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Optimization of biotechnological systems through geometric programming.

Alberto Marin-Sanguino1, Eberhard O Voit, Carlos Gonzalez-Alcon

  • 1Grupo de Tecnologia Bioquímica, Departamento de Bioquimica y Biologia Molecular, Facultad de Biologia, Universidad de La Laguna, 38206 La Laguna, Tenerife, Islas Canarias, Spain. amarin@ull.es

Theoretical Biology & Medical Modelling
|September 28, 2007
PubMed
Summary
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A new geometric programming method efficiently optimizes Generalized Mass Action (GMA) systems for metabolic engineering yield. This approach matches or surpasses prior methods in identifying optima and improving efficiency.

Area of Science:

  • Metabolic Engineering
  • Systems Biology
  • Biochemical Engineering

Background:

  • Past yield optimization used stoichiometric or structured nonlinear models (e.g., S-systems).
  • These models convert optimization to linear programs for efficient solutions.
  • An Indirect Optimization Method (IOM) handled non-standard models by converting them to S-systems.

Purpose of the Study:

  • Introduce and evaluate a novel method for model-based yield optimization.
  • Apply geometric programming techniques to Generalized Mass Action (GMA) systems.
  • Demonstrate the efficacy of this combined approach on real biological systems.

Main Methods:

  • Utilized geometric programming techniques for optimizing Generalized Mass Action (GMA) systems.
  • Reviewed GMA system basics and geometric programming principles.

Related Experiment Videos

  • Applied the combined method to a didactic problem and two biological models.

    • Main Results:

    • The proposed geometric programming method for GMA systems proved highly efficient.
    • Performance was evaluated against established methods using models of S. cerevisiae fermentation and E. coli tryptophan operon.
    • The geometric programming approach demonstrated equal or superior performance in identifying optima and efficiency.

    Conclusions:

    • Generalized Mass Action (GMA) systems are significant as they encompass stoichiometric, mass action, and S-systems.
    • Algebraic transformations can convert most dynamical models into the GMA form.
    • Efficient optimization of GMA systems offers broad applicability in metabolic engineering and related fields.