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Related Experiment Videos

Estimating optimal profiles of genetic alterations using constraint-based models.

Kapil G Gadkar1, Francis J Doyle Iii, Jeremy S Edwards

  • 1Department of Chemical Engineering, University of California, Santa Barbara, CA 92121, USA.

Biotechnology and Bioengineering
|December 14, 2004
PubMed
Summary
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Optimizing metabolic engineering by timing genetic alterations improves production efficiency. This study shows that delaying genetic modifications enhances product yield, such as glycerol and ethanol, in Escherichia coli.

Area of Science:

  • Metabolic Engineering
  • Biotechnology
  • Biochemical Engineering

Background:

  • Metabolic engineering utilizes recombinant DNA technology to enhance cellular properties for improved product yield and profitability.
  • Productivity is a critical factor in metabolic engineering, particularly for bulk chemical production like 1,3-propanediol.
  • Inducing genetic alterations at the onset of production can be suboptimal for overall productivity.

Purpose of the Study:

  • To develop a bi-level optimization scheme for determining optimal temporal flux profiles in manipulated metabolic reactions.
  • To investigate the impact of timing genetic alterations on production efficiency in Escherichia coli.
  • To analyze the influence of process parameters like mass transfer and batch time on product concentration.

Main Methods:

Related Experiment Videos

  • Formulation of a bi-level optimization scheme to identify optimal temporal flux profiles.
  • Case study 1: Optimization of glycerol kinase flux for enhanced glycerol production in aerobic batch cultivation of Escherichia coli.
  • Case study 2: Optimization of acetate pathway flux for increased ethanol production in anaerobic batch fermentation of an Escherichia coli ldh(-) strain.

Main Results:

  • An optimal flux profile for glycerol kinase resulted in a 30% increase in glycerol concentration compared to constitutive flux.
  • Optimizing the acetate pathway flux in Escherichia coli ldh(-) strain led to higher ethanol concentrations (11.92 mmol L(-1)) than constitutive (6.22 mmol L(-1)) or no acetate flux (8.36 mmol L(-1)).
  • The study examined the effects of mass transfer coefficients, growth inhibition, and batch time variations on product yield.

Conclusions:

  • Timing of genetic alterations is crucial for optimizing productivity in metabolic engineering.
  • The bi-level optimization approach effectively determines optimal temporal flux profiles for enhanced product formation.
  • This strategy offers significant improvements in bulk chemical production efficiency using microbial systems.