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Curtain Flow Column: Optimization of Efficiency and Sensitivity
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Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis.

Paul Stapor1,2, Fabian Fröhlich1,2, Jan Hasenauer1,2

  • 1Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany.

Bioinformatics (Oxford, England)
|June 29, 2018
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Summary
This summary is machine-generated.

We developed efficient methods for parameter estimation in biological ordinary differential equation (ODE) models. Our approach improves computational speed and robustness for large-scale models, making complex biological system analysis more accessible.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Biophysics

Background:

  • Parameter estimation for ordinary differential equation (ODE) models is crucial for understanding biological processes.
  • Established methods for Hessian computation in ODE models face computational complexity challenges, limiting scalability.

Purpose of the Study:

  • To introduce efficient methods for computing Hessians and profile likelihoods for ODE models.
  • To improve the computational efficiency and robustness of parameter estimation for medium and large-scale biological models.

Main Methods:

  • Second order adjoint sensitivity analysis for Hessian computation.
  • Hybrid optimization-integration-based approach for profile likelihood computation.
  • Implementation within the Advanced MATLAB Interface to CVODES and IDAS (AMICI) and the Parameter Estimation Toolbox (PESTO).

Main Results:

  • Second order adjoint sensitivity analysis offers linear scaling with parameters and state variables.
  • The hybrid profile likelihood method demonstrated over 2-fold speed improvement compared to competitors.
  • Improved computational efficiency and robustness in parameter estimation for biological ODE models.

Conclusions:

  • The proposed methods enhance parameter estimation for medium and large-scale ODE models.
  • Adjoint sensitivity analysis and hybrid profile likelihood computation provide significant computational advantages.
  • Freely available software tools (AMICI and PESTO) facilitate the application of these methods.