pks_stochastic

This repository contains code for the data generation and visualization for Stochastic modelling of PKS recombination in Streptomyces conducted at UZH at the Department of Evolutionary Biology and Environmental Studies under the supervision of Dr. Aleks Nivina and Prof. Dr. Wagner.

Table of contents

1. Code availability
   • Structure
2. Usage
   • Software requirements
   • Installation

Code availability

The code for running the stochastic models and their visualization is in the code folder. The data folder contains the images for each run with 20 replicatesa and summary figures in plotting folder in each folder for different selection modes.

Structure

The folder code contains 3 python files and one Jupyter notebook:

• no_selection.py - code for simulations under no selection, parallelized
• pretty_stochastic.py - code for simulations under selection, parallelized
• stochastic_plotting.ipynb - code for visualizations (different sections for processing no selection and selection data)
• utils.py - code for summary statistics used for visualization

The folder data contains the visualizations of the simulation and two folders:

• no_selection_f3less - plots the proportions of each genotype throughout simulation under no selection
• dif_selection_f3less - plots the proportions of each genotype throughout simulation under selection

Each of these subfolders also contains plotting folder, where summary statistics are visualized.

The project has the following structure:

pks_stochastic
│   README.md
│   requirements.txt  
│
└───code
│   │   no_selection.py
│   │   selection.py
│   │   stochastic_plotting.ipynb
│   │   utils.py
│ 
└───data
│   │
│   └───no_selection_f3less
│       │   ...
│       └───plotting
│       │      │   ...
│   │
│   └───dif_selection_f3less
│       │   ...
│       └───plotting
│       │      │   ...

Usage

All code was run on Python 3.12.7 and macOS Sonoma 14.4.

Software Requirements

• Python 3.12.7

Installation

1. You need git to be installed. Open terminal (Crtl+Alt+t) and run following commands:

git clone https://github.com/AnnaToi01/pks_stochastic.git
cd pks_stochastic

2. Create virtual environment

   • Via venv

     • Create virtual environment

       python -m venv venv

     • Activate it

       source venv/bin/activate

   • Via virtualenv

     • Install virtualenv if it is not installed.

       pip install virtualenv

     • Create virtual environment

       virtualenv venv --python=3.12

     • Activate it

       source ./venv/bin/activate

   • Via conda

     • Install Anaconda if not already installed (see Instructions).
     • Create virtual environment

       conda create --name <env_name> python=3.12

     • Activate it

       conda activate <env_name>

3. Install necessary libraries

pip install -r requirements.txt

After all required libraries installation you can launch the code and Jupyter notebooks.