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Characterizing and ranking computed metabolic engineering strategies.

Philipp Schneider1, Steffen Klamt1

  • 1Max Planck Institute for Dynamics of Complex Technical Systems, Analysis and Redesign of Biological Networks, Magdeburg, Germany.

Bioinformatics (Oxford, England)
|January 17, 2019
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Summary

This study introduces a new ranking system for metabolic engineering strategies, evaluating factors like robustness and thermodynamic properties to identify optimal interventions for growth-coupled product synthesis. The method aids in selecting the best strategies from numerous computational outputs for improved bioprocess design.

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Area of Science:

  • Metabolic Engineering
  • Computational Biology
  • Systems Biology

Background:

  • Computer-aided design is crucial for metabolic engineering, generating numerous intervention strategies.
  • Selecting the optimal strategy from thousands of options for growth-coupled product synthesis is challenging.

Purpose of the Study:

  • To develop and present a comprehensive method for evaluating and ranking metabolic engineering strategies.
  • To introduce novel criteria for selecting the most suitable intervention strategies for growth-coupled product synthesis.

Main Methods:

  • Developed a ranking procedure incorporating ten criteria, including robustness, thermodynamic properties, and overlap with alternative solutions.
  • Introduced equivalence classes for grouping strategies with identical solution spaces.
  • Applied the ranking procedure to knockout-based strategies in a genome-scale model of E. coli for l-methionine and 1,4-butanediol synthesis.

Main Results:

  • Presented new criteria for ranking metabolic engineering strategies beyond simple metrics like growth rate and product yield.
  • Demonstrated the ranking procedure's applicability using E. coli models for specific product syntheses.
  • Identified key factors for selecting robust and efficient metabolic interventions.

Conclusions:

  • The developed ranking procedure provides a meaningful way to evaluate and select optimal metabolic engineering strategies.
  • This approach enhances the integrated design of metabolic pathways for improved bioproduction.
  • The methodology facilitates the selection of feasible and robust intervention strategies for industrial applications.