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Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation

P Mendes1, D Kell

  • 1Institute of Biological Sciences, University of Wales Aberystwyth, Aberystwyth, Ceredigion SY23 3DD, UK. prm@aber.ac.uk

Bioinformatics (Oxford, England)
|February 3, 1999
PubMed
Summary

This study presents a simulation-optimization strategy for biochemical kinetic systems, enhancing metabolic engineering and parameter estimation. Diverse optimization methods are recommended for robust simulation software like Gepasi.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Systems Biology

Background:

  • Biochemical kinetic system simulations are crucial for model validation, 'what if' analyses, and exploring model behaviors.
  • These simulations are vital for metabolic engineering and solving the inverse problem of parameter estimation from experimental data.

Purpose of the Study:

  • To develop a generic approach combining numerical optimization with biochemical kinetic simulations.
  • To facilitate rational design of improved metabolic pathways and parameter estimation in metabolic pathways.

Main Methods:

  • Integration of diverse numerical optimization methods with biochemical kinetic simulations.
  • Implementation of a simulation-optimization strategy within the Gepasi biochemical kinetics simulator.

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Main Results:

  • Evaluation of various optimization methods, emphasizing their ability to find global optima.
  • Recommendation for incorporating a suite of diverse optimization methods in simulation software for broader applicability.
  • Demonstration of the simulation-optimization strategy's application through examples.

Conclusions:

  • A robust simulation-optimization strategy enhances the capabilities of biochemical kinetic simulators.
  • The Gepasi software (version 3.20) now incorporates this advanced methodology.
  • This approach supports advancements in metabolic engineering and systems biology research.