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Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
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Using control bias to identify initial targets for bioproduction improvement.

Michael Binns1, Pedro de Atauri2, Marta Cascante2

  • 1Department of Chemical and Biochemical Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea.

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Summary

This study introduces a novel method for analyzing bioprocess metabolic networks. It identifies key reaction steps influencing chemical production, aiding in optimizing bioprocesses for enhanced yields.

Keywords:
A. SuccinogenesBioproductionControl biasControl coefficientsElasticitiesMetabolic control analysisMetabolic networkSuccinic acid

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Area of Science:

  • Biotechnology
  • Metabolic Engineering
  • Systems Biology

Background:

  • Sensitivity analysis of bioprocess metabolic networks predicts system parameters affecting production.
  • Uncertainties in kinetic expressions and flux distributions limit prediction accuracy.

Purpose of the Study:

  • To propose a preliminary method for calculating control bias in metabolic networks using minimal information.
  • To identify reaction steps with significant positive, negative, or uncertain control over production.

Main Methods:

  • Utilizes sampling of elasticities and metabolic fluxes.
  • Requires minimal information: reaction stoichiometry, external fluxes, and potentially equilibrium steps.
  • Calculates control bias to assess the influence of reaction steps.

Main Results:

  • The control bias successfully identifies reaction steps with significant influence on bioprocess outcomes.
  • Applied to succinic acid bioproduction using Actinobacillus succinogenes.
  • Revealed key reaction steps positively and negatively impacting biosuccinic acid production.

Conclusions:

  • The proposed method offers initial guidance for identifying targets to enhance valuable chemical production.
  • It aids in directing further detailed investigations in metabolic engineering.
  • Successfully applied to a case study of biosuccinic acid production.