Construction of a comprehensive model of the ErbB signaling network

From text into executable models

1. Use pasmopy.Text2Model to build a mechanistic model

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

2. Rename erbb_network/ to CCLE_name or TCGA_ID, e.g., MCF7_BREAST or TCGA_3C_AALK_01A

    import shutil
   
    shutil.move(
        os.path.join("models", "erbb_network"),
        os.path.join("models", "breast", "TCGA_3C_AALK_01A")
    )
   

Other tasks for incorporating gene expression levels

1. Add weighting factors for each gene (prefix: "w_") to name2idx/parameters.py

    from pasmopy import Model
    from pasmopy.preprocessing import WeightingFactors
   
    from models import erbb_network
   
   
    model = Model(erbb_network.__package__).create()
   
    gene_expression = {
        "ErbB1": ["EGFR"],
        "ErbB2": ["ERBB2"],
        "ErbB3": ["ERBB3"],
        "ErbB4": ["ERBB4"],
        "Grb2": ["GRB2"],
        "Shc": ["SHC1", "SHC2", "SHC3", "SHC4"],
        "RasGAP": ["RASA1", "RASA2", "RASA3"],
        "PI3K": ["PIK3CA", "PIK3CB", "PIK3CD", "PIK3CG"],
        "PTEN": ["PTEN"],
        "SOS": ["SOS1", "SOS2"],
        "Gab1": ["GAB1"],
        "RasGDP": ["HRAS", "KRAS", "NRAS"],
        "Raf": ["ARAF", "BRAF", "RAF1"],
        "MEK": ["MAP2K1", "MAP2K2"],
        "ERK": ["MAPK1", "MAPK3"],
        "Akt": ["AKT1", "AKT2"],
        "PTP1B": ["PTPN1"],
        "GSK3b": ["GSK3B"],
        "DUSP": ["DUSP5", "DUSP6", "DUSP7"],
        "cMyc": ["MYC"],
    }
   
    weighting_factors = WeightingFactors(model, gene_expression)
    weighting_factors.add_to_params()
    weighting_factors.set_search_bounds()
   

2. Edit SearchParam class

    import os
   
    import numpy as np
   
    from pasmopy import Individualization
   
    from . import __path__
    from .name2idx import C, V
    from .set_model import initial_values, param_values
   
   
    incorporating_gene_expression_levels = Individualization(
        parameters=C.NAMES,
        species=V.NAMES,
        transcriptomic_data=os.path.join("transcriptomic_data", "TPM_RLE_postComBat_BRCA_BREAST.csv"),
        gene_expression={
            "ErbB1": ["EGFR"],
            "ErbB2": ["ERBB2"],
            "ErbB3": ["ERBB3"],
            "ErbB4": ["ERBB4"],
            "Grb2": ["GRB2"],
            "Shc": ["SHC1", "SHC2", "SHC3", "SHC4"],
            "RasGAP": ["RASA1", "RASA2", "RASA3"],
            "PI3K": ["PIK3CA", "PIK3CB", "PIK3CD", "PIK3CG"],
            "PTEN": ["PTEN"],
            "SOS": ["SOS1", "SOS2"],
            "Gab1": ["GAB1"],
            "RasGDP": ["HRAS", "KRAS", "NRAS"],
            "Raf": ["ARAF", "BRAF", "RAF1"],
            "MEK": ["MAP2K1", "MAP2K2"],
            "ERK": ["MAPK1", "MAPK3"],
            "Akt": ["AKT1", "AKT2"],
            "PTP1B": ["PTPN1"],
            "GSK3b": ["GSK3B"],
            "DUSP": ["DUSP5", "DUSP6", "DUSP7"],
            "cMyc": ["MYC"],
        },
        read_csv_kws={"index_col": "Description"}
    )
   
    ...
   
    class SearchParam(object):
   
        ...
   
        def update(self, indiv):
            x = param_values()
            y0 = initial_values()
   
            for i, j in enumerate(self.idx_params):
                x[j] = indiv[i]
            for i, j in enumerate(self.idx_initials):
                y0[j] = indiv[i + len(self.idx_params)]
   
            # As maximal transcription rate
            x[C.V291] = incorporating_gene_expression_levels.as_reaction_rate(
                __path__[0].split(os.sep)[-1], x, "V291", "DUSP"
            )
            x[C.V310] = incorporating_gene_expression_levels.as_reaction_rate(
                __path__[0].split(os.sep)[-1], x, "V310", "cMyc"
            )
            # As initial conditions
            y0 = incorporating_gene_expression_levels.as_initial_conditions(
                __path__[0].split(os.sep)[-1], x, y0
            )
   
            ...