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1 """
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2 Scripts to generate a tabular file of a metabolic model (built-in or custom).
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3
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4 This script loads a COBRA model (built-in or custom), optionally applies
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5 medium and gene nomenclature settings, derives reaction-related metadata
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6 (GPR rules, formulas, bounds, objective coefficients, medium membership,
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7 and compartments for ENGRO2), and writes a tabular summary.
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8 """
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9
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10 import os
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11 import csv
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12 import cobra
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13 import argparse
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14 import pandas as pd
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15 import utils.general_utils as utils
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16 from typing import Optional, Tuple, List
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17 import utils.model_utils as modelUtils
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18 import logging
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19
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20 ARGS : argparse.Namespace
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21 def process_args(args: List[str] = None) -> argparse.Namespace:
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22 """
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23 Parse command-line arguments for metabolic_model_setting.
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24 """
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25
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26 parser = argparse.ArgumentParser(
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27 usage="%(prog)s [options]",
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28 description="Generate custom data from a given model"
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29 )
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30
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31 parser.add_argument("--out_log", type=str, required=True,
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32 help="Output log file")
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33
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34 parser.add_argument("--model", type=str,
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35 help="Built-in model identifier (e.g., ENGRO2, Recon, HMRcore)")
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36 parser.add_argument("--input", type=str,
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37 help="Custom model file (JSON or XML)")
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38 parser.add_argument("--name", type=str, required=True,
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39 help="Model name (default or custom)")
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40
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41 parser.add_argument("--medium_selector", type=str, required=True,
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42 help="Medium selection option")
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43
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44 parser.add_argument("--gene_format", type=str, default="Default",
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45 help="Gene nomenclature format: Default (original), ENSNG, HGNC_SYMBOL, HGNC_ID, ENTREZ")
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46
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47 parser.add_argument("--out_tabular", type=str,
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48 help="Output file for the merged dataset (CSV or XLSX)")
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49
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50 parser.add_argument("--tool_dir", type=str, default=os.path.dirname(__file__),
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51 help="Tool directory (passed from Galaxy as $__tool_directory__)")
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52
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53
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54 return parser.parse_args(args)
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55
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56 ################################- INPUT DATA LOADING -################################
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57 def load_custom_model(file_path :utils.FilePath, ext :Optional[utils.FileFormat] = None) -> cobra.Model:
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58 """
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59 Loads a custom model from a file, either in JSON, XML, MAT, or YML format.
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60
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61 Args:
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62 file_path : The path to the file containing the custom model.
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63 ext : explicit file extension. Necessary for standard use in galaxy because of its weird behaviour.
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64
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65 Raises:
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66 DataErr : if the file is in an invalid format or cannot be opened for whatever reason.
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67
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68 Returns:
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69 cobra.Model : the model, if successfully opened.
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70 """
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71 ext = ext if ext else file_path.ext
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72 try:
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73 if ext is utils.FileFormat.XML:
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74 return cobra.io.read_sbml_model(file_path.show())
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75
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76 if ext is utils.FileFormat.JSON:
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77 return cobra.io.load_json_model(file_path.show())
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78
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79 if ext is utils.FileFormat.MAT:
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80 return cobra.io.load_matlab_model(file_path.show())
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81
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82 if ext is utils.FileFormat.YML:
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83 return cobra.io.load_yaml_model(file_path.show())
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84
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85 except Exception as e: raise utils.DataErr(file_path, e.__str__())
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86 raise utils.DataErr(
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87 file_path,
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88 f"Unrecognized format '{file_path.ext}'. Only JSON, XML, MAT, YML are supported."
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89 )
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90
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91
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92 ###############################- FILE SAVING -################################
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93 def save_as_csv_filePath(data :dict, file_path :utils.FilePath, fieldNames :Tuple[str, str]) -> None:
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94 """
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95 Saves any dictionary-shaped data in a .csv file created at the given file_path as FilePath.
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96
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97 Args:
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98 data : the data to be written to the file.
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99 file_path : the path to the .csv file.
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100 fieldNames : the names of the fields (columns) in the .csv file.
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101
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102 Returns:
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103 None
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104 """
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105 with open(file_path.show(), 'w', newline='') as csvfile:
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106 writer = csv.DictWriter(csvfile, fieldnames = fieldNames, dialect="excel-tab")
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107 writer.writeheader()
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108
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109 for key, value in data.items():
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110 writer.writerow({ fieldNames[0] : key, fieldNames[1] : value })
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111
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112 def save_as_csv(data :dict, file_path :str, fieldNames :Tuple[str, str]) -> None:
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113 """
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114 Saves any dictionary-shaped data in a .csv file created at the given file_path as string.
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115
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116 Args:
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117 data : the data to be written to the file.
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118 file_path : the path to the .csv file.
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119 fieldNames : the names of the fields (columns) in the .csv file.
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120
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121 Returns:
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122 None
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123 """
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124 with open(file_path, 'w', newline='') as csvfile:
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125 writer = csv.DictWriter(csvfile, fieldnames = fieldNames, dialect="excel-tab")
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126 writer.writeheader()
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127
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128 for key, value in data.items():
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129 writer.writerow({ fieldNames[0] : key, fieldNames[1] : value })
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130
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131 def save_as_tabular_df(df: pd.DataFrame, path: str) -> None:
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132 """
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133 Save a pandas DataFrame as a tab-separated file, creating directories as needed.
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134
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135 Args:
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136 df: The DataFrame to write.
