Acta Scientific Computer Sciences

Review Article Volume 3 Issue 1

Kinetic Models with Default Enzyme Kinetics from Genome-scale Models

Nabil Amir-Hamzah, Zhi Jue Kuan and Maurice HT Ling*

School of Applied Science, Temasek Polytechnic, Singapore

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

Received: December 09, 2021; Published: December 29, 2021

Abstract

Many genome-scale models of metabolism [GSMs] have been constructed to study the effects of changing native gene expression on its metabolism. Kinetic models of metabolism [KMs] can be a useful tool to study the effects of transgenes and regulations on the time-course metabolic profile of the host. However, the availability of KMs is substantially lesser with smaller scope than GSMs. A possibility is to generate KMs from GSMs but such tool is not available. Here, we present a converter to convert substrate-product pairs in GSM rate laws to enzyme kinetic equations in KM using default enzyme kinetics. Our testing results suggests that simulatable KMs can be successfully generated from GSMs to generate time-course metabolic profiles.

Keywords: Genome-scale Metabolic Models; Kinetic Models; Automated Model Conversion

References

  1. 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.
  2. Eissing T. “A Computational Systems Biology Software Platform for Multiscale Modeling and Simulation: Integrating Whole-Body Physiology, Disease Biology, and Molecular Reaction Networks”. Frontiers in Physiology 2 (2011): 4.
  3. Ji Z., et al. “Mathematical and Computational Modeling in Complex Biological Systems”. BioMed Research International (2017): 1-16.
  4. Bhat NG and Balaji S. “Whole-Cell Modeling and Simulation: A Brief Survey”. New Gener Comput1 (2020): 259-281.
  5. Endler L., et al. “Designing and Encoding Models for Synthetic Biology”. Journal of the Royal Society Interface 6 (2009): S405-417.
  6. Wang H., et al. “Two Cellular Resource-Based Models Linking Growth and Parts Characteristics Aids the Study and Optimisation of Synthetic Gene Circuits”. Eng Biol 1.1 (2017): 30-39.
  7. Wong A and Ling MHT. “Characterization of Transcriptional Activities”. In: Encyclopedia of Bioinformatics and Computational Biology. Elsevier (2019): 830-41.
  8. Ling MH. “AdvanceSyn Toolkit: An Open Source Suite for Model Development and Analysis in Biological Engineering”. MOJ Proteomics Bioinforma 9.4 (2020): 83-86.
  9. Richelle A., et al. “Towards a Widespread Adoption of Metabolic Modeling Tools in Biopharmaceutical Industry: A Process Systems Biology Engineering Perspective”. npj Systems Biology and Applications1 (2020): 6.
  10. Srinivasan S., et al. “Constructing Kinetic Models of Metabolism at Genome-Scales: A Review”. Biotechnology Journal9 (2015): 1345-1359.
  11. Gu C., et al. “Current Status and Applications of Genome-Scale Metabolic Models”. Genome Biology1 (2019): 121.
  12. O’Brien EJ., et al. “Using Genome-scale Models to Predict Biological Capabilities”. Cell5 (2015): 971-987.
  13. Edwards JS and Palsson BO. “Systems Properties of the Haemophilus influenzae Rd Metabolic Genotype”. Journal of Biological Chemistry25 (1999): 17410-17416.
  14. Helmy M., et al. “Systems Biology Approaches Integrated with Artificial Intelligence for Optimized Metabolic Engineering”. Metabolic Engineering Communications 11 (2020): e00149.
  15. Du B., et al. “Evaluation of Rate Law Approximations in Bottom-Up Kinetic Models of Metabolism”. BMC Systems Biology1 (2016): 40.
  16. 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.
  17. 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.
  18. Brunk E., et al. “Recon3D Enables a Three-Dimensional View of Gene Variation in Human Metabolism”. Nature Biotechnology3 (2018): 272-281.
  19. Cardoso JGR., et al. “Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories”. ACS Synthetic Biology4 (2018): 1163-1166.
