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pyPESTO: a modular and scalable tool for parameter estimation for dynamic models.

Yannik Schälte1,2,3, Fabian Fröhlich4, Paul J Jost1

  • 1Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany.

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|November 23, 2023
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Summary
This summary is machine-generated.

Estimating parameters in complex biological models is challenging. The pyPESTO framework offers scalable tools for systematic parameter estimation and uncertainty quantification in these systems.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Biophysics

Background:

  • Mechanistic models are crucial for understanding biological processes.
  • Parameter estimation in large, complex biological systems presents significant computational challenges.
  • Existing methods often lack scalability and unified interfaces for diverse modeling approaches.

Purpose of the Study:

  • To introduce pyPESTO, a modular Python framework designed for systematic parameter estimation.
  • To provide scalable algorithms for optimization and uncertainty quantification in mechanistic models.
  • To offer a unified interface for integrating various simulation and inference tools.

Main Methods:

  • Development of a modular Python framework (pyPESTO) for parameter estimation.
  • Implementation of scalable optimization and uncertainty quantification algorithms.
  • Integration with popular simulation and inference methods for broad applicability.

Main Results:

  • pyPESTO facilitates systematic parameter estimation in complex biological models.
  • The framework supports scalable optimization and uncertainty quantification.
  • It provides a unified interface for diverse modeling and inference approaches, applicable beyond ordinary differential equations.

Conclusions:

  • pyPESTO addresses the challenges of parameter estimation in large-scale biological modeling.
  • The framework enhances the efficiency and accessibility of mechanistic model analysis.
  • Its modular design and broad applicability make it a valuable tool for systems and computational biology research.