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Related Experiment Videos

Parameter estimation in biochemical systems models with alternating regression.

I-Chun Chou1, Harald Martens, Eberhard O Voit

  • 1The Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA. gtg392p@mail.gatech.edu

Theoretical Biology & Medical Modelling
|July 21, 2006
PubMed
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Alternating regression (AR) offers a fast, scalable method for estimating parameter values in biological systems. This approach simplifies complex problems into linear regressions, significantly improving computational analysis speed.

Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Parameter estimation is a major challenge in computational analysis of biological systems.
  • Existing methods for parameter estimation are often slow and not scalable.
  • Development of effective, fast, and scalable methods is crucial.

Purpose of the Study:

  • To introduce Alternating Regression (AR) as a novel method for parameter estimation in S-system models.
  • To demonstrate AR's effectiveness in identifying parameter values from time-series data.
  • To highlight AR's advantages over conventional methods in terms of speed and scalability.

Main Methods:

  • Alternating Regression (AR) applied to S-system models.
  • Decoupling systems of differential equations.

Related Experiment Videos

  • Iterative linear regression to solve nonlinear inverse problems.

    • Main Results:

    • AR provides a fast tool for parameter value identification from time-series data.
    • The method dissects nonlinear problems into iterative linear regression steps.
    • AR is significantly faster (3-5 orders of magnitude) than direct structure identification methods.

    Conclusions:

    • AR offers a novel strategy for parameter estimation and structure identification in S-systems.
    • AR is generally very fast, though convergence requires further study.
    • The method's speed makes it feasible to overcome convergence issues with appropriate settings.