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PhysiCOOL: A generalized framework for model Calibration and Optimization Of modeLing projects.

Inês G Gonçalves1, David A Hormuth2, Sandhya Prabhakaran3

  • 1Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering Research (I3A), University of Zaragoza, Spain.

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

PhysiCOOL is a new Python library that simplifies parameter exploration and calibration for complex biological simulations. This tool enhances the biological relevance of models without needing high-performance computing access.

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Area of Science:

  • Computational biology
  • Mathematical biology
  • Systems biology

Background:

  • In silico models require extensive parameter validation.
  • Parameter space exploration is crucial for model characterization and validation.
  • Experimental data can constrain parameters to improve biological relevance.

Purpose of the Study:

  • Introduce PhysiCOOL, an open-source Python library.
  • Provide standardized calibration and optimization routines for PhysiCell models.
  • Enable parameter space exploration without high-performance computing.

Main Methods:

  • Developed an open-source Python library, PhysiCOOL.
  • Integrated PhysiCOOL with the PhysiCell platform.
  • Created standardized routines for parameter exploration and model calibration.

Main Results:

  • PhysiCOOL facilitates parameter space exploration for PhysiCell models.
  • The library enables model calibration using experimental data.
  • PhysiCOOL offers a generalized approach accessible without HPC.

Conclusions:

  • PhysiCOOL addresses the limitations of PhysiCell regarding parameter exploration and calibration.
  • The library enhances the usability and accessibility of computational modeling in mathematical biology.
  • PhysiCOOL promotes more robust and biologically relevant in silico models.