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Development of a sampling-based global sensitivity analysis workflow for multiscale computational cancer models.

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Summary

Researchers developed a new workflow for global sensitivity analysis (GSA) in computational cancer models. This method identified extracellular signal-regulated kinase as key to lung tumor growth and expansion.

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Area of Science:

  • Computational biology
  • Cancer research
  • Systems biology

Background:

  • Global sensitivity analysis (GSA) for multiscale cancer models faces computational intensity and a lack of standardized methods.
  • Existing GSA approaches may not be optimal for complex, multiscale 'in silico' models.

Purpose of the Study:

  • To propose and exemplify a novel sampling-based GSA workflow for multiscale cancer models.
  • To address the computational and methodological challenges in analyzing complex cancer simulations.

Main Methods:

  • Developed a three-phase GSA workflow: pre-analysis, analysis, and post-analysis.
  • Integrated Monte Carlo and resampling methods with repeated analysis of variance.
  • Applied the workflow to a two-dimensional multiscale lung cancer model.

Main Results:

  • The workflow generated a summarized ranking of input parameters by averaging rankings from multiple GSA methods.
  • Identified extracellular signal-regulated kinase (ERK) as the most impactful parameter.
  • ERK significantly influences tumor volume and expansion rate in the investigated lung cancer model.

Conclusions:

  • The proposed GSA workflow effectively analyzes multiscale cancer models.
  • Extracellular signal-regulated kinase plays a critical role in lung cancer progression within this model.
  • This approach provides a robust method for parameter importance assessment in complex biological simulations.