Exploring spatial pattern formation in cell-based models, starting with Turing patterns in PhysiCell
This repo houses the code for the ENRG-E 399/599 final project for Team 1. Team members: [John Metzcar])(https://github.com/johnmetzcar) and Ben Duggan. The goal of this project is to create Turing-like patterns using PhysiCell. You can download the code and run it (found in the PhysiCell directory) or run it on (NanoHub)[].
Example simultion of the model:
There are 3 cells in the model: A cells, B cells and Wall cells.
- A cells are the inhibitor cells and are blue.
- Using live cycle model and apoptosis
- Motility enabled, persistance time of 5 minutes, migration bias set to 0 (completely random).
- Secretes alpha and is effected by beta
- B cells are the effected by A cells and are yellow.
- Using live cycle model and apoptosis
- Motility enabled, persistance time of 5 minutes, migration bias set to 0 (completely random).
- Secretes beta and is effected by alpha
- Wall cells are placed along the outside of the domain and prevent cells spilling outside of the domain. black.
- Using live cycle model and apoptosis but both have rates set to 0 (disabeling them)
- Motility disabled
- Secretes and uptakes nothing
Only alpha and beta chemiclas are used in this simulation (no oxygen).
The domain is initilized to be a 500 um x 500 um with dx=dy=dz=20 um. The simulations are designed to be run in 2D but could be converted to 3D with ease. The domain is initiallized with wall cells along the outer perimiter. The A and B cells are initilized ____________________- final initialization here.