Stem Cell Aging Simulation

This project implements a stochastic simulation of stem cell aging based on telomere dynamics across multiple cell divisions.

The model compares normal somatic cells with telomerase-active (cancer-like) cells using repeated Monte Carlo simulations.

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Scientific Background

Telomeres shorten during cell division due to the end-replication problem. When telomeres reach a critical length (Hayflick limit), cells enter senescence or apoptosis.

Some cells (e.g. cancer cells) activate telomerase, partially or fully compensating telomere loss and enabling replicative immortality.

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Model Description

• Random telomere loss per division
• Multiple independent simulation runs
• Mean trajectory and interquartile range (IQR)
• Comparison between:
  • Normal stem cells
  • Telomerase-active cells

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Output

The simulation produces:

• Mean telomere length trajectories
• Variability bands (IQR)
• Example single-cell trajectories
• Estimated divisions to senescence

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Technologies

• Python 3.9+
• NumPy
• Matplotlib

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Usage

pip install -r requirements.txt
python stem_cell_aging_simulation.py

⚠️ Limitations

This is a simplified stochastic model and does not capture
DNA damage response, cell cycle checkpoints, or tissue-level effects.

Author

Roman Lupashin