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1 # Flux Simulation
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2
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3 Simulate flux distributions from constraint-based metabolic models using different optimization or sampling strategies.
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4
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5 ## Overview
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6
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7 Two types of analysis are available:
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8 - **flux optimization**
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9 - **flux sampling**
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10
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11 For flux optimization, one of the following methods can be performed: parsimonious-FBA, Flux Variability Analysis, Biomass sensitivity analysis (single reaction knock-out)
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12 The objective function, a linear combination of fluxes weighted by specific coefficients, depends on the provided metabolic network.
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13
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14 For flux sampling, one of the following methods can be performed: CBS (Corner-based sampling), OPTGP (Improved Artificial Centering Hit-and-Run sampler).
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15
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16 ## Galaxy Interface
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17
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18 In Galaxy: **COBRAxy → Flux Simulation**
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19
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20 1. Select model and upload bounds files
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21 2. Choose algorithm (CBS/OPTGP) and sampling parameters
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22 3. Click **Run tool**
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23
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24 ## Command-line console
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25
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26 ```bash
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27 flux_simulation -ms ENGRO2 \
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28 -in bounds/*.tsv \
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29 -ni Sample1,Sample2,Sample3 \
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30 -a CBS \
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31 -ns 1000 \
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32 -idop output/
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33 ```
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34
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35 ## Parameters
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36
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37 | Parameter | Flag | Description | Default |
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38 |-----------|------|-------------|---------|
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39 | Model Selector | `-ms` | ENGRO2, Recon, or Custom | ENGRO2 |
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40 | Input Format | `--model_and_bounds` | Separate files (true) or complete models (false) | true |
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41 | Input Bounds | `-in` | Bounds files | - |
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42 | Name Input | `-ni` | Sample names (comma-separated) | - |
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43 | Algorithm | `-a` | CBS or OPTGP | CBS |
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44 | Num Samples | `-ns` | Number of samples per batch | 1000 |
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45 | Num Batches | `-nb` | Number of batches | 1 |
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46 | Thinning | `-th` | OPTGP thinning parameter | 100 |
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47 | Output Type | `-ot` | mean, median, quantiles, fluxes | mean,median |
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48 | FVA Optimality | `--perc_opt` | Optimality fraction (0.0-1.0) | 0.90 |
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49 | Output Path | `-idop` | Output directory | flux_simulation/ |
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50
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51 ## Algorithms
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52
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53 ### CBS (Corner-Based Sampling)
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54 - Random objective optimization
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55 - Requires GLPK (recommended) or COBRApy solver
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56 - Suitable for large models
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57
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58 ### OPTGP (MCMC Sampling)
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59 - Markov Chain Monte Carlo
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60 - Uniform sampling guarantee
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61 - Requires thinning parameter
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62
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63 ## Input Modes
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64
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65 The tool supports two different input formats:
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66
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67 ### Mode 1: Model + Bounds (default, `--model_and_bounds true`)
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68 Upload one base model + multiple bound files (one per sample/context):
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69 - Base model: Tabular file with reaction structure (from Import Metabolic Model)
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70 - Bounds: Individual TSV files with sample-specific constraints (from RAS to Bounds)
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71 - Use when you have RAS-derived bounds for multiple samples
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72
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73 ### Mode 2: Multiple Complete Models (`--model_and_bounds false`)
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74 Upload pre-built model files, each already containing integrated bounds:
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75 - Each file is a complete tabular model with reaction structure + bounds
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76 - Use when models are already prepared with specific constraints
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77 - Useful for comparing different modelling scenarios
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78
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79 ## Input Format
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80
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81 Bounds files (TSV):
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82
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83 ```
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84 reaction lower_bound upper_bound
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85 R00001 -125.0 125.0
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86 R00002 -65.0 65.0
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87 ```
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88
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89 **File Format Notes:**
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90 - Use **tab-separated** values (TSV)
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91 - Column headers must be: reaction, lower_bound, upper_bound
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92 - Reaction IDs must match model reaction IDs
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93 - Numeric values for bounds
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94
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95 ## Sampling Outputs
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96
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97 The tool can generate different types of output from flux sampling:
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98
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99 | Output Type | Description |
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100 |-------------|-------------|
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101 | **mean** | Mean flux across all samples |
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102 | **median** | Median flux across all samples |
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103 | **quantiles** | 25th, 50th, 75th percentiles |
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104 | **fluxes** | Complete flux distributions (all samples, all reactions) |
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105
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106 **Note**: The `fluxes` output can be very large for many samples. Use summary statistics (mean/median/quantiles) unless you need the complete distribution.
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107
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108 ## Optimization Methods
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109
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110 In alternative to sampling, the tool can perform optimization analyses:
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111
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112 | Method | Description | Output |
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113 |--------|-------------|--------|
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114 | **FVA** | Flux Variability Analysis | Min/max flux ranges for each reaction |
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115 | **pFBA** | Parsimonious FBA | Flux distribution with minimal total flux |
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116 | **sensitivity** | Reaction knockout analysis | Biomass impact of single reaction deletions |
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117
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118 ### FVA Optimality Fraction
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119
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120 The `--perc_opt` parameter (default: 0.90) controls the optimality constraint for FVA:
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121 - **1.0**: Only optimal solutions (100% of maximum biomass)
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122 - **0.90**: Allow suboptimal solutions (≥90% of maximum biomass)
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123 - **Lower values**: Explore broader flux ranges
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124
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125 ## Output
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126
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127 - `mean.csv`: Mean flux values
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128 - `median.csv`: Median flux values
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129 - `quantiles.csv`: Flux quantiles (25%, 50%, 75%)
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130 - `fluxes/`: Complete flux distributions (if requested)
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131 - `fva.csv`: FVA results (if requested)
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132 - `pfba.csv`: pFBA results (if requested)
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133 - `sensitivity.csv`: Knockout sensitivity analysis (if requested)
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134 - `*.log`: Processing log
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135
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136 ## Examples
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137
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138 ### Basic CBS Sampling
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139
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140 ```bash
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141 flux_simulation -ms ENGRO2 \
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142 -in bounds/*.tsv \
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143 -ni Sample1,Sample2 \
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144 -a CBS \
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145 -ns 1000 \
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146 -idop output/
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147 ```
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148
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149 ### OPTGP Sampling
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150
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151 ```bash
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152 flux_simulation -ms ENGRO2 \
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153 -in bounds/*.tsv \
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154 -ni Sample1,Sample2 \
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155 -a OPTGP \
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156 -ns 1000 \
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157 -th 200 \
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158 -idop output/
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159 ```
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160
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161 ### Custom Model with CBS Sampling
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162
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163 ```bash
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164 flux_simulation -ms Custom \
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165 -mo custom_model.xml \
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166 -in bounds/*.tsv \
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167 -ni Sample1 \
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168 -a CBS \
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169 -ns 2000 \
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170 -idop output/
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171 ```
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172
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173 ## Troubleshooting
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174
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175 | Error | Solution |
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176 |-------|----------|
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177 | "GLPK solver failed" | Install GLPK libraries |
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178 | "Model infeasible" | Check bounds constraints |
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179
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180 ## See Also
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181
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182 - [RAS to Bounds](tools/ras-to-bounds)
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183 - [Flux to Map](tools/flux-to-map)
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184 - [Built-in Models](reference/built-in-models)
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