Aging of Evolving Genomes In Silico (AY-jis, /eɪd͡ʒɪs/)
Numerical model for life history evolution of age-structured populations under customizable ecological scenarios.
You can run AEGIS simulations on a webserver or locally. The webserver is especially useful if you want to try AEGIS out and run a couple of simple simulations. For more demanding simulations, it is best to install and run AEGIS on your local machine.
You can access the AEGIS webserver here. The server is running AEGIS GUI.
You can install AEGIS locally using pip (pip install aegis-sim
). The package is available on https://pypi.org/project/aegis-sim/. You can use AEGIS with a GUI or in a terminal. GUI is useful for running individual simulations, while the terminal is useful for running batches of simulations.
aegis gui # starts GUI
aegis sim -c {path/to/config_file} # runs a simulation within a terminal
aegis --help # shows help documentation
To run simulations within a terminal, you need to prepare config files in YAML format which contain custom values for simulation parameters. The list of parameters, including their descriptions and default values you can find here. An example of a config file:
RANDOM_SEED: 42
STEPS_PER_SIMULATION: 10000
AGE_LIMIT: 50
If you want to contribute to the codebase, install AEGIS from github:
python3 -m pip install -e git+https://github.com/valenzano-lab/aegis.git#egg=aegis-sim
If you are having installation issues, check that pip is up to date (python3 -m pip install --upgrade pip
).
Graphical user interface for AEGIS can be used on the webserver or with a local installation. It contains sections for launching and analyzing/plotting simulations.
Most documentation about the model is available within the GUI itself, including description of inputs, outputs, submodels and the genetic architecture. Use the webserver or a local installation to access the GUI. Further information is available in the following articles:
- AEGIS: An In Silico Tool to model Genome Evolution in Age-Structured Populations (2019)
- An In Silico Model to Simulate the Evolution of Biological Aging (2016)
Exhaustive, searchable API reference made by pdoc is available here.