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Nonlinear RANSAC Optimization for Parameter Estimation with Applications to Phagocyte Transmigration.

Mingon Kang1, Jean Gao, Liping Tang

  • 1Department of Computer Science, University of Texas at Arlington, Arlington, Texas, USA 76019.

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications
|December 11, 2012
PubMed
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We developed nonlinear RANSAC, a data-driven method for estimating parameters in complex biological models. This approach enhances computational modeling accuracy and efficiency for systems like phagocyte transmigration.

Area of Science:

  • Computational Biology
  • Systems Biology
  • Biomedical Engineering

Background:

  • Accurate parameter estimation is crucial for reliable biological system modeling and simulation.
  • Complex biological systems often have numerous unknown parameters, posing challenges for traditional modeling approaches.
  • Existing methods like RANdom SAmple Consensus (RANSAC) are effective for linear models but limited for nonlinear systems.

Purpose of the Study:

  • To develop a novel data-driven global optimization method for parameter estimation in nonlinear system models.
  • To extend the capabilities of the RANSAC algorithm to handle nonlinear biological systems.
  • To improve the accuracy and computational efficiency of parameter estimation in complex biological models.

Main Methods:

  • Development of nonlinear RANSAC, a data-driven global optimization technique.

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  • Application of nonlinear RANSAC for parameter estimation in nonlinear system models.
  • Validation of the method using a model of phagocyte transmigration and signaling pathways described by ordinary differential equations.

    • Main Results:

    • The nonlinear RANSAC method demonstrated outstanding performance in parameter estimation for nonlinear system models.
    • Successfully applied the method to estimate parameters for phagocyte transmigration, a process relevant to fibrosis and biomedical device implantation.
    • Validated the generalizability of the approach on signaling pathways modeled using ordinary differential equations.

    Conclusions:

    • Nonlinear RANSAC provides an effective solution for parameter estimation challenges in complex, nonlinear biological systems.
    • The developed method enhances the reliability and feasibility of computational modeling for biological processes.
    • This data-driven approach offers a significant advancement for systems biology and biomedical applications.