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456
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1 """
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2 Utilities for generating and manipulating COBRA models and related metadata.
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3
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4 This module includes helpers to:
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5 - extract rules, reactions, bounds, objective coefficients, and compartments
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6 - build a COBRA model from a tabular file
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7 - set objective and medium from dataframes
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8 - validate a model and convert gene identifiers
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9 - translate model GPRs using mapping tables
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10 """
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418
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11 import os
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12 import cobra
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13 import pandas as pd
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419
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14 import re
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426
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15 import logging
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419
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16 from typing import Optional, Tuple, Union, List, Dict, Set
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426
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17 from collections import defaultdict
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418
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18 import utils.rule_parsing as rulesUtils
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419
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19 import utils.reaction_parsing as reactionUtils
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20 from cobra import Model as cobraModel, Reaction, Metabolite
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490
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21 import sys
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22
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23
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24 ############################ check_methods ####################################
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25 def gene_type(l :str, name :str) -> str:
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26 """
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27 Determine the type of gene ID.
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28
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29 Args:
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30 l (str): The gene identifier to check.
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31 name (str): The name of the dataset, used in error messages.
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32
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33 Returns:
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34 str: The type of gene ID ('hugo_id', 'ensembl_gene_id', 'symbol', or 'entrez_id').
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35
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36 Raises:
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37 sys.exit: If the gene ID type is not supported, the execution is aborted.
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38 """
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39 if check_hgnc(l):
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40 return 'hugo_id'
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41 elif check_ensembl(l):
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42 return 'ensembl_gene_id'
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43 elif check_symbol(l):
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44 return 'symbol'
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45 elif check_entrez(l):
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46 return 'entrez_id'
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47 else:
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48 sys.exit('Execution aborted:\n' +
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49 'gene ID type in ' + name + ' not supported. Supported ID'+
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50 'types are: HUGO ID, Ensemble ID, HUGO symbol, Entrez ID\n')
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51
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52 def check_hgnc(l :str) -> bool:
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53 """
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54 Check if a gene identifier follows the HGNC format.
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55
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56 Args:
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57 l (str): The gene identifier to check.
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58
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59 Returns:
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60 bool: True if the gene identifier follows the HGNC format, False otherwise.
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61 """
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62 if len(l) > 5:
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63 if (l.upper()).startswith('HGNC:'):
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64 return l[5:].isdigit()
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65 else:
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66 return False
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67 else:
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68 return False
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69
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70 def check_ensembl(l :str) -> bool:
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71 """
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72 Check if a gene identifier follows the Ensembl format.
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73
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74 Args:
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75 l (str): The gene identifier to check.
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76
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77 Returns:
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78 bool: True if the gene identifier follows the Ensembl format, False otherwise.
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79 """
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80 return l.upper().startswith('ENS')
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81
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82
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83 def check_symbol(l :str) -> bool:
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84 """
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85 Check if a gene identifier follows the symbol format.
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86
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87 Args:
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88 l (str): The gene identifier to check.
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89
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90 Returns:
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91 bool: True if the gene identifier follows the symbol format, False otherwise.
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92 """
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93 if len(l) > 0:
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94 if l[0].isalpha() and l[1:].isalnum():
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95 return True
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96 else:
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97 return False
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98 else:
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99 return False
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100
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101 def check_entrez(l :str) -> bool:
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102 """
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103 Check if a gene identifier follows the Entrez ID format.
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104
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105 Args:
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106 l (str): The gene identifier to check.
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107
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108 Returns:
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109 bool: True if the gene identifier follows the Entrez ID format, False otherwise.
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110 """
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111 if len(l) > 0:
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112 return l.isdigit()
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113 else:
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114 return False
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418
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115
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116 ################################- DATA GENERATION -################################
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117 ReactionId = str
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419
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118 def generate_rules(model: cobraModel, *, asParsed = True) -> Union[Dict[ReactionId, rulesUtils.OpList], Dict[ReactionId, str]]:
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418
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119 """
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456
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120 Generate a dictionary mapping reaction IDs to GPR rules from the model.
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418
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121
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122 Args:
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456
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123 model: COBRA model to derive data from.
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124 asParsed: If True, parse rules into a nested list structure; otherwise keep raw strings.
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418
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125
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126 Returns:
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456
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127 Dict[ReactionId, rulesUtils.OpList]: Parsed rules by reaction ID.
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128 Dict[ReactionId, str]: Raw rules by reaction ID.
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418
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129 """
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130 _ruleGetter = lambda reaction : reaction.gene_reaction_rule
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131 ruleExtractor = (lambda reaction :
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132 rulesUtils.parseRuleToNestedList(_ruleGetter(reaction))) if asParsed else _ruleGetter
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133
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134 return {
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135 reaction.id : ruleExtractor(reaction)
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136 for reaction in model.reactions
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137 if reaction.gene_reaction_rule }
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138
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419
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139 def generate_reactions(model :cobraModel, *, asParsed = True) -> Dict[ReactionId, str]:
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418
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140 """
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456
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141 Generate a dictionary mapping reaction IDs to reaction formulas from the model.
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418
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142
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143 Args:
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456
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144 model: COBRA model to derive data from.
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145 asParsed: If True, convert formulas into a parsed representation; otherwise keep raw strings.
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418
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146
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147 Returns:
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456
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148 Dict[ReactionId, str]: Reactions by reaction ID (parsed if requested).
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418
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149 """
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150
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151 unparsedReactions = {
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152 reaction.id : reaction.reaction
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153 for reaction in model.reactions
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154 if reaction.reaction
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155 }
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156
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157 if not asParsed: return unparsedReactions
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158
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159 return reactionUtils.create_reaction_dict(unparsedReactions)
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160
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419
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161 def get_medium(model:cobraModel) -> pd.DataFrame:
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456
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162 """
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163 Extract the uptake reactions representing the model medium.
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164
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165 Returns a DataFrame with a single column 'reaction' listing exchange reactions
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166 with negative lower bound and no positive stoichiometric coefficients (uptake only).
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167 """
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418
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168 trueMedium=[]
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169 for r in model.reactions:
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170 positiveCoeff=0
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171 for m in r.metabolites:
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172 if r.get_coefficient(m.id)>0:
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173 positiveCoeff=1;
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174 if (positiveCoeff==0 and r.lower_bound<0):
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175 trueMedium.append(r.id)
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176
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177 df_medium = pd.DataFrame()
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178 df_medium["reaction"] = trueMedium
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179 return df_medium
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180
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426
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181 def extract_objective_coefficients(model: cobraModel) -> pd.DataFrame:
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182 """
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456
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183 Extract objective coefficients for each reaction.
