Optimizing Dosage-Specific Treatments in a Multi-Scale Model of a Tumor Growth

Area of Science:

• Computational Biology
• Cancer Research
• Mathematical Modeling

Background:

• Cell resistance to cancer treatment arises from complex, multi-scale processes.
• Molecular mechanisms and population dynamics like competition and variability are key.
• Multi-scale models integrate diverse biological scales (molecular, cellular, intercellular).

Purpose of the Study:

• To explore effective treatment strategies using TNF pulses via a hybrid multi-scale model.
• To deeply investigate the parameter space for optimizing treatment efficacy.
• To develop an HPC-optimized workflow for model exploration using EMEWS.

Main Methods:

• Utilized an extended hybrid multi-scale model of 3T3 fibroblast spheroids.
• Employed an HPC-optimized model exploration workflow based on EMEWS.
• Optimized TNF pulse supply strategies in 2D monolayers and 3D spheroids, considering spatial distribution and population heterogeneity.

Main Results:

• The model exploration workflow successfully identified effective treatments across various conditions.
• Spatial distribution of cells significantly impacts treatment parameter values.
• Effective treatments demonstrated robustness when considering heterogeneous cell populations.

Conclusions:

• Cells' spatial geometry and population variability are critical factors for optimizing cancer treatment strategies.
• Considering these factors leads to more robust and effective treatment parameter sets.
• The developed workflow efficiently explores parameter spaces for multi-scale biological models.