Stocco

This is Stocco, final project for the exam Stochastic Modelling and Simulation from MSc in Data Science and Scientific Computing at University of Trieste/SISSA. I took the base model from this paper, by Tianqi Zhu, Yucheng Hu, Zhi-Ming Ma, De-Xing Zhang, Tiejun Li and Ziheng Yang. My contribute consisted in adding competition and a spatial structure.

For an overview of the whole project see this presentation.

Note: there is a small error in the code for spatial model, leading to an increasing mutation rate for increasing resolution; because of that, please consider the results on waiting time and efficiency of spatiotemporal algorithms when varying resolution to be wrong.

Note: some of the data in CSV files are not the same used in the slide presentation; the reason is that I ran some additional tests after I had already written the presentation. However, the results are coherent.

What you will find in this repository

• This README file
• Stocco-Presentation.pdf: slide presentation of the project
• src/: directory containing all python codes used to run the simulations; contains:
  • stocco_lib.py: library containing all functions used for the simulations
  • fixed_population.py: script used for fixed-population non-spatial simulations
  • dynamic_population.py: script used for dynamic-population non-spatial simulations
  • spatial.py: script used for dynamic-population spatial simulations (naive, actually never used in the project)
  • spatial_ngb.py: script used for dynamic-population spatial simulations (less naive, used in the project)
• algo_comparison/: directory containing results obtained with all algorithms to compare waiting time and efficiency
• fitness_comparison/: directory containing results obtained with fixed population and different fitness landscapes to compare the behaviour of the system
• dynamic_population/: directory containing results obtained with dynamic population to check whether the $\tilde{N}$ parameter is actually able to control the population size
• spatial_model/: directory containing results for final average genotype distribution in spatial model
• fitness_graphs/: directory containing scripts for graphs (not interesting)

References

• Tianqi Zhu, Yucheng Hu, Zhi-Ming Ma, De-Xing Zhang, Tiejun Li, Ziheng Yang, Efficient simulation under a population genetics model of carcinogenesis, Bioinformatics, Volume 27, Issue 6, March 2011, Pages 837–843, https://doi.org/10.1093/bioinformatics/btr025.