  20. McKinney W. “Data Structures for Statistical Computing in Python”. In: Proceedings of the 9th Python in Science Conference. Austin, Texas, USA (2010): 51-6.
  21. Smith LP., et al. “Antimony: A Modular Model Definition Language”. Bioinformatics18 (2009): 2452-2454.
  22. Bar-Even A., et al. “The Moderately Efficient Enzyme: Evolutionary and Physicochemical Trends Shaping Enzyme Parameters”. Biochemistry 21 (2011): 4402-4410.
  23. Liebermeister W and Klipp E. “Bringing Metabolic Networks to Life: Convenience Rate Law and Thermodynamic Constraints”. Theoretical Biology and Medical Modelling1 (2006): 41.
  24. Voit EO. “Biochemical Systems Theory: A Review”. Raffelsberger W, Pérez-Correa R, editors. 2013 (2013): 897658.
  25. Qian H and Bishop LM. “The Chemical Master Equation Approach to Nonequilibrium Steady-State of Open Biochemical Systems: Linear Single-Molecule Enzyme Kinetics and Nonlinear Biochemical Reaction Networks”. International Journal of Molecular Sciences9 (2010): 3472-500.
  26. Cakir T and Khatibipour MJ. “Metabolic Network Discovery by Top-Down and Bottom-Up Approaches and Paths for Reconciliation”. Front Bioeng Biotechnol 2 (2014): 62.
  27. Bouchoux C and Uhlmann F. “A Quantitative Model for Ordered Cdk Substrate Dephosphorylation during Mitotic Exit”. Cell4 (2011): 803-814.
  28. Yasemi M and Jolicoeur M. “Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches”. Processes2 (2021): 322.
  29. Kolomeisky AB. “Michaelis-Menten Relations for Complex Enzymatic Networks”. The Journal of Chemical Physics15 (2011): 155101.
  30. Hackett SR., et al. “Systems-Level Analysis of Mechanisms Regulating Yeast Metabolic Flux”. Science 6311 (2016): aaf2786.
  31. Zelezniak A., et al. “Contribution of Network Connectivity in Determining the Relationship between Gene Expression and Metabolite Concentration Changes. Hatzimanikatis V, editor”. PLOS Computational Biology4 (2014): e1003572.
  32. Murabito E., et al. “Monte-Carlo Modeling of the Central Carbon Metabolism of Lactococcus lactis: Insights into Metabolic Regulation”. PLoS ONE 9 (2014): e106453.
  33. Clement TJ., et al. “Unlocking Elementary Conversion Modes: ecmtool Unveils All Capabilities of Metabolic Networks”. Patterns1 (2021): 100177.
  34. Heinonen M., et al. “Bayesian Metabolic Flux Analysis Reveals Intracellular Flux Couplings”. Bioinformatics14 (2019): i548-557.
  35. Venayak N., et al. “MoVE Identifies Metabolic Valves to Switch Between Phenotypic States”. Nature Communication1 (2018): 5332.
  36. King ZA., et al. “BiGG Models: A Platform for Integrating, Standardizing and Sharing Genome-Scale Models”. Nucleic Acids ResearchD1 (2016): D515-522.
  37. Karlsen E., et al. “Automated Generation of Genome-Scale Metabolic Draft Reconstructions Based on KEGG”. BMC Bioinformatics1 (2018): 467.
  38. Kanehisa M., et al. “The KEGG Databases at GenomeNet”. Nucleic Acids Research1 (2002): 42-46.
  39. Kanehisa M., et al. “KEGG: Integrating Viruses and Cellular Organisms”. Nucleic Acids ResearchD1 (2019): D545-551.
  40. Machado D., et al. “Fast Automated Reconstruction of Genome-Scale Metabolic Models for Microbial Species and Communities”. Nucleic Acids Research15 (2018): 7542-7553.
  41. Shannon P., et al. “Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks”. Genome Research11 (2003): 2498-2504.
  42. Marinos G., et al. “Defining the Nutritional Input for Genome-Scale Metabolic Models: A Roadmap”. PLoS ONE8 (2020): e0236890.

 

Citation

Citation: Maurice HT Ling., et al. “Kinetic Models with Default Enzyme Kinetics from Genome-scale Models". Acta Scientific Computer Sciences 3.1 (2022): 59-63.

Copyright

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.




Metrics

Acceptance rate35%
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 July 10, 2022.
  • 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