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REDEMPTION: reduced dimension ensemble modeling and parameter estimation.

Yang Liu1, Erica Manesso1, Rudiyanto Gunawan1

  • 1Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093, Zurich, Switzerland and Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.

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This summary is machine-generated.

This study introduces REDEMPTION, a toolbox for parameter estimation and ensemble modeling of ordinary differential equations (ODEs) using time-series data. It offers efficient solutions for complex biological models and aids in identifying optimal parameter sets.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Mathematical Modeling

Background:

  • Parameter estimation and ensemble modeling are crucial for understanding complex biological systems.
  • Ordinary differential equations (ODEs) are widely used to model dynamic biological processes.
  • Challenges arise in parameter estimation when models have more reactions than measured species.

Purpose of the Study:

  • To present REDEMPTION, a novel toolbox for parameter estimation and ensemble modeling of ODEs.
  • To provide a solution for parameter estimation in complex biological models using time-series data.
  • To facilitate the identification of parameter sets that yield satisfactory model fits.

Main Methods:

  • REDEMPTION formulates parameter estimation as a nested optimization problem for complex models.
  • It employs an incremental parameter estimation strategy.
  • The toolbox includes functionality for identifying an ensemble of parameter combinations with good data fit.

Main Results:

  • REDEMPTION enables parameter estimation and ensemble modeling for ODEs with time-series data.
  • It addresses challenges in biological modeling where reactions exceed measured species.
  • The toolbox identifies parameter ensembles providing satisfactory goodness-of-fit.

Conclusions:

  • REDEMPTION offers an efficient and accessible solution for parameter estimation and ensemble modeling in systems biology.
  • Its implementation in MATLAB, with UI and scripting options, enhances usability.
  • Numerical parallelization is available for computational speed-up.