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Computer simulation of clonal growth cancer models. I. Parameter estimation using an iterative absolute bisection

D A Kramer1, R B Conolly

  • 1Chemical Industry Institute of Toxicology, Research Triangle Park, North Carolina 27709, USA.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|February 1, 1997
PubMed
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We developed a new parameter estimation routine for numerical clonal growth simulation (CGS) models. This method improves the accuracy and reproducibility of cancer risk assessment models for chemical carcinogens.

Area of Science:

  • Quantitative toxicology
  • Computational biology
  • Carcinogenesis modeling

Background:

  • Quantitative models are crucial for evaluating chemical carcinogen exposure and cancer risk.
  • Mathematical tractability often limits the biological complexity of analytical models.
  • Numerical simulation offers greater biological realism but lacks robust parameter estimation methods.

Purpose of the Study:

  • To develop a formal parameter estimation routine for numerical clonal growth simulation (CGS) models.
  • To apply this routine to a CGS model of preneoplastic lesion growth.
  • To enhance the utility of CGS models in hypothesis evaluation and risk assessment.

Main Methods:

  • Developed an iterative bisection algorithm for parameter estimation.

Related Experiment Videos

  • Applied the algorithm to time-course data on initiated cells and clones.
  • Investigated the influence of data points, stochastic repetitions, and other variables on parameter estimates.

    • Main Results:

    • The algorithm successfully estimated parameter values, achieving a best fit to observed data.
    • Parameter estimates were robust across different starting values.
    • The method ensures uniformity and reproducibility of parameter estimates for CGS models.

    Conclusions:

    • The developed parameter estimation routine effectively supports the application of CGS models.
    • This advancement facilitates more accurate hypothesis evaluation and risk assessment for chemical carcinogens.
    • The routine enhances the reliability and reproducibility of quantitative biological modeling.