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Related Experiment Videos

Parameter estimation in biochemical pathways: a comparison of global optimization methods.

Carmen G Moles1, Pedro Mendes, Julio R Banga

  • 1Process Engineering Group, Instituto de Investigaciones Marinas (CSIC), 36208 Vigo, Spain.

Genome Research
|October 16, 2003
PubMed
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Estimating parameters in complex biochemical pathways is challenging. Evolution strategies (ES), a type of stochastic global optimization, successfully solved a benchmark problem where local methods failed.

Area of Science:

  • Biochemistry and Systems Biology
  • Computational Chemistry
  • Optimization Theory

Background:

  • Parameter estimation for nonlinear dynamic biochemical pathways is an inverse problem.
  • These problems are often ill-conditioned and multimodal, challenging traditional optimization methods.
  • Nonlinear programming (NLP) with differential-algebraic constraints is frequently employed.

Purpose of the Study:

  • To explore state-of-the-art deterministic and stochastic global optimization methods for biochemical pathway parameter estimation.
  • To identify robust algorithms capable of handling ill-conditioned and multimodal optimization landscapes.
  • To benchmark optimization strategies on a complex nonlinear biochemical dynamic model.

Main Methods:

  • Formulation of parameter estimation as a nonlinear programming (NLP) problem.

Related Experiment Videos

  • Evaluation of various deterministic and stochastic global optimization techniques.
  • Application of a benchmark case study involving the estimation of 36 parameters in a nonlinear biochemical dynamic model.

    • Main Results:

    • Traditional gradient-based local optimization methods failed to find satisfactory solutions.
    • Only evolution strategies (ES), a specific stochastic algorithm, successfully solved the benchmark parameter estimation problem.
    • Stochastic methods, particularly ES, demonstrated robustness and provided a lower bound for the cost function.

    Conclusions:

    • Evolution strategies (ES) are highly effective for parameter estimation in nonlinear dynamic biochemical pathways.
    • Stochastic global optimization methods are superior to local methods for complex, ill-conditioned biochemical models.
    • ES offers a robust and reliable approach for tackling challenging inverse problems in systems biology.