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PESTO: Parameter EStimation TOolbox.

Paul Stapor1,2, Daniel Weindl1, Benjamin Ballnus1,2

  • 1Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.

Bioinformatics (Oxford, England)
|October 26, 2017
PubMed
Summary
This summary is machine-generated.

PESTO is a versatile MATLAB toolbox for parameter estimation, offering scalable optimization and uncertainty analysis for black-box objective functions. This customizable tool supports diverse parameter estimation challenges in scientific modeling.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Mathematical Modeling

Background:

  • Parameter estimation is crucial for building predictive models in systems biology.
  • Existing tools may lack flexibility or scalability for complex models.
  • A generic approach is needed to handle diverse objective functions.

Purpose of the Study:

  • To introduce PESTO, a new MATLAB toolbox for parameter estimation.
  • To provide a flexible and scalable solution for optimization and uncertainty analysis.
  • To enable users to apply parameter estimation to any problem with a deterministic objective function.

Main Methods:

  • PESTO treats the objective function as a black box.
  • It employs scalable algorithms for optimization.
  • Includes methods for uncertainty and identifiability analysis.

Main Results:

  • PESTO is a widely applicable and highly customizable toolbox.
  • It supports generic parameter estimation problems in MATLAB.
  • Offers robust optimization and uncertainty quantification.

Conclusions:

  • PESTO provides a powerful and flexible platform for parameter estimation in MATLAB.
  • Its black-box approach enhances applicability across various scientific domains.
  • The toolbox facilitates advanced analysis for model development and validation.