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Tissue mechanics and morphogen transport in neural tube patterning

Content

Main file: simulations/neuraltube.py

Main sources, in nt_vertex:

  • NT_sim: definition of the NT_simulation class, which contains methods to set up output files, input parameters, run simulation, save and load checkpoints;
  • NT_vtx.py: full neural tube object;
  • FE_vtx.py: solving diffusion-degradation equation for morphogen with finite element method on a growing vertex model;
  • GeneRegulatoryNetwork.py: signalling and grn dynamics for individual cells.
  • mesh.py: Mesh object (and other auxiliary objects), containing the information about the vertex model geometry.
  • cells.py: Cells object, containing a Mesh as attribute, and other properties about cells.

Setup

You will need to have installed

  • python>=3.7.9, with
    • numpy
    • pandas
    • pip
    • scipy
    • seaborn
    • numba
    • matplotlib
    • dill
  • ffmpeg (as backend for videos)

A conda environment satisfying these requirements can be created from the environment file provided:

conda env create -f environment.yml

Activate the environment and install the package in it:

conda activate vertex
make

Usage

An example file which runs a simulation and produces a video of it, is simulations/neuraltube.py

You can copy neuraltube.py where you like.

After changing to the directory that contains the simulation script (here is simulations), you can check all the parameters that can be set from command line:

cd simulations
python neuraltube.py --help

For instance, a basic simulation setting a tag for the output directory, total simulation time (separately for initialization and full simulation), time-step and time between frames:

python neuraltube.py \
	[--prefix <tag for output directory>]
	[-t  <total simulation time>]
	[--init  <initialization time>]
	[--dt  <time step>]
	[--every  <time b/w frames>]

The same options can be passed to the NT_simulation constructor as keyword arguments. In this case, the command-line options are overwritten. The equivalent is obtained with

sim = NT_simulation(
		prefix="tag_for_output_directory">,
		T_sim=<total simulation time>,
		T_init=<initialization time>,
		dt=<time step>,
		frame_every=<time b/w frames>
	)

in neuraltube.py.