Examples and Case Studies

A case study comprises of one or more examples.

• Example 01 (file: 01_basic_functions_one_cell_deployment.py): Composes of a basic simulation, having most functions set to be passed except for the mutation_scheme, population_report and database_report. Features simple simulation parameters as an experimental simulation to try out the early functionality of DOSE.

• Example 02 (file: 02_basic_functions_even_deployment.py): Introduces DOSE's ability to simulate multiple populations together and a different kind of deployment scheme that allows organisms to be evenly deployed across the different eco-cells.

• Example 03 (file: 03_no_migration_isolated_mating.py): Examines the effects of having no migration on genetic distance from an initially identical population. Features the implementation of a user-defined mating scheme.

• Example 04 (file: 04_adjacent_migration_isolated_mating.py): Examines the effects of having adjacent cell migration on genetic distance from an initially identical population. Features the implementation of the organism_movement function that facilitates a short movement scheme of the organisms across the eco-cells.

• Example 05 (file: 05_long_migration_isolated_mating.py): Examines the effects of having long distance migration across one or more eco-cells on genetic distance from an initially identical population. Features the implementation of the organism_location function that facilitates a long movement scheme of the organisms across the eco-cells.

• Example 06 (file: 06_revive_simulation_01.py): Features DOSE's capability to revive a simulation from a buried population (.gap file) and a buried world (.eco file). In this example, the flexibility to specify how long the revival would generate and the option to change the previous simulation's parameters was exemplified.

• Example 07 (file: 07_logging_database_extraction.py): Introduces the different database extraction functions that are pre-defined to help the user extract data from a database.

• Example 08 (file: 08_revive_simulation_03.py): Features DOSE's capability to revive a simulation from a saved database file (*.db file). In this example, the revival of the database's last saved generation was extended for 200 more generations.

• Case study 01 using Example 09 (files: 09_adjacent_migration_isolated_mating.py, 09_long_migration_isolated_mating.py, and 09_no_migration_isolated_mating.py): Incorporates the functions introduced in the previous examples to examine the functionality of DOSE, specifically the effects on genetic distance, with having a specified background_mutation_rate across three different kinds of migration schemes. Reference: Castillo, CFG, Ling, MHT. 2014. Digital Organism Simulation Environment (DOSE): A Library for Ecologically-Based In Silico Experimental Evolution. Advances in Computer Science: an International Journal 3(1): 44-50. [Abstract] [PDF]

• Case study 02 using Example 10 (files: 10_adjacent_migration_isolated_mating.py, 10_long_migration_isolated_mating.py, and 10_no_migration_isolated_mating.py): Extends from Example 09 to examine the functionality of DOSE with having doubled background_mutation_rate across three different kinds of migration schemes. Reference: Castillo, CFG, Ling, MHT. 2014. Digital Organism Simulation Environment (DOSE): A Library for Ecologically-Based In Silico Experimental Evolution. Advances in Computer Science: an International Journal 3(1): 44-50. [Abstract] [PDF]

• [Example 11] (https://github.com/mauriceling/dose/blob/master/examples/11_no_migration_natural_selection.py) (file: 11_no_migration_natural_selection.py): Introduces DOSE's function to facilitate pre-generational and post-generational control schemes to simulate regular events before and after executing mating schemes. Features an implemented fitness scheme along with a goal to reach to examine the effects of natural selection on an evolving population.

• [Example 12] (https://github.com/mauriceling/dose/blob/master/examples/12_revive_simulation_11.py) (file: 12_revive_simulation_11.py): Extends the simulation of Example 11, showing DOSE's feature to revive a simulation from a database file given a specified generation.

• Case study 03 using Example 13 to 17 (files: 13_no_migration_truncated_selection.py, 14_revive_simulation_13_fitness_loss.py, 15_revive_simulation_14_fitness_gain.py, 16_no_migration_no_natural_selection.py, and 17_no_migration_proportionate_selection.py): Examines the gain-loss-regain of antibiotics resistant traits during initial antibiotics use, discontinuation of antibiotics after prevalent resistance, and subsequent re-use of the same antibiotics, assuming that no fitness cost is incurred for maintaining resistance. Reference: Castillo, CFG, Ling, MHT. 2014. Resistant Traits in Digital Organisms Do Not Revert Preselection Status despite Extended Deselection: Implications to Microbial Antibiotics Resistance. BioMed Research International 2014, Article ID 648389.

• Case study 04 using Example 17 and 18 (files: 17_no_migration_proportionate_selection.py and 18_revive_simulation_17_fitness_cost_fitness_loss.py): Examines the gain-loss-regain of antibiotics resistant traits during initial antibiotics use, discontinuation of antibiotics after prevalent resistance, and subsequent re-use of the same antibiotics, assuming that fitness cost based on GC content of chromosome. Reference: Castillo, CFG, Chay ZE, Ling, MHT. 2015. Resistance Maintained in Digital Organisms Despite Guanine/Cytosine-Based Fitness Cost and Extended De-Selection: Implications to Microbial Antibiotics Resistance. MOJ Proteomics & Bioinformatics 2(2): 00039.