Individualization of the mechanistic model

Parameter estimation

Here, we use phospho-protein time-course datasets to train kinetic constants and weighting factors.

📒 Note: In this study, we used BioMASS.jl, but in most cases you can use pasmopy.optimize() function for parameter estimation.

1. Build a mechanistic model to identify model parameters

   import os
   
   from pasmopy import Text2Model
   
   
   Text2Model(os.path.join("models", "erbb_network.txt"), lang="julia").convert()
   

2. Add time-series data to experimental_data.jl

3. Set an objective function to be minimized in fitness.jl

4. Run optimize_parallel.sh

   $ mv erbb_network_jl training
   $ cd training
   $ mkdir errout
   $ sh optimize_parallel.sh  # It will take more than a few days to optimize parameters.
   $ cd ..
   

   When finished, run:

   $ julia
   

   using BioMASS
   
   param2biomass("training")
   

   And you will get dat2npy/out/. This is the estimated parameter sets that biomass can recognize and read. Copy out/ to each patient-specific model folder via:

   import os
   import shutil
   
   
   breast_cancer_models = []
   path_to_models = os.path.join("models", "breast")
   for model in os.listdir(path_to_models):
       if os.path.isdir(os.path.join(path_to_models, model)) and (
           model.startswith("TCGA_") or model.endswith("_BREAST")
       ):
           breast_cancer_models.append(model)
   # Set optimized parameters
   for model in breast_cancer_models:
       shutil.copytree(
           os.path.join("training", "erbb_network_jl", "dat2npy", "out"),
           os.path.join(path_to_models, f"{model}", "out"),
       )
   

Patient-specific simulations

• Use pasmopy.PatientModelSimulations

  import os
  import shutil
  from pathlib import Path
  
  from pasmopy import PatientModelSimulations
  
  import models.breast
  
  
  TCGA_ID = [
      l.strip() for l in Path("models", "breast", "sample_names.txt").read_text("utf-8").splitlines()
  ]
  # Create patient-specific models
  for patient in TCGA_ID:
      if patient != "TCGA_3C_AALK_01A":
          shutil.copytree(
              os.path.join("models", "breast", "TCGA_3C_AALK_01A"),
              os.path.join("models", "breast", f"{patient}"),
          )
  # Execute patient-specific models
  simulations = PatientModelSimulations(models.breast.__package__, TCGA_ID)
  simulations.run()