyoto Encyclopaedia of Genes and Genomes (KEGG); Turnover Number; Kinetic Models

×

References

  1. Shwab K. “The Fourth Industrial Revolution: What It Means, How to Respond”. Foreign Affair 12 (2015): 2015-7.
  2. Ramzi AB. “Metabolic Engineering and Synthetic Biology”. Advances in Experimental Medicine and Biology 1102 (2018): 81-95.
  3. García-Granados R., et al. “Metabolic Engineering and Synthetic Biology: Synergies, Future, and Challenges”. Frontiers in Bioengineering and Biotechnology 7 (2019): 36.
  4. Keasling JD. “Manufacturing Molecules Through Metabolic Engineering”. Science6009 (2010): 1355-1358.
  5. Kim OD., et al. “A Review of Dynamic Modeling Approaches and Their Application in Computational Strain Optimization for Metabolic Engineering”. Frontiers in Microbiology 9 (2018): 1690.
  6. Machado D., et al. “Exploring the Gap Between Dynamic and Constraint-Based Models of Metabolism”. Metabolic Engineering 2 (2012): 112-119.
  7. Reed JL. “Genome-Scale Metabolic Modeling and Its Application to Microbial Communities”. In: The Chemistry of Microbiomes. National Academies Press (2017).
  8. Fernandez-de-Cossio-Diaz J., et al. “Characterizing Steady States of Genome-Scale Metabolic Networks in Continuous Cell Cultures”. Nielsen J, editor. PLOS Computational Biology 11 (2017): e1005835.
  9. Yang JE., et al. “One-Step Fermentative Production of Aromatic Polyesters from Glucose by Metabolically Engineered Escherichia coli Strains”. Nature Communications1 (2018): 79.
  10. Wang RS. “Ordinary Differential Equation (ODE), Model”. In: Dubitzky W, Wolkenhauer O, Cho K-H, Yokota H, editors. Encyclopedia of Systems Biology. New York, NY: Springer New York (2013): 1606-1608.
  11. Khodayari A., et al. “A Kinetic Model of Escherichia coli Core Metabolism Satisfying Multiple Sets of Mutant Flux Data”. Metabolic Engineering 25 (2014): 50-62.
  12. Khodayari A., et al. “Succinate Overproduction: A Case Study of Computational Strain Design Using a Comprehensive Escherichia coli Kinetic Model”. Frontiers in Bioengineering and Biotechnology 2 (2014): 76.
  13. Khodayari A and Maranas CD. “A Genome-Scale Escherichia coli Kinetic Metabolic Model k-ecoli457 Satisfying Flux Data for Multiple Mutant Strains”. Nature Communications 7 (2016): 13806.
  14. Johnson KA and Goody RS. “The Original Michaelis Constant: Translation of the 1913 Michaelis-Menten Paper”. Biochemistry39 (2011): 8264-8269.
  15. Sonnad JR and Goudar CT. “Solution of the Haldane Equation for Substrate Inhibition Enzyme Kinetics Using the Decomposition Method”. Mathematical and Computer Modelling 5-6 (2004): 573-82.
  16. Kanehisa M., et al. “The KEGG databases at Genome Net”. Nucleic Acids Research 1 (2002): 42-46.
  17. Chen WW., et al. “Classic and Contemporary Approaches to Modeling Biochemical Reactions”. Genes and Development 17 (2010): 1861-1875.
  18. Yong B. “The Comparison of Fourth Order Runge-Kutta and Homotopy Analysis Method for Solving Three Basic Epidemic Models”. Journal of Physics: Conference Series 1317 (2019): 012020.
  19. Ling MH. “COPADS IV: Fixed Time-Step ODE Solvers for a System of Equations Implemented as a Set of Python Functions”. Advanced Computer Science 3 (2016): 5-11.
  20. 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”. Annals of Agricultural Sciences 2 (2018): 173-180.
  21. 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 Resources Research 5 (2015): 3070-3092.
  22. Willmott CJ. “Some Comments on the Evaluation of Model Performance”. Bulletin of the American Meteorological Society 11 (1982): 1309-1313.
  23. Waldmann P. “On the Use of the Pearson Correlation Coefficient for Model Evaluation in Genome-Wide Prediction”. Frontiers in Genetics 10 (2019): 899.
  24. Prelich G. “Gene Overexpression: Uses, Mechanisms, and Interpretation”. Genetics3 (2012): 841-854.
  25. Ko Y-S., et al. “Tools and Strategies of Systems Metabolic Engineering for the Development of Microbial Cell Factories for Chemical Production”. Chemical Society Reviews 14 (2020): 4615-4636.
×

Citation

Citation: Maurice HT Ling., et al. “UniKin1: A Universal, Non-Species-Specific Whole Cell Kinetic Model". Acta Scientific Microbiology 3.10 (2020): 04-08.




Metrics

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 November 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