Fungal Structure Code

Before attempting to run the code:

• Go to the line "cwd_path ='/Users/libra/FBI_project/fungalGrowthModel_singleNutrient_py/0521'" in the driver_fungalGrowth_singleNutrient.py and change the path to where you place the folder containing all files in this repository.
• Go to "def get_filepath(params):" in help_functions.py to change the name of folder string and file string.

To Run the code:

• Option 1: Just run the driver_fungalGrowth_singleNutrient.py file
  • The number of iterations of runs is determined by the variable ‘num_runs’ (set around line 269 - later will be an input if this file changes to a function)
  • If num_runs==1, then it will run just one iteration of a fungal mycelia growth
  • If num_runs>1, then it will run multiple iterations using the same set of parameters in parallel using the python Parallel function from the joblib package.
• Option 2: run the function (from within the py file listed in Option 1) using the command ‘driver_singleNutrient(1)’ to run one iteration of fungal mycelia growth.

About the various files:

• driver_fungalGrowth_singleNutrient.py: contains the main driver file that executes the steps of diffusion in the external domain, elongation, branching, fusion, translocation, and uptake.
• parameters.ini: where all the parameters values are stored. If you want to change a parameter, it is most likely listed in this file.
• helper_functions.py: contains functions that do the following
  • convert parameters from the ini file to a usable form
  • generates filenames used for saving data specific to simulation with given parameters
  • plotting funtions (the fungal structure, the external domain, various output stats)
• setup_functions.py: contains functions that do the following
  • set up the dictionary for storing info related to the mycelia structure
  • sets up the initial fungal structure and it’s properties
  • sets up the external grid shape and amount of nutrient in each cell
• growth_functions.py: contains functions that pertain to
  • elongation of hyphae at the tip
  • branching
  • anastomosis (fusion) of hyphae
• nutrient_functions.py: contains functions that pertain to
  • translocation update
  • uptake of nutrients

Particulars for branch Bill1:

• The code is set-up to run and get the same results as the main branch. Run this first to convince yourself.
• To run the version with updated Michaelis-Menten kinetics, in each of grow_functions.py, nutrient_functions.py and driver_fungalGrowth_singleNutrient.py set the variable use_original = 0.
• • The Michaelis Menten parameters still need to be tweaked. Also, I attempted a new approach to choose whether a branch should be created or not. It also does not work correctly yet.
• Out of the box, this code produces minimal branching; ideally, we want it to produce branching something like the original.

Metabolism:

Possible To Do - Soonish:

• Translocation
  • Different velocity coefficient for cell wall materials at septa (should slow down when passing through a septa)
  • Convert velocity of cell wall materials from a constant to a function that takes into account that the number of vissicles of cell wall material being transported in myosin-like filaments can become saturated. The likely effect is that segments closer to the tip may have higher concentrations of cell wall material than those further from the tip.
• Branching
  • Change the function for calculating the probability of branching. An exponential probability function is now available, but not sure if the results are any different.

Possible To Do - Future:

• Apical branching: We may want to model Neurospora or another fungi instead.
• Growth Direction
  • Instead of pulling angles from a distribution, possibly have it grow towards higher levels of oxygen
  • Implement negative autotrophism (hyphal avoidance)?
• Aging
  • Different behavior for older hyphae near the center of the colony vs younger near the periphery - direction, growth rate, metabolism, etc.
  • Varying diameters of hyphae - older hyphae thicker than younger
• 3D