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137 path: Destination file path (will be written as TSV).
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138
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139 Raises:
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140 DataErr: If writing the output fails for any reason.
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141
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142 Returns:
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143 None
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144 """
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145 try:
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146 os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
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147 df.to_csv(path, sep="\t", index=False)
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148 except Exception as e:
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149 raise utils.DataErr(path, f"failed writing tabular output: {e}")
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150
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151
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152 ###############################- ENTRY POINT -################################
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153 def main(args:List[str] = None) -> None:
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154 """
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155 Initialize and generate custom data based on the frontend input arguments.
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156
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157 Returns:
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158 None
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159 """
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160 # Parse args from frontend (Galaxy XML)
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161 global ARGS
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162 ARGS = process_args(args)
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163
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164
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165 if ARGS.input:
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166 # Load a custom model from file
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167 model = load_custom_model(
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168 utils.FilePath.fromStrPath(ARGS.input), utils.FilePath.fromStrPath(ARGS.name).ext)
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169 else:
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170 # Load a built-in model
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171
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172 try:
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173 model_enum = utils.Model[ARGS.model] # e.g., Model['ENGRO2']
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174 except KeyError:
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175 raise utils.ArgsErr("model", "one of Recon/ENGRO2/HMRcore/Custom_model", ARGS.model)
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176
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177 # Load built-in model (Model.getCOBRAmodel uses tool_dir to locate local models)
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178 try:
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179 model = model_enum.getCOBRAmodel(toolDir=ARGS.tool_dir)
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180 except Exception as e:
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181 # Wrap/normalize load errors as DataErr for consistency
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182 raise utils.DataErr(ARGS.model, f"failed loading built-in model: {e}")
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183
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184 # Determine final model name: explicit --name overrides, otherwise use the model id
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185
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186 model_name = ARGS.name if ARGS.name else ARGS.model
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187
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188 if ARGS.name == "ENGRO2" and ARGS.medium_selector != "Default":
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189 df_mediums = pd.read_csv(ARGS.tool_dir + "/local/medium/medium.csv", index_col = 0)
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190 ARGS.medium_selector = ARGS.medium_selector.replace("_", " ")
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191 medium = df_mediums[[ARGS.medium_selector]]
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192 medium = medium[ARGS.medium_selector].to_dict()
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193
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194 # Reset all medium reactions lower bound to zero
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195 for rxn_id, _ in model.medium.items():
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196 model.reactions.get_by_id(rxn_id).lower_bound = float(0.0)
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197
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198 # Apply selected medium uptake bounds (negative for uptake)
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199 for reaction, value in medium.items():
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200 if value is not None:
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201 model.reactions.get_by_id(reaction).lower_bound = -float(value)
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202
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203 if (ARGS.name == "Recon" or ARGS.name == "ENGRO2") and ARGS.gene_format != "Default":
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204 logging.basicConfig(level=logging.INFO)
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205 logger = logging.getLogger(__name__)
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206
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207 model = modelUtils.translate_model_genes(
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208 model=model,
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209 mapping_df= pd.read_csv(ARGS.tool_dir + "/local/mappings/genes_human.csv", dtype={'entrez_id': str}),
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210 target_nomenclature=ARGS.gene_format,
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211 source_nomenclature='HGNC_symbol',
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212 logger=logger
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213 )
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214
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215 # generate data
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216 rules = modelUtils.generate_rules(model, asParsed = False)
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217 reactions = modelUtils.generate_reactions(model, asParsed = False)
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218 bounds = modelUtils.generate_bounds(model)
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219 medium = modelUtils.get_medium(model)
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220 objective_function = modelUtils.extract_objective_coefficients(model)
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221
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222 if ARGS.name == "ENGRO2":
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223 compartments = modelUtils.generate_compartments(model)
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224
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225 df_rules = pd.DataFrame(list(rules.items()), columns = ["ReactionID", "GPR"])
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226 df_reactions = pd.DataFrame(list(reactions.items()), columns = ["ReactionID", "Formula"])
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227
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228 df_bounds = bounds.reset_index().rename(columns = {"index": "ReactionID"})
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229 df_medium = medium.rename(columns = {"reaction": "ReactionID"})
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230 df_medium["InMedium"] = True
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231
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232 merged = df_reactions.merge(df_rules, on = "ReactionID", how = "outer")
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233 merged = merged.merge(df_bounds, on = "ReactionID", how = "outer")
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234 merged = merged.merge(objective_function, on = "ReactionID", how = "outer")
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235 if ARGS.name == "ENGRO2":
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236 merged = merged.merge(compartments, on = "ReactionID", how = "outer")
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237 merged = merged.merge(df_medium, on = "ReactionID", how = "left")
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238
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239 merged["InMedium"] = merged["InMedium"].fillna(False)
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240
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241 merged = merged.sort_values(by = "InMedium", ascending = False)
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242
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243 if not ARGS.out_tabular:
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244 raise utils.ArgsErr("out_tabular", "output path (--out_tabular) is required when output_format == tabular", ARGS.out_tabular)
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245 save_as_tabular_df(merged, ARGS.out_tabular)
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246 expected = ARGS.out_tabular
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247
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248 # verify output exists and non-empty
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249 if not expected or not os.path.exists(expected) or os.path.getsize(expected) == 0:
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250 raise utils.DataErr(expected, "Output not created or empty")
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251
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252 print("Metabolic_model_setting: completed successfully")
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253
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254 if __name__ == '__main__':
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255
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256 main()
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