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184
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426
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185 Args:
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456
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186 model: COBRA model
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187
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426
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188 Returns:
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456
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189 pd.DataFrame with columns: ReactionID, ObjectiveCoefficient
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426
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190 """
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191 coeffs = []
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456
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192 # model.objective.expression is a linear expression
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426
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193 objective_expr = model.objective.expression.as_coefficients_dict()
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194
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195 for reaction in model.reactions:
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196 coeff = objective_expr.get(reaction.forward_variable, 0.0)
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197 coeffs.append({
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198 "ReactionID": reaction.id,
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199 "ObjectiveCoefficient": coeff
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200 })
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201
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202 return pd.DataFrame(coeffs)
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203
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419
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204 def generate_bounds(model:cobraModel) -> pd.DataFrame:
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456
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205 """
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206 Build a DataFrame of lower/upper bounds for all reactions.
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207
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208 Returns:
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209 pd.DataFrame indexed by reaction IDs with columns ['lower_bound', 'upper_bound'].
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210 """
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418
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211
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212 rxns = []
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213 for reaction in model.reactions:
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214 rxns.append(reaction.id)
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215
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216 bounds = pd.DataFrame(columns = ["lower_bound", "upper_bound"], index=rxns)
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217
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218 for reaction in model.reactions:
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219 bounds.loc[reaction.id] = [reaction.lower_bound, reaction.upper_bound]
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220 return bounds
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221
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222
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223
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419
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224 def generate_compartments(model: cobraModel) -> pd.DataFrame:
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418
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225 """
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226 Generates a DataFrame containing compartment information for each reaction.
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227 Creates columns for each compartment position (Compartment_1, Compartment_2, etc.)
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228
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229 Args:
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230 model: the COBRA model to extract compartment data from.
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231
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232 Returns:
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233 pd.DataFrame: DataFrame with ReactionID and compartment columns
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234 """
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235 pathway_data = []
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236
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237 # First pass: determine the maximum number of pathways any reaction has
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238 max_pathways = 0
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239 reaction_pathways = {}
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240
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241 for reaction in model.reactions:
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242 # Get unique pathways from all metabolites in the reaction
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243 if type(reaction.annotation['pathways']) == list:
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244 reaction_pathways[reaction.id] = reaction.annotation['pathways']
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245 max_pathways = max(max_pathways, len(reaction.annotation['pathways']))
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246 else:
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247 reaction_pathways[reaction.id] = [reaction.annotation['pathways']]
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248
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249 # Create column names for pathways
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250 pathway_columns = [f"Pathway_{i+1}" for i in range(max_pathways)]
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251
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252 # Second pass: create the data
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253 for reaction_id, pathways in reaction_pathways.items():
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254 row = {"ReactionID": reaction_id}
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255
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256 # Fill pathway columns
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257 for i in range(max_pathways):
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258 col_name = pathway_columns[i]
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259 if i < len(pathways):
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260 row[col_name] = pathways[i]
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261 else:
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262 row[col_name] = None # or "" if you prefer empty strings
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263
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264 pathway_data.append(row)
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265
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419
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266 return pd.DataFrame(pathway_data)
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267
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268
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269
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270 def build_cobra_model_from_csv(csv_path: str, model_id: str = "new_model") -> cobraModel:
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271 """
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456
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272 Build a COBRApy model from a tabular file with reaction data.
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273
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419
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274 Args:
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456
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275 csv_path: Path to the tab-separated file.
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276 model_id: ID for the newly created model.
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277
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419
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278 Returns:
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456
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279 cobra.Model: The constructed COBRApy model.
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419
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280 """
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281
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282 df = pd.read_csv(csv_path, sep='\t')
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283
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284 model = cobraModel(model_id)
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285
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286 metabolites_dict = {}
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287 compartments_dict = {}
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288
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456
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289 print(f"Building model from {len(df)} reactions...")
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419
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290
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291 for idx, row in df.iterrows():
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448
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292 reaction_formula = str(row['Formula']).strip()
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419
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293 if not reaction_formula or reaction_formula == 'nan':
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294 continue
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295
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296 metabolites = extract_metabolites_from_reaction(reaction_formula)
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297
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298 for met_id in metabolites:
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299 compartment = extract_compartment_from_metabolite(met_id)
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300
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301 if compartment not in compartments_dict:
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302 compartments_dict[compartment] = compartment
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303
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304 if met_id not in metabolites_dict:
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305 metabolites_dict[met_id] = Metabolite(
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306 id=met_id,
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307 compartment=compartment,
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308 name=met_id.replace(f"_{compartment}", "").replace("__", "_")
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309 )
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310
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311 model.compartments = compartments_dict
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312
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313 model.add_metabolites(list(metabolites_dict.values()))
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314
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456
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315 print(f"Added {len(metabolites_dict)} metabolites and {len(compartments_dict)} compartments")
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419
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316
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317 reactions_added = 0
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318 reactions_skipped = 0
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319
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320 for idx, row in df.iterrows():
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321
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322 reaction_id = str(row['ReactionID']).strip()
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427
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323 reaction_formula = str(row['Formula']).strip()
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419
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324
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325 if not reaction_formula or reaction_formula == 'nan':
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456
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326 raise ValueError(f"Missing reaction formula for {reaction_id}")
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419
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327
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328 reaction = Reaction(reaction_id)
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329 reaction.name = reaction_id
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330
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331 reaction.lower_bound = float(row['lower_bound']) if pd.notna(row['lower_bound']) else -1000.0
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332 reaction.upper_bound = float(row['upper_bound']) if pd.notna(row['upper_bound']) else 1000.0
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333
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427
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334 if pd.notna(row['GPR']) and str(row['GPR']).strip():
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335 reaction.gene_reaction_rule = str(row['GPR']).strip()
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419
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336
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337 try:
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338 parse_reaction_formula(reaction, reaction_formula, metabolites_dict)
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339 except Exception as e:
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456
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340 print(f"Error parsing reaction {reaction_id}: {e}")
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419
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341 reactions_skipped += 1
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342 continue
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343
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344 model.add_reactions([reaction])
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345 reactions_added += 1
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346
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347
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456
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348 print(f"Added {reactions_added} reactions, skipped {reactions_skipped} reactions")
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419
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349
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430
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350 # set objective function
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351 set_objective_from_csv(model, df, obj_col="ObjectiveCoefficient")
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352
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419
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353 set_medium_from_data(model, df)
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354
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456
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355 print(f"Model completed: {len(model.reactions)} reactions, {len(model.metabolites)} metabolites")
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419
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356
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357 return model
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358
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359
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360 # Estrae tutti gli ID metaboliti nella formula (gestisce prefissi numerici + underscore)
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361 def extract_metabolites_from_reaction(reaction_formula: str) -> Set[str]:
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362 """
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456
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363 Extract metabolite IDs from a reaction formula.
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364 Robust pattern: tokens ending with _<compartment> (e.g., _c, _m, _e),
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365 allowing leading digits/underscores.
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419
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366 """
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367 metabolites = set()
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456
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368 # optional coefficient followed by a token ending with _<letters>
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493
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369 if reaction_formula[-1] == ']' and reaction_formula[-3] == '[':
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370 pattern = r'(?:\d+(?:\.\d+)?\s+)?([A-Za-z0-9_]+[[A-Za-z0-9]]+)'
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371 else:
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372 pattern = r'(?:\d+(?:\.\d+)?\s+)?([A-Za-z0-9_]+_[A-Za-z0-9]+)'
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419
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373 matches = re.findall(pattern, reaction_formula)
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374 metabolites.update(matches)
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375 return metabolites
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376
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377
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378 def extract_compartment_from_metabolite(metabolite_id: str) -> str:
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456
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379 """Extract the compartment from a metabolite ID."""
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419
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380 if '_' in metabolite_id:
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381 return metabolite_id.split('_')[-1]
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493
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382 if metabolite_id[-1] == ']' and metabolite_id[-3] == '[':
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383 return metabolite_id[-2]
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419
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384 return 'c' # default cytoplasm
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385
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386
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387 def parse_reaction_formula(reaction: Reaction, formula: str, metabolites_dict: Dict[str, Metabolite]):
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456
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388 """Parse a reaction formula and set metabolites with their coefficients."""
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419
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389
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390 if '<=>' in formula:
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391 left, right = formula.split('<=>')
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392 reversible = True
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393 elif '<--' in formula:
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394 left, right = formula.split('<--')
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395 reversible = False
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396 elif '-->' in formula:
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397 left, right = formula.split('-->')
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398 reversible = False
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399 elif '<-' in formula:
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400 left, right = formula.split('<-')
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401 reversible = False
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402 else:
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456
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403 raise ValueError(f"Unrecognized reaction format: {formula}")
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419
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404
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405 reactants = parse_metabolites_side(left.strip())
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406 products = parse_metabolites_side(right.strip())
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407
|
|
|
408 metabolites_to_add = {}
|
|
|
409
|
|
|
410 for met_id, coeff in reactants.items():
|
|
|
411 if met_id in metabolites_dict:
|
|
|
412 metabolites_to_add[metabolites_dict[met_id]] = -coeff
|
|
|
413
|
|
|
414 for met_id, coeff in products.items():
|
|
|
415 if met_id in metabolites_dict:
|
|
|
416 metabolites_to_add[metabolites_dict[met_id]] = coeff
|
|
|
417
|
|
|
418 reaction.add_metabolites(metabolites_to_add)
|
|
|
419
|
|
|
420
|
|
|
421 def parse_metabolites_side(side_str: str) -> Dict[str, float]:
|
|
456
|
422 """Parse one side of a reaction and extract metabolites with coefficients."""
|
|
419
|
423 metabolites = {}
|
|
|
424 if not side_str or side_str.strip() == '':
|
|
|
425 return metabolites
|
|
|
426
|
|
|
427 terms = side_str.split('+')
|
|
|
428 for term in terms:
|
|
|
429 term = term.strip()
|
|
|
430 if not term:
|
|
|
431 continue
|
|
|
432
|
|
456
|
433 # optional coefficient + id ending with _<compartment>
|
|
419
|
434 match = re.match(r'(?:(\d+\.?\d*)\s+)?([A-Za-z0-9_]+_[a-z]+)', term)
|
|
|
435 if match:
|
|
|
436 coeff_str, met_id = match.groups()
|
|
|
437 coeff = float(coeff_str) if coeff_str else 1.0
|
|
|
438 metabolites[met_id] = coeff
|
|
|
439
|
|
|
440 return metabolites
|
|
|
441
|
|
|
442
|
|
|
443
|
|
430
|
444 def set_objective_from_csv(model: cobra.Model, df: pd.DataFrame, obj_col: str = "ObjectiveCoefficient"):
|
|
419
|
445 """
|
|
430
|
446 Sets the model's objective function based on a column of coefficients in the CSV.
|
|
|
447 Can be any reaction(s), not necessarily biomass.
|
|
419
|
448 """
|
|
430
|
449 obj_dict = {}
|
|
419
|
450
|
|
430
|
451 for idx, row in df.iterrows():
|
|
|
452 reaction_id = str(row['ReactionID']).strip()
|
|
|
453 coeff = float(row[obj_col]) if pd.notna(row[obj_col]) else 0.0
|
|
|
454 if coeff != 0:
|
|
|
455 if reaction_id in model.reactions:
|
|
|
456 obj_dict[model.reactions.get_by_id(reaction_id)] = coeff
|
|
|
457 else:
|
|
|
458 print(f"Warning: reaction {reaction_id} not found in model, skipping for objective.")
|
|
|
459
|
|
|
460 if not obj_dict:
|
|
|
461 raise ValueError("No reactions found with non-zero objective coefficient.")
|
|
|
462
|
|
|
463 model.objective = obj_dict
|
|
|
464 print(f"Objective set with {len(obj_dict)} reactions.")
|
|
|
465
|
|
|
466
|
|
419
|
467
|
|
|
468
|
|
|
469 def set_medium_from_data(model: cobraModel, df: pd.DataFrame):
|
|
456
|
470 """Set the medium based on the 'InMedium' column in the dataframe."""
|
|
419
|
471 medium_reactions = df[df['InMedium'] == True]['ReactionID'].tolist()
|
|
|
472
|
|
|
473 medium_dict = {}
|
|
|
474 for rxn_id in medium_reactions:
|
|
|
475 if rxn_id in [r.id for r in model.reactions]:
|
|
|
476 reaction = model.reactions.get_by_id(rxn_id)
|
|
|
477 if reaction.lower_bound < 0: # Solo reazioni di uptake
|
|
|
478 medium_dict[rxn_id] = abs(reaction.lower_bound)
|
|
|
479
|
|
|
480 if medium_dict:
|
|
|
481 model.medium = medium_dict
|
|
456
|
482 print(f"Medium set with {len(medium_dict)} components")
|
|
419
|
483
|
|
|
484
|
|
|
485 def validate_model(model: cobraModel) -> Dict[str, any]:
|
|
456
|
486 """Validate the model and return basic statistics."""
|
|
419
|
487 validation = {
|
|
|
488 'num_reactions': len(model.reactions),
|
|
|
489 'num_metabolites': len(model.metabolites),
|
|
|
490 'num_genes': len(model.genes),
|
|
|
491 'num_compartments': len(model.compartments),
|
|
|
492 'objective': str(model.objective),
|
|
|
493 'medium_size': len(model.medium),
|
|
|
494 'reversible_reactions': len([r for r in model.reactions if r.reversibility]),
|
|
|
495 'exchange_reactions': len([r for r in model.reactions if r.id.startswith('EX_')]),
|
|
|
496 }
|
|
|
497
|
|
|
498 try:
|
|
456
|
499 # Growth test
|
|
419
|
500 solution = model.optimize()
|
|
|
501 validation['growth_rate'] = solution.objective_value
|
|
|
502 validation['status'] = solution.status
|
|
|
503 except Exception as e:
|
|
|
504 validation['growth_rate'] = None
|
|
|
505 validation['status'] = f"Error: {e}"
|
|
|
506
|
|
|
507 return validation
|
|
|
508
|
|
456
|
509 def convert_genes(model, annotation):
|
|
|
510 """Rename genes using a selected annotation key in gene.notes; returns a model copy."""
|
|
419
|
511 from cobra.manipulation import rename_genes
|
|
|
512 model2=model.copy()
|
|
|
513 try:
|
|
|
514 dict_genes={gene.id:gene.notes[annotation] for gene in model2.genes}
|
|
|
515 except:
|
|
|
516 print("No annotation in gene dict!")
|
|
|
517 return -1
|
|
|
518 rename_genes(model2,dict_genes)
|
|
|
519
|
|
426
|
520 return model2
|
|
|
521
|
|
|
522 # ---------- Utility helpers ----------
|
|
|
523 def _normalize_colname(col: str) -> str:
|
|
|
524 return col.strip().lower().replace(' ', '_')
|
|
|
525
|
|
|
526 def _choose_columns(mapping_df: 'pd.DataFrame') -> Dict[str, str]:
|
|
|
527 """
|
|
456
|
528 Find useful columns and return a dict {ensg: colname1, hgnc_id: colname2, ...}.
|
|
|
529 Raise ValueError if no suitable mapping is found.
|
|
426
|
530 """
|
|
|
531 cols = { _normalize_colname(c): c for c in mapping_df.columns }
|
|
|
532 chosen = {}
|
|
456
|
533 # candidate names for each category
|
|
426
|
534 candidates = {
|
|
|
535 'ensg': ['ensg', 'ensembl_gene_id', 'ensembl'],
|
|
|
536 'hgnc_id': ['hgnc_id', 'hgnc', 'hgnc:'],
|
|
444
|
537 'hgnc_symbol': ['hgnc_symbol', 'hgnc symbol', 'symbol'],
|
|
455
|
538 'entrez_id': ['entrez', 'entrez_id', 'entrezgene'],
|
|
|
539 'gene_number': ['gene_number']
|
|
426
|
540 }
|
|
|
541 for key, names in candidates.items():
|
|
|
542 for n in names:
|
|
|
543 if n in cols:
|
|
|
544 chosen[key] = cols[n]
|
|
|
545 break
|
|
|
546 return chosen
|
|
|
547
|
|
|
548 def _validate_target_uniqueness(mapping_df: 'pd.DataFrame',
|
|
|
549 source_col: str,
|
|
|
550 target_col: str,
|
|
|
551 model_source_genes: Optional[Set[str]] = None,
|
|
|
552 logger: Optional[logging.Logger] = None) -> None:
|
|
|
553 """
|
|
456
|
554 Check that, within the filtered mapping_df, each target maps to at most one source.
|
|
|
555 Log examples if duplicates are found.
|
|
426
|
556 """
|
|
|
557 if logger is None:
|
|
|
558 logger = logging.getLogger(__name__)
|
|
|
559
|
|
|
560 if mapping_df.empty:
|
|
|
561 logger.warning("Mapping dataframe is empty for the requested source genes; skipping uniqueness validation.")
|
|
|
562 return
|
|
|
563
|
|
456
|
564 # normalize temporary columns for grouping (without altering the original df)
|
|
426
|
565 tmp = mapping_df[[source_col, target_col]].copy()
|
|
|
566 tmp['_src_norm'] = tmp[source_col].astype(str).map(_normalize_gene_id)
|
|
|
567 tmp['_tgt_norm'] = tmp[target_col].astype(str).str.strip()
|
|
|
568
|
|
456
|
569 # optionally filter to the set of model source genes
|
|
426
|
570 if model_source_genes is not None:
|
|
|
571 tmp = tmp[tmp['_src_norm'].isin(model_source_genes)]
|
|
|
572
|
|
|
573 if tmp.empty:
|
|
|
574 logger.warning("After filtering to model source genes, mapping table is empty — nothing to validate.")
|
|
|
575 return
|
|
|
576
|
|
456
|
577 # build reverse mapping: target -> set(sources)
|
|
426
|
578 grouped = tmp.groupby('_tgt_norm')['_src_norm'].agg(lambda s: set(s.dropna()))
|
|
456
|
579 # find targets with more than one source
|
|
426
|
580 problematic = {t: sorted(list(s)) for t, s in grouped.items() if len(s) > 1}
|
|
|
581
|
|
|
582 if problematic:
|
|
456
|
583 # prepare warning message with examples (limited subset)
|
|
455
|
584 sample_items = list(problematic.items())
|
|
426
|
585 msg_lines = ["Mapping validation failed: some target IDs are associated with multiple source IDs."]
|
|
|
586 for tgt, sources in sample_items:
|
|
|
587 msg_lines.append(f" - target '{tgt}' <- sources: {', '.join(sources)}")
|
|
|
588 full_msg = "\n".join(msg_lines)
|
|
456
|
589 # log warning
|
|
455
|
590 logger.warning(full_msg)
|
|
426
|
591
|
|
456
|
592 # if everything is fine
|
|
426
|
593 logger.info("Mapping validation passed: no target ID is associated with multiple source IDs (within filtered set).")
|
|
|
594
|
|
|
595
|
|
|
596 def _normalize_gene_id(g: str) -> str:
|
|
456
|
597 """Normalize a gene ID for use as a key (removes prefixes like 'HGNC:' and strips)."""
|
|
426
|
598 if g is None:
|
|
|
599 return ""
|
|
|
600 g = str(g).strip()
|
|
|
601 # remove common prefixes
|
|
|
602 g = re.sub(r'^(HGNC:)', '', g, flags=re.IGNORECASE)
|
|
|
603 g = re.sub(r'^(ENSG:)', '', g, flags=re.IGNORECASE)
|
|
|
604 return g
|
|
|
605
|
|
493
|
606 def _is_or_only_expression(expr: str) -> bool:
|
|
|
607 """
|
|
|
608 Check if a GPR expression contains only OR operators (no AND operators).
|
|
|
609
|
|
|
610 Args:
|
|
|
611 expr: GPR expression string
|
|
|
612
|
|
|
613 Returns:
|
|
|
614 bool: True if expression contains only OR (and parentheses) and has multiple genes, False otherwise
|
|
|
615 """
|
|
|
616 if not expr or not expr.strip():
|
|
|
617 return False
|
|
|
618
|
|
|
619 # Normalize the expression
|
|
|
620 normalized = expr.replace(' AND ', ' and ').replace(' OR ', ' or ')
|
|
|
621
|
|
|
622 # Check if it contains any AND operators
|
|
|
623 has_and = ' and ' in normalized.lower()
|
|
|
624
|
|
|
625 # Check if it contains OR operators
|
|
|
626 has_or = ' or ' in normalized.lower()
|
|
|
627
|
|
|
628 # Must have OR operators and no AND operators
|
|
|
629 return has_or and not has_and
|
|
|
630
|
|
|
631
|
|
|
632 def _flatten_or_only_gpr(expr: str) -> str:
|
|
|
633 """
|
|
|
634 Flatten a GPR expression that contains only OR operators by:
|
|
|
635 1. Removing all parentheses
|
|
|
636 2. Extracting unique gene names
|
|
|
637 3. Joining them with ' or '
|
|
|
638
|
|
|
639 Args:
|
|
|
640 expr: GPR expression string with only OR operators
|
|
|
641
|
|
|
642 Returns:
|
|
|
643 str: Flattened GPR expression
|
|
|
644 """
|
|
|
645 if not expr or not expr.strip():
|
|
|
646 return expr
|
|
|
647
|
|
|
648 # Extract all gene tokens (exclude logical operators and parentheses)
|
|
|
649 gene_pattern = r'\b[A-Za-z0-9:_.-]+\b'
|
|
|
650 logical = {'and', 'or', 'AND', 'OR', '(', ')'}
|
|
|
651
|
|
|
652 tokens = re.findall(gene_pattern, expr)
|
|
|
653 genes = [t for t in tokens if t not in logical]
|
|
|
654
|
|
|
655 # Create set to remove duplicates, then convert back to list to maintain some order
|
|
|
656 unique_genes = list(dict.fromkeys(genes)) # Preserves insertion order
|
|
|
657
|
|
|
658 if len(unique_genes) == 0:
|
|
|
659 return expr
|
|
|
660 elif len(unique_genes) == 1:
|
|
|
661 return unique_genes[0]
|
|
|
662 else:
|
|
|
663 return ' or '.join(unique_genes)
|
|
|
664
|
|
|
665
|
|
455
|
666 def _simplify_boolean_expression(expr: str) -> str:
|
|
|
667 """
|
|
490
|
668 Simplify a boolean expression by removing duplicates while strictly preserving semantics.
|
|
|
669 This function handles simple duplicates within parentheses while being conservative about
|
|
|
670 complex expressions that could change semantics.
|
|
455
|
671 """
|
|
|
672 if not expr or not expr.strip():
|
|
|
673 return expr
|
|
|
674
|
|
490
|
675 # Normalize operators and whitespace
|
|
455
|
676 expr = expr.replace(' AND ', ' and ').replace(' OR ', ' or ')
|
|
490
|
677 expr = ' '.join(expr.split()) # Normalize whitespace
|
|
455
|
678
|
|
490
|
679 def simplify_parentheses_content(match_obj):
|
|
|
680 """Helper function to simplify content within parentheses."""
|
|
|
681 content = match_obj.group(1) # Content inside parentheses
|
|
455
|
682
|
|
490
|
683 # Only simplify if it's a pure OR or pure AND chain
|
|
|
684 if ' or ' in content and ' and ' not in content:
|
|
|
685 # Pure OR chain - safe to deduplicate
|
|
|
686 parts = [p.strip() for p in content.split(' or ') if p.strip()]
|
|
|
687 unique_parts = []
|
|
|
688 seen = set()
|
|
|
689 for part in parts:
|
|
|
690 if part not in seen:
|
|
|
691 unique_parts.append(part)
|
|
|
692 seen.add(part)
|
|
455
|
693
|
|
490
|
694 if len(unique_parts) == 1:
|
|
|
695 return unique_parts[0] # Remove unnecessary parentheses for single items
|
|
|
696 else:
|
|
|
697 return '(' + ' or '.join(unique_parts) + ')'
|
|
|
698
|
|
|
699 elif ' and ' in content and ' or ' not in content:
|
|
|
700 # Pure AND chain - safe to deduplicate
|
|
|
701 parts = [p.strip() for p in content.split(' and ') if p.strip()]
|
|
|
702 unique_parts = []
|
|
|
703 seen = set()
|
|
|
704 for part in parts:
|
|
|
705 if part not in seen:
|
|
|
706 unique_parts.append(part)
|
|
|
707 seen.add(part)
|
|
455
|
708
|
|
490
|
709 if len(unique_parts) == 1:
|
|
|
710 return unique_parts[0] # Remove unnecessary parentheses for single items
|
|
|
711 else:
|
|
|
712 return '(' + ' and '.join(unique_parts) + ')'
|
|
|
713 else:
|
|
|
714 # Mixed operators or single item - return with parentheses as-is
|
|
|
715 return '(' + content + ')'
|
|
|
716
|
|
|
717 def remove_duplicates_simple(parts_str: str, separator: str) -> str:
|
|
|
718 """Remove duplicates from a simple chain of operations."""
|
|
|
719 parts = [p.strip() for p in parts_str.split(separator) if p.strip()]
|
|
455
|
720
|
|
490
|
721 # Remove duplicates while preserving order
|
|
|
722 unique_parts = []
|
|
|
723 seen = set()
|
|
|
724 for part in parts:
|
|
|
725 if part not in seen:
|
|
|
726 unique_parts.append(part)
|
|
|
727 seen.add(part)
|
|
455
|
728
|
|
490
|
729 if len(unique_parts) == 1:
|
|
|
730 return unique_parts[0]
|
|
455
|
731 else:
|
|
490
|
732 return f' {separator} '.join(unique_parts)
|
|
455
|
733
|
|
|
734 try:
|
|
490
|
735 import re
|
|
|
736
|
|
|
737 # First, simplify content within parentheses
|
|
|
738 # This handles cases like (A or A) -> A and (B and B) -> B
|
|
|
739 expr_simplified = re.sub(r'\(([^()]+)\)', simplify_parentheses_content, expr)
|
|
|
740
|
|
|
741 # Check if the resulting expression has mixed operators
|
|
|
742 has_and = ' and ' in expr_simplified
|
|
|
743 has_or = ' or ' in expr_simplified
|
|
|
744
|
|
|
745 # Only simplify top-level if it's pure AND or pure OR
|
|
|
746 if has_and and not has_or and '(' not in expr_simplified:
|
|
|
747 # Pure AND chain at top level - safe to deduplicate
|
|
|
748 return remove_duplicates_simple(expr_simplified, 'and')
|
|
|
749 elif has_or and not has_and and '(' not in expr_simplified:
|
|
|
750 # Pure OR chain at top level - safe to deduplicate
|
|
|
751 return remove_duplicates_simple(expr_simplified, 'or')
|
|
|
752 else:
|
|
|
753 # Mixed operators or has parentheses - return the simplified version (with parentheses content cleaned)
|
|
|
754 return expr_simplified
|
|
|
755
|
|
455
|
756 except Exception:
|
|
490
|
757 # If anything goes wrong, return the original expression
|
|
455
|
758 return expr
|
|
|
759
|
|
492
|
760
|
|
426
|
761 def translate_model_genes(model: 'cobra.Model',
|
|
|
762 mapping_df: 'pd.DataFrame',
|
|
|
763 target_nomenclature: str,
|
|
|
764 source_nomenclature: str = 'hgnc_id',
|
|
455
|
765 allow_many_to_one: bool = False,
|
|
490
|
766 logger: Optional[logging.Logger] = None) -> Tuple['cobra.Model', Dict[str, str]]:
|
|
426
|
767 """
|
|
456
|
768 Translate model genes from source_nomenclature to target_nomenclature using a mapping table.
|
|
|
769 mapping_df should contain columns enabling mapping (e.g., ensg, hgnc_id, hgnc_symbol, entrez).
|
|
|
770
|
|
455
|
771 Args:
|
|
456
|
772 model: COBRA model to translate.
|
|
|
773 mapping_df: DataFrame containing the mapping information.
|
|
|
774 target_nomenclature: Desired target key (e.g., 'hgnc_symbol').
|
|
|
775 source_nomenclature: Current source key in the model (default 'hgnc_id').
|
|
|
776 allow_many_to_one: If True, allow many-to-one mappings and handle duplicates in GPRs.
|
|
|
777 logger: Optional logger.
|
|
490
|
778
|
|
|
779 Returns:
|
|
|
780 Tuple containing:
|
|
|
781 - Translated COBRA model
|
|
|
782 - Dictionary mapping reaction IDs to translation issue descriptions
|
|
426
|
783 """
|
|
|
784 if logger is None:
|
|
|
785 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
|
|
|
786 logger = logging.getLogger(__name__)
|
|
|
787
|
|
|
788 logger.info(f"Translating genes from '{source_nomenclature}' to '{target_nomenclature}'")
|
|
|
789
|
|
|
790 # normalize column names and choose relevant columns
|
|
|
791 chosen = _choose_columns(mapping_df)
|
|
|
792 if not chosen:
|
|
|
793 raise ValueError("Could not detect useful columns in mapping_df. Expected at least one of: ensg, hgnc_id, hgnc_symbol, entrez.")
|
|
|
794
|
|
|
795 # map source/target to actual dataframe column names (allow user-specified source/target keys)
|
|
|
796 # normalize input args
|
|
|
797 src_key = source_nomenclature.strip().lower()
|
|
|
798 tgt_key = target_nomenclature.strip().lower()
|
|
|
799
|
|
|
800 # try to find the actual column names for requested keys
|
|
|
801 col_for_src = None
|
|
|
802 col_for_tgt = None
|
|
|
803 # first, try exact match
|
|
|
804 for k, actual in chosen.items():
|
|
|
805 if k == src_key:
|
|
|
806 col_for_src = actual
|
|
|
807 if k == tgt_key:
|
|
|
808 col_for_tgt = actual
|
|
|
809
|
|
|
810 # if not found, try mapping common names
|
|
|
811 if col_for_src is None:
|
|
|
812 possible_src_names = {k: v for k, v in chosen.items()}
|
|
|
813 # try to match by contained substring
|
|
|
814 for k, actual in possible_src_names.items():
|
|
|
815 if src_key in k:
|
|
|
816 col_for_src = actual
|
|
|
817 break
|
|
|
818
|
|
|
819 if col_for_tgt is None:
|
|
|
820 for k, actual in chosen.items():
|
|
|
821 if tgt_key in k:
|
|
|
822 col_for_tgt = actual
|
|
|
823 break
|
|
|
824
|
|
|
825 if col_for_src is None:
|
|
|
826 raise ValueError(f"Source column for '{source_nomenclature}' not found in mapping dataframe.")
|
|
|
827 if col_for_tgt is None:
|
|
|
828 raise ValueError(f"Target column for '{target_nomenclature}' not found in mapping dataframe.")
|
|
|
829
|
|
|
830 model_source_genes = { _normalize_gene_id(g.id) for g in model.genes }
|
|
|
831 logger.info(f"Filtering mapping to {len(model_source_genes)} source genes present in model (normalized).")
|
|
|
832
|
|
|
833 tmp_map = mapping_df[[col_for_src, col_for_tgt]].dropna().copy()
|
|
|
834 tmp_map[col_for_src + "_norm"] = tmp_map[col_for_src].astype(str).map(_normalize_gene_id)
|
|
|
835
|
|
|
836 filtered_map = tmp_map[tmp_map[col_for_src + "_norm"].isin(model_source_genes)].copy()
|
|
|
837
|
|
|
838 if filtered_map.empty:
|
|
|
839 logger.warning("No mapping rows correspond to source genes present in the model after filtering. Proceeding with empty mapping (no translation will occur).")
|
|
|
840
|
|
455
|
841 if not allow_many_to_one:
|
|
|
842 _validate_target_uniqueness(filtered_map, col_for_src, col_for_tgt, model_source_genes=model_source_genes, logger=logger)
|
|
426
|
843
|
|
455
|
844 # Crea il mapping
|
|
426
|
845 gene_mapping = _create_gene_mapping(filtered_map, col_for_src, col_for_tgt, logger)
|
|
|
846
|
|
|
847 # copy model
|
|
|
848 model_copy = model.copy()
|
|
|
849
|
|
|
850 # statistics
|
|
493
|
851 stats = {'translated': 0, 'one_to_one': 0, 'one_to_many': 0, 'not_found': 0, 'simplified_gprs': 0, 'flattened_or_gprs': 0}
|
|
426
|
852 unmapped = []
|
|
|
853 multi = []
|
|
490
|
854
|
|
|
855 # Dictionary to store translation issues per reaction
|
|
|
856 reaction_translation_issues = {}
|
|
426
|
857
|
|
|
858 original_genes = {g.id for g in model_copy.genes}
|
|
|
859 logger.info(f"Original genes count: {len(original_genes)}")
|
|
|
860
|
|
|
861 # translate GPRs
|
|
|
862 for rxn in model_copy.reactions:
|
|
|
863 gpr = rxn.gene_reaction_rule
|
|
|
864 if gpr and gpr.strip():
|
|
490
|
865 new_gpr, rxn_issues = _translate_gpr(gpr, gene_mapping, stats, unmapped, multi, logger, track_issues=True)
|
|
|
866 if rxn_issues:
|
|
|
867 reaction_translation_issues[rxn.id] = rxn_issues
|
|
|
868
|
|
426
|
869 if new_gpr != gpr:
|
|
493
|
870 # Check if this GPR has translation issues and contains only OR operators
|
|
|
871 if rxn_issues and _is_or_only_expression(new_gpr):
|
|
|
872 # Flatten the GPR: remove parentheses and create set of unique genes
|
|
|
873 flattened_gpr = _flatten_or_only_gpr(new_gpr)
|
|
|
874 if flattened_gpr != new_gpr:
|
|
|
875 stats['flattened_or_gprs'] += 1
|
|
|
876 logger.debug(f"Flattened OR-only GPR with issues for {rxn.id}: '{new_gpr}' -> '{flattened_gpr}'")
|
|
|
877 new_gpr = flattened_gpr
|
|
|
878
|
|
455
|
879 simplified_gpr = _simplify_boolean_expression(new_gpr)
|
|
|
880 if simplified_gpr != new_gpr:
|
|
|
881 stats['simplified_gprs'] += 1
|
|
|
882 logger.debug(f"Simplified GPR for {rxn.id}: '{new_gpr}' -> '{simplified_gpr}'")
|
|
|
883 rxn.gene_reaction_rule = simplified_gpr
|
|
|
884 logger.debug(f"Reaction {rxn.id}: '{gpr}' -> '{simplified_gpr}'")
|
|
426
|
885
|
|
|
886 # update model genes based on new GPRs
|
|
|
887 _update_model_genes(model_copy, logger)
|
|
|
888
|
|
|
889 # final logging
|
|
|
890 _log_translation_statistics(stats, unmapped, multi, original_genes, model_copy.genes, logger)
|
|
|
891
|
|
|
892 logger.info("Translation finished")
|
|
490
|
893 return model_copy, reaction_translation_issues
|
|
426
|
894
|
|
|
895
|
|
|
896 # ---------- helper functions ----------
|
|
|
897 def _create_gene_mapping(mapping_df, source_col: str, target_col: str, logger: logging.Logger) -> Dict[str, List[str]]:
|
|
|
898 """
|
|
|
899 Build mapping dict: source_id -> list of target_ids
|
|
|
900 Normalizes IDs (removes prefixes like 'HGNC:' etc).
|
|
|
901 """
|
|
|
902 df = mapping_df[[source_col, target_col]].dropna().copy()
|
|
|
903 # normalize to string
|
|
|
904 df[source_col] = df[source_col].astype(str).map(_normalize_gene_id)
|
|
|
905 df[target_col] = df[target_col].astype(str).str.strip()
|
|
|
906
|
|
|
907 df = df.drop_duplicates()
|
|
|
908
|
|
|
909 logger.info(f"Creating mapping from {len(df)} rows")
|
|
|
910
|
|
|
911 mapping = defaultdict(list)
|
|
|
912 for _, row in df.iterrows():
|
|
|
913 s = row[source_col]
|
|
|
914 t = row[target_col]
|
|
|
915 if t not in mapping[s]:
|
|
|
916 mapping[s].append(t)
|
|
|
917
|
|
|
918 # stats
|
|
|
919 one_to_one = sum(1 for v in mapping.values() if len(v) == 1)
|
|
|
920 one_to_many = sum(1 for v in mapping.values() if len(v) > 1)
|
|
|
921 logger.info(f"Mapping: {len(mapping)} source keys, {one_to_one} 1:1, {one_to_many} 1:many")
|
|
|
922 return dict(mapping)
|
|
|
923
|
|
|
924
|
|
|
925 def _translate_gpr(gpr_string: str,
|
|
|
926 gene_mapping: Dict[str, List[str]],
|
|
|
927 stats: Dict[str, int],
|
|
|
928 unmapped_genes: List[str],
|
|
|
929 multi_mapping_genes: List[Tuple[str, List[str]]],
|
|
490
|
930 logger: logging.Logger,
|
|
|
931 track_issues: bool = False) -> Union[str, Tuple[str, str]]:
|
|
426
|
932 """
|
|
|
933 Translate genes inside a GPR string using gene_mapping.
|
|
490
|
934 Returns new GPR string, and optionally translation issues if track_issues=True.
|
|
426
|
935 """
|
|
|
936 # Generic token pattern: letters, digits, :, _, -, ., (captures HGNC:1234, ENSG000..., symbols)
|
|
|
937 token_pattern = r'\b[A-Za-z0-9:_.-]+\b'
|
|
|
938 tokens = re.findall(token_pattern, gpr_string)
|
|
|
939
|
|
|
940 logical = {'and', 'or', 'AND', 'OR', '(', ')'}
|
|
|
941 tokens = [t for t in tokens if t not in logical]
|
|
|
942
|
|
|
943 new_gpr = gpr_string
|
|
490
|
944 issues = []
|
|
426
|
945
|
|
|
946 for token in sorted(set(tokens), key=lambda x: -len(x)): # longer tokens first to avoid partial replacement
|
|
|
947 norm = _normalize_gene_id(token)
|
|
|
948 if norm in gene_mapping:
|
|
|
949 targets = gene_mapping[norm]
|
|
|
950 stats['translated'] += 1
|
|
|
951 if len(targets) == 1:
|
|
|
952 stats['one_to_one'] += 1
|
|
|
953 replacement = targets[0]
|
|
|
954 else:
|
|
|
955 stats['one_to_many'] += 1
|
|
|
956 multi_mapping_genes.append((token, targets))
|
|
|
957 replacement = "(" + " or ".join(targets) + ")"
|
|
490
|
958 if track_issues:
|
|
|
959 issues.append(f"{token} -> {' or '.join(targets)}")
|
|
426
|
960
|
|
|
961 pattern = r'\b' + re.escape(token) + r'\b'
|
|
|
962 new_gpr = re.sub(pattern, replacement, new_gpr)
|
|
|
963 else:
|
|
|
964 stats['not_found'] += 1
|
|
|
965 if token not in unmapped_genes:
|
|
|
966 unmapped_genes.append(token)
|
|
|
967 logger.debug(f"Token not found in mapping (left as-is): {token}")
|
|
|
968
|
|
490
|
969 # Check for many-to-one cases (multiple source genes mapping to same target)
|
|
|
970 if track_issues:
|
|
|
971 # Build reverse mapping to detect many-to-one cases from original tokens
|
|
|
972 original_to_target = {}
|
|
|
973
|
|
|
974 for orig_token in tokens:
|
|
|
975 norm = _normalize_gene_id(orig_token)
|
|
|
976 if norm in gene_mapping:
|
|
|
977 targets = gene_mapping[norm]
|
|
|
978 for target in targets:
|
|
|
979 if target not in original_to_target:
|
|
|
980 original_to_target[target] = []
|
|
|
981 if orig_token not in original_to_target[target]:
|
|
|
982 original_to_target[target].append(orig_token)
|
|
|
983
|
|
|
984 # Identify many-to-one mappings in this specific GPR
|
|
|
985 for target, original_genes in original_to_target.items():
|
|
|
986 if len(original_genes) > 1:
|
|
|
987 issues.append(f"{' or '.join(original_genes)} -> {target}")
|
|
|
988
|
|
|
989 issue_text = "; ".join(issues) if issues else ""
|
|
|
990
|
|
|
991 if track_issues:
|
|
|
992 return new_gpr, issue_text
|
|
|
993 else:
|
|
|
994 return new_gpr
|
|
426
|
995
|
|
|
996
|
|
|
997 def _update_model_genes(model: 'cobra.Model', logger: logging.Logger):
|
|
|
998 """
|
|
|
999 Rebuild model.genes from gene_reaction_rule content.
|
|
|
1000 Removes genes not referenced and adds missing ones.
|
|
|
1001 """
|
|
|
1002 # collect genes in GPRs
|
|
|
1003 gene_pattern = r'\b[A-Za-z0-9:_.-]+\b'
|
|
|
1004 logical = {'and', 'or', 'AND', 'OR', '(', ')'}
|
|
|
1005 genes_in_gpr: Set[str] = set()
|
|
|
1006
|
|
|
1007 for rxn in model.reactions:
|
|
|
1008 gpr = rxn.gene_reaction_rule
|
|
|
1009 if gpr and gpr.strip():
|
|
|
1010 toks = re.findall(gene_pattern, gpr)
|
|
|
1011 toks = [t for t in toks if t not in logical]
|
|
|
1012 # normalize IDs consistent with mapping normalization
|
|
|
1013 toks = [_normalize_gene_id(t) for t in toks]
|
|
|
1014 genes_in_gpr.update(toks)
|
|
|
1015
|
|
|
1016 # existing gene ids
|
|
|
1017 existing = {g.id for g in model.genes}
|
|
|
1018
|
|
|
1019 # remove obsolete genes
|
|
|
1020 to_remove = [gid for gid in existing if gid not in genes_in_gpr]
|
|
|
1021 removed = 0
|
|
|
1022 for gid in to_remove:
|
|
|
1023 try:
|
|
|
1024 gene_obj = model.genes.get_by_id(gid)
|
|
|
1025 model.genes.remove(gene_obj)
|
|
|
1026 removed += 1
|
|
|
1027 except Exception:
|
|
|
1028 # safe-ignore
|
|
|
1029 pass
|
|
|
1030
|
|
|
1031 # add new genes
|
|
|
1032 added = 0
|
|
|
1033 for gid in genes_in_gpr:
|
|
|
1034 if gid not in existing:
|
|
|
1035 new_gene = cobra.Gene(gid)
|
|
|
1036 try:
|
|
|
1037 model.genes.add(new_gene)
|
|
|
1038 except Exception:
|
|
|
1039 # fallback: if model.genes doesn't support add, try append or model.add_genes
|
|
|
1040 try:
|
|
|
1041 model.genes.append(new_gene)
|
|
|
1042 except Exception:
|
|
|
1043 try:
|
|
|
1044 model.add_genes([new_gene])
|
|
|
1045 except Exception:
|
|
|
1046 logger.warning(f"Could not add gene object for {gid}")
|
|
|
1047 added += 1
|
|
|
1048
|
|
|
1049 logger.info(f"Model genes updated: removed {removed}, added {added}")
|
|
|
1050
|
|
|
1051
|
|
|
1052 def _log_translation_statistics(stats: Dict[str, int],
|
|
|
1053 unmapped_genes: List[str],
|
|
|
1054 multi_mapping_genes: List[Tuple[str, List[str]]],
|
|
|
1055 original_genes: Set[str],
|
|
|
1056 final_genes,
|
|
|
1057 logger: logging.Logger):
|
|
|
1058 logger.info("=== TRANSLATION STATISTICS ===")
|
|
|
1059 logger.info(f"Translated: {stats.get('translated', 0)} (1:1 = {stats.get('one_to_one', 0)}, 1:many = {stats.get('one_to_many', 0)})")
|
|
|
1060 logger.info(f"Not found tokens: {stats.get('not_found', 0)}")
|
|
455
|
1061 logger.info(f"Simplified GPRs: {stats.get('simplified_gprs', 0)}")
|
|
493
|
1062 logger.info(f"Flattened OR-only GPRs with issues: {stats.get('flattened_or_gprs', 0)}")
|
|
426
|
1063
|
|
|
1064 final_ids = {g.id for g in final_genes}
|
|
|
1065 logger.info(f"Genes in model: {len(original_genes)} -> {len(final_ids)}")
|
|
|
1066
|
|
|
1067 if unmapped_genes:
|
|
|
1068 logger.warning(f"Unmapped tokens ({len(unmapped_genes)}): {', '.join(unmapped_genes[:20])}{(' ...' if len(unmapped_genes)>20 else '')}")
|
|
|
1069 if multi_mapping_genes:
|
|
|
1070 logger.info(f"Multi-mapping examples ({len(multi_mapping_genes)}):")
|
|
|
1071 for orig, targets in multi_mapping_genes[:10]:
|
|
490
|
1072 logger.info(f" {orig} -> {', '.join(targets)}")
|
|
493
|
1073
|
|
|
1074 # Log summary of flattened GPRs if any
|
|
|
1075 if stats.get('flattened_or_gprs', 0) > 0:
|
|
|
1076 logger.info(f"Flattened {stats['flattened_or_gprs']} OR-only GPRs that had translation issues (removed parentheses, created unique gene sets)")
|
|
490
|
1077
|
|
|
1078 |
