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AMICI: high-performance sensitivity analysis for large ordinary differential equation models.

Fabian Fröhlich1, Daniel Weindl2, Yannik Schälte2,3

  • 1Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.

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|April 6, 2021
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Summary
This summary is machine-generated.

Ordinary differential equation models are crucial for understanding biological processes but can be computationally expensive. AMICI (Adaptive Multiscale Intelligent Computational Interface) is a new toolbox that offers efficient simulation and sensitivity analysis for large models, enabling faster parameter estimation and uncertainty quantification.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Biophysics

Background:

  • Ordinary differential equation (ODE) models are widely used to understand complex biological systems, including cellular signal transduction pathways.
  • Simulating and calibrating large-scale ODE models presents significant computational challenges, limiting their practical application.
  • Efficient computational tools are needed to overcome these limitations in systems biology research.

Purpose of the Study:

  • To introduce AMICI, a novel modular toolbox designed to enhance the efficiency of ODE model simulation and analysis.
  • To provide a software solution for scalable, gradient-based parameter estimation and uncertainty quantification in biological modeling.
  • To facilitate the development and application of large, comprehensive biological models.

Main Methods:

  • AMICI is implemented in C++ with Python and MATLAB interfaces, offering flexibility for different user preferences.
  • The toolbox provides efficient routines for model simulation and sensitivity analysis, crucial for parameter estimation.
  • It is specifically tailored for gradient-based optimization methods, enabling scalable computational performance.

Main Results:

  • AMICI significantly reduces the computational cost associated with simulating and analyzing large ODE models.
  • The toolbox supports efficient gradient-based parameter estimation, improving the calibration of complex biological models.
  • AMICI facilitates robust uncertainty quantification, providing reliable insights into model parameters.

Conclusions:

  • AMICI is a powerful and efficient toolbox for computational biology, addressing the limitations of traditional ODE modeling.
  • Its modular design and efficient routines enable scalable parameter estimation and uncertainty quantification.
  • The toolbox will accelerate research in systems biology and the understanding of cellular processes.