A simple model of a growing tumour

Area of Science:

• Computational Biology
• Mathematical Oncology
• Cancer Research

Background:

• Tumor growth dynamics and response to treatment are complex.
• Predictive models are crucial for optimizing cancer therapies.
• Existing models may rely on unobservable parameters.

Purpose of the Study:

• To present CellCycler, a novel computational model for simulating tumor growth.
• To predict the impact of therapeutic interventions on tumor progression.
• To utilize observable parameters for enhanced model applicability.

Main Methods:

• Ordinary differential equations (ODEs) to model cell cycle progression.
• Focus on observable cell states (phase, apoptosis, damage) rather than internal cell dynamics.
• Development of a freely available web application for accessibility.

Main Results:

• The model naturally accounts for heterogeneous cell populations with varying doubling times.
• CellCycler accurately captures the dynamics of growing tumors under treatment.
• Demonstrated utility through three distinct case studies.

Conclusions:

• A simplified modeling approach using observable parameters can effectively simulate tumor behavior.
• CellCycler provides a valuable tool for xenograft studies and clinical treatment prediction.
• The model's accessibility facilitates wider adoption in cancer research.