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Efficient gradient-based parameter estimation for dynamic models using qualitative data.

Leonard Schmiester1,2, Daniel Weindl1, Jan Hasenauer1,2,3

  • 1Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg 85764, Germany.

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Summary
This summary is machine-generated.

Estimating parameters for dynamical models using qualitative data is now more efficient. Our new framework uses gradient-based optimization, improving speed and accuracy for ordinary differential equation models.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Mathematical Modeling

Background:

  • Parameter estimation for dynamical models is crucial for understanding biological systems.
  • Existing methods for qualitative data are computationally inefficient and have convergence issues.

Purpose of the Study:

  • To develop an efficient and reliable framework for parameter estimation from qualitative data.
  • To improve the speed and accuracy of parameterization for ordinary differential equation models.

Main Methods:

  • Derivation of a semi-analytical gradient calculation algorithm for qualitative data.
  • Integration of gradient information into gradient-based optimization algorithms.
  • Application of the framework to various biological models.

Main Results:

  • Achieved over a tenfold speedup compared to gradient-free optimization methods.
  • Demonstrated improved objective function values and fit quality in application examples.
  • Successfully parameterized ordinary differential equation models using qualitative data.

Conclusions:

  • The proposed framework significantly enhances parameter estimation from qualitative data.
  • Gradient-based optimization offers substantial improvements in efficiency and reliability.
  • The open-source pyPESTO toolbox facilitates the application of this method.