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.
- This README file
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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)
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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)
- 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.