A COMPUTATIONAL COMPARISON OF GENOME-SCALE METABOLIC MODELS HIGHLIGHTING THE NEED FOR THE ADOPTION OF UNIVERSAL CONVENTIONS AND STANDARDS

Open Access
Author:
Spagnol, Stephen Thomas
Area of Honors:
Chemical Engineering
Degree:
Bachelor of Science
Document Type:
Thesis
Thesis Supervisors:
  • Costas D Maranas, Thesis Supervisor
  • Themis Matsoukas, Honors Advisor
  • Andrew Zydney, Faculty Reader
Keywords:
  • genome-scale metabolic model
  • metabolic engineering
  • genome
  • genome-scale
  • metabolic model
  • metabolic reconstruction
  • strain optimization
  • gap filling
Abstract:
This study aims to compare some of the existing genome-scale metabolic models and display the inconsistencies between them in order to highlight the need for the adoption of universal conventions and standards of completeness and coverage to enhance the versatility and applicability of these models to metabolic engineering applications. These applications include uses for industry (i.e. biofuels, commodity chemicals, biochemicals, etc.), medicine (i.e. drug production, vaccines, antibiotics, drug target identification, etc.), bioremediation, and the growing number of problems to which metabolic engineering is being applied. However, the potential usage of genome-scale metabolic models for these applications is limited by the lack of congruency between models, which hinders attempts at strain optimization, gap filling, production of new metabolic reconstructions, and insertion of foreign pathways into a new host. These discrepancies primarily include incomplete reaction data, such as elementally and charge unbalanced reactions, and a lack of universal metabolite specificity and naming conventions. In this study, a Metabolite Rosetta Stone was created to allow for the translation of the different metabolite abbreviations from each model to a common form for comparison of their metabolic networks. In comparing 34 genome-scale metabolic models and the Escherichia coli core model, only three reactions were found to be common among all 35 reaction networks, which contradicts the fact that many of these organisms share several conserved metabolic pathways and, thus, reactions. However, in a comparison of seven models of more uniform conventions, a better agreement was observed with 40 reactions found common to all of them. This result conveys the need for the adoption of uniform conventions and standards and a reconciliation of these previous models in order to compare existing models, develop new ones from them, and incorporate existing pathways from one model into another. In addition, this study will detail what needs to be done to rectify the current problems and also provide some potential solutions to enhance the capabilities and effectiveness of genome-scale metabolic models.