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Parameter estimation for cell cycle ordinary differential equation (ODE) models using a grid approach.

Roberta Alfieri1, Ettore Mosca, Ivan Merelli

  • 1Institute for Biomedical Technology, National Research Council, Milan, Italy. roberta.alfieri@itb.cnr.it

Studies in Health Technology and Informatics
|May 4, 2007
PubMed
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This study presents an automated system for simulating the cell cycle, aiding cancer research and drug discovery. It efficiently identifies the best mathematical model for experimental data using a grid approach, accelerating biological process analysis.

Area of Science:

  • Computational Biology
  • Systems Biology
  • Mathematical Modeling

Background:

  • The cell cycle is crucial for cancer research and drug discovery.
  • Mathematical simulations reveal the complexity of cell cycle processes.
  • Accurate modeling is essential for understanding normal and deregulated cell cycles.

Purpose of the Study:

  • To develop an automated approach for mathematical simulation of the cell cycle.
  • To identify the best-fitting mathematical model for experimental cell cycle data.
  • To provide an interactive system for visualizing and simulating cell cycle models.

Main Methods:

  • Implemented a system to retrieve mathematical information from a Cell Cycle Database.
  • Utilized a grid approach for distributed computation of Ordinary Differential Equation (ODE) systems.

Related Experiment Videos

  • Employed parameter estimation techniques, including evolutionary algorithms, for model selection.

    • Main Results:

    • The system automates the simulation and parameter estimation for cell cycle models.
    • A grid infrastructure significantly reduces the computation time for identifying the best model.
    • The approach facilitates the comparison of models under various conditions.

    Conclusions:

    • The developed automated system enhances the mathematical simulation of cell cycle processes.
    • This computational approach accelerates the identification of accurate cell cycle models.
    • The tool supports research in cancer and drug discovery by enabling efficient model comparison.