Python Juxtacrine Signalling Simulation

I need a computational model, written entirely in Python, that captures receptor–ligand interactions driving juxtacrine signalling. The focus is on contact-dependent communication, so each cell in the virtual tissue should bind, activate, and internalise ligands only when it directly touches a neighbour.

Core requirements
• A mechanistic representation of receptors and membrane-bound ligands at the single-cell level (ODE or discrete-time update, whichever you feel suits the biology).
• Spatial organisation: a small 2-D lattice or mesh is sufficient, provided it lets me vary neighbourhood size and boundary conditions.
• Parameter control through a config file or clear function arguments so I can sweep ligand density, receptor turnover, and binding affinities.
• Outputs that include time-series plots of bound complexes per cell and at least one heat-map style visualisation of signalling strength across the lattice.
• Well-commented code plus a short README that explains model assumptions, key equations, and how to reproduce the figures.

The project is complete when I can run a single command (preferably from a conda or pip-based environment), reproduce your example simulation, and tweak parameters without touching the core logic. If you plan to rely on specialist libraries beyond NumPy, SciPy, or Matplotlib, just flag them early so I can install the right versions.