Simulation of a neuro-epithelium

Vertex model simulation on a toroidal domain, a model for growth and morphogenesis of the developing neural tube.

Python 3 version of this original code. Study published in

Guerrero, P., et al. (2019). Neuronal differentiation influences progenitor arrangement in the vertebrate neuroepithelium. Development, 146(23) https://doi.org/10.1242/dev.176297

Requirements

• Python 3.7.9
• numpy
• matplotlib
• numba
• ...

Installation

It's recommended to setup a conda environment with the above requirements (here called py37)

conda create -n py37 python==3.7.9 numpy matplotlib numba jupyter
conda init $SHELL
conda activate py37

Once you setup your python installation and/or activated your conda environment

git clone https://github.com/Rebeca99/IKNM_model.git
cd IKNM_model/
pip install -e .

Run

You can test the installation and the functions available with the IKNM_model from the jupyter notebook in example_simulation_17_04_21.ipynb:

jupyter-notebook example_simulation_17_04_21.ipynb

Main functions

The main functions to look at, in order to see how to set differentiation rates etc, are in IKNM_model/run_select_final.py:

• simulation_with_division
• simulation_with_division_clone
• simulation_with_division_clone_differentiation
• simulation_with_division_clone_differenciation_3stripes

IKNM model with delayed drift, stochasticity and crowding force (Rebeca)

• simulation_with_division_model_1 -> delayed drift + noise -> used, in the end, for my project
• simulation_with_division_model_2 -> drift = velocity + noise
• simulation_with_division_model_3 -> delayed drift + noise + crowding force -> used, in the end, for my project
• simulation_with_division_model_4 -> drift = velocity + noise + crowding force

These are the functions to be called for a simulation run. The function run_simulation_INM is a wrapper for these function, and allows to select which of these to be run, along with other options.