cowmunity1.0

Cowmunity Model: Community Metabolic Modeling with Molecular Docking Integration

Overview

The Cowmunity Model is a community metabolic model that simulates a three-species rumen microbial community using the OptCom (Optimal Community) bilevel optimization framework. The model integrates molecular docking predictions to mechanistically predict how small molecule treatments affect methane production in the rumen microbiome.

What We've Done

1. Community Metabolic Modeling: Built a bilevel optimization model that simulates three rumen bacteria:

   • Methanobrevibacter gottschalkii (MGK): Methanogen that produces methane
   • Prevotella ruminicola (PRM): Fibrolytic bacterium
   • Ruminococcus flavefaciens (RