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456
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1 """
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2 CBS backend utilities using GLPK for constraint-based sampling.
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3
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4 This module builds and solves LP problems from COBRA models, supports random
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5 objective function generation (CBS), and provides both swiglpk-based and
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6 COBRApy-based sampling fallbacks.
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7 """
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381
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8 from swiglpk import *
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9 import random
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10 import pandas as pd
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11 import numpy as np
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12 import cobra as cb
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13
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14 # Initialize LP problem
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15 def initialize_lp_problem(S):
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456
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16 """
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17 Prepare sparse matrix structures for GLPK given a stoichiometric matrix.
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18
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19 Args:
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20 S: Stoichiometric matrix (DOK or similar) as returned by cobra.util.create_stoichiometric_matrix.
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21
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22 Returns:
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23 tuple: (len_vector, values, indexes, ia, ja, ar, nrows, ncol)
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24 - len_vector: number of non-zero entries
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25 - values: list of non-zero values
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26 - indexes: list of (row, col) indices for non-zero entries
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27 - ia, ja, ar: GLPK-ready arrays for the sparse matrix
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28 - nrows, ncol: matrix shape
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29 """
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381
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30
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31 len_vector=len(S.keys())
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32 values=list(S.values())
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33 indexes=list(S.keys())
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34 ia = intArray(len_vector+1);
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35 ja = intArray(len_vector+1);
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36 ar = doubleArray(len_vector+1);
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37
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38 i=0
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39 ind_row=[indexes[i][0]+1 for i in range(0, len(values) )]
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40 ind_col=[indexes[i][1]+1 for i in range(0, len(values) )]
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41 for i in range(1, len(values) + 1):
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42 ia[i]=ind_row[i-1]
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43 ja[i]=ind_col[i-1]
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44 ar[i] = values[i-1]
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45
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46 nrows=S.shape[0]
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47 ncol=S.shape[1]
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48
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49 return len_vector, values, indexes, ia, ja, ar, nrows, ncol
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50
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51
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52
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53 def create_and_solve_lp_problem(lb,ub,nrows, ncol, len_vector, ia, ja, ar,
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54 obj_coefs,reactions,return_lp=False):
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456
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55 """
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56 Create and solve a GLPK LP problem for a metabolic model.
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57
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58 Args:
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59 lb, ub: Lower/upper bounds per reaction (lists of floats).
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60 nrows, ncol: Dimensions of the S matrix.
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61 len_vector, ia, ja, ar: Sparse matrix data prepared for GLPK.
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62 obj_coefs: Objective function coefficients (list of floats).
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63 reactions: Reaction identifiers (list of str), used for output mapping.
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64 return_lp: If True, also return the GLPK problem object (caller must delete).
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65
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66 Returns:
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67 tuple: (fluxes, Z) or (fluxes, Z, lp) if return_lp=True.
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68 """
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381
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69
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70
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71 lp = glp_create_prob();
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72 glp_set_prob_name(lp, "sample");
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73 glp_set_obj_dir(lp, GLP_MAX);
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74 glp_add_rows(lp, nrows);
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75 eps = 1e-16
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76 for i in range(nrows):
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77 glp_set_row_name(lp, i+1, "constrain_"+str(i+1));
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78 glp_set_row_bnds(lp, i+1, GLP_FX, 0.0, 0.0);
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79 glp_add_cols(lp, ncol);
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80 for i in range(ncol):
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81 glp_set_col_name(lp, i+1, "flux_"+str(i+1));
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82 glp_set_col_bnds(lp, i+1, GLP_DB,lb[i]-eps,ub[i]+eps);
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83 glp_load_matrix(lp, len_vector, ia, ja, ar);
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84
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85 try:
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86 fluxes,Z=solve_lp_problem(lp,obj_coefs,reactions)
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87 if return_lp:
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88 return fluxes,Z,lp
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89 else:
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90 glp_delete_prob(lp);
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91 return fluxes,Z
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92 except Exception as e:
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93 glp_delete_prob(lp)
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94 raise Exception(e)
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95
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96
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97 def solve_lp_problem(lp,obj_coefs,reactions):
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456
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98 """
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99 Configure and solve an LP with GLPK using provided objective coefficients.
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100
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101 Args:
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102 lp: GLPK problem handle.
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103 obj_coefs: Objective coefficients.
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104 reactions: Reaction identifiers (unused in computation; length used for output).
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105
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106 Returns:
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107 tuple: (fluxes, Z) where fluxes is a list of primal column values and Z is the objective value.
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108 """
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381
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109
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110 # Set the coefficients of the objective function
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111 i=1
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112 for ind_coef in obj_coefs:
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113 glp_set_obj_coef(lp, i, ind_coef);
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114 i+=1
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115
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116 # Initialize the parameters
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117 params=glp_smcp()
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118 params.presolve=GLP_ON
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119 params.msg_lev = GLP_MSG_ERR
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120 params.tm_lim=4000
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121 glp_init_smcp(params)
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122
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123 glp_term_out(GLP_OFF)
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124
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125 try:
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126
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127 # Solve the problem
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128 glp_scale_prob(lp,GLP_SF_AUTO)
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129
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130 value=glp_simplex(lp, params)
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131
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132 Z = glp_get_obj_val(lp);
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133
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134 if value == 0:
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135 fluxes = []
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136 for i in range(len(reactions)): fluxes.append(glp_get_col_prim(lp, i+1))
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137 return fluxes,Z
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138 else:
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139 raise Exception("error in LP problem. Problem:",str(value))
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140 except Exception as e:
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141 # Re-enable terminal output for error reporting
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142 glp_term_out(GLP_ON)
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143 raise Exception(e)
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144 finally:
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145 # Re-enable terminal output after solving
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146 glp_term_out(GLP_ON)
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147
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148 def create_lp_structure(model):
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456
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149 """
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150 Extract the LP structure (S matrix, bounds, objective) from a COBRA model.
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151
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152 Args:
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153 model (cobra.Model): The COBRA model.
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154
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155 Returns:
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156 tuple: (S, lb, ub, coefs_obj, reactions)
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157 """
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381
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158
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159 reactions=[el.id for el in model.reactions]
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160 coefs_obj=[reaction.objective_coefficient for reaction in model.reactions]
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161
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162 # Lower and upper bounds
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163 lb=[reaction.lower_bound for reaction in model.reactions]
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164 ub=[reaction.upper_bound for reaction in model.reactions]
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165
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166 # Create S matrix
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167 S=cb.util.create_stoichiometric_matrix(model,array_type="dok")
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168
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169 return S,lb,ub,coefs_obj,reactions
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170
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171 def randomObjectiveFunctionSampling(model, nsample, coefficients_df, df_sample):
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456
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172 """
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173 Sample fluxes using GLPK by iterating over random objective functions.
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174
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175 Args:
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176 model: COBRA model.
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177 nsample: Number of samples to generate.
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178 coefficients_df: DataFrame of objective coefficients (columns are samples; last row is type_of_problem).
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179 df_sample: Output DataFrame to fill with flux samples (index: sample, columns: reactions).
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180
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181 Returns:
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182 None
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183 """
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381
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184
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185 S,lb,ub,coefs_obj,reactions = create_lp_structure(model)
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186 len_vector, values, indexes, ia, ja, ar, nrow, ncol = initialize_lp_problem(S)
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187
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188 for i in range(nsample):
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189
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190 coefs_obj=coefficients_df.iloc[:,i].values
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191
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456
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192 if coefs_obj[-1]==1: # minimize
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381
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193 coefs_obj= coefs_obj[0:-1] * -1
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194 else:
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195 coefs_obj=coefs_obj[0:-1]
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196
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197 fluxes,Z = create_and_solve_lp_problem(lb,ub, nrow, ncol, len_vector,
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198 ia, ja, ar, coefs_obj,reactions,return_lp=False)
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199 df_sample.loc[i] = fluxes
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456
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200 return
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381
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201
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202 def randomObjectiveFunctionSampling_cobrapy(model, nsample, coefficients_df, df_sample):
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456
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203 """
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204 Fallback sampling using COBRApy's optimize with per-sample randomized objectives.
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205
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206 Args:
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207 model: COBRA model.
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208 nsample: Number of samples to generate.
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209 coefficients_df: DataFrame of objective coefficients (columns are samples; last row is type_of_problem).
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210 df_sample: Output DataFrame to fill with flux samples (index: sample, columns: reactions).
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211
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212 Returns:
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213 None
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214 """
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381
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215
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216 for i in range(nsample):
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217
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218 dict_coeff={}
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219 if(coefficients_df.iloc[-1][i]==1):
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220 type_problem = -1 # minimize
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221 else:
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456
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222 type_problem = 1 # maximize
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381
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223
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224 for rxn in [reaction.id for reaction in model.reactions]:
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225 dict_coeff[model.reactions.get_by_id(rxn)] = coefficients_df.loc[rxn][i] * type_problem
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226
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227 model.objective = dict_coeff
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228 solution = model.optimize().fluxes
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229 for rxn, flux in solution.items():
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230 df_sample.loc[i][rxn] = flux
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231
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456
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232 return
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233
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234 def randomObjectiveFunction(model, n_samples, df_fva, seed=0):
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235 """
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236 Create random objective function coefficients for CBS sampling.
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381
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237
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456
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238 The last row 'type_of_problem' encodes 0 for maximize and 1 for minimize.
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239
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240 Args:
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241 model: COBRA model.
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242 n_samples: Number of random objectives to generate.
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243 df_fva: Flux Variability Analysis results with 'minimum' and 'maximum' per reaction.
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244 seed: Seed for reproducibility.
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381
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245
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456
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246 Returns:
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247 pandas.DataFrame: Coefficients DataFrame indexed by reaction IDs plus 'type_of_problem'.
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248 """
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249 # reactions = model.reactions
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250 reactions = [reaction.id for reaction in model.reactions]
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251 cont = seed
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252 list_ex = reactions.copy()
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253 list_ex.append("type_of_problem")
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254 coefficients_df = pd.DataFrame(index=list_ex, columns=[str(i) for i in range(n_samples)])
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255
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256 for i in range(0, n_samples):
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381
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257
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258 cont = cont + 1
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259 random.seed(cont)
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260
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261 # Generate a random threshold in [0, 1]
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262 threshold = random.random()
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263
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264 for reaction in reactions:
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265
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266 cont = cont + 1
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267 random.seed(cont)
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456
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268
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269 val = random.random()
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381
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270
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456
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271 if val > threshold:
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272
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273 cont = cont + 1
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381
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274 random.seed(cont)
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456
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275
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276 # Coefficient in [-1, 1]
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277 c = 2 * random.random() - 1
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381
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278
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279 val_max = np.max([abs(df_fva.loc[reaction, "minimum"]), abs(df_fva.loc[reaction, "maximum"])])
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280
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281 if val_max != 0: # only if FVA is non-zero
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282 coefficients_df.loc[reaction, str(i)] = c / val_max # scale by FVA
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283 else:
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284 coefficients_df.loc[reaction, str(i)] = 0
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381
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285
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456
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286 else:
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287 coefficients_df.loc[reaction, str(i)] = 0
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381
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288
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289 cont = cont + 1
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290 random.seed(cont)
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381
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291
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456
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292 if random.random() < 0.5:
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293 coefficients_df.loc["type_of_problem", str(i)] = 0 # maximize
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294 else:
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295 coefficients_df.loc["type_of_problem", str(i)] = 1 # minimize
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296
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297 return coefficients_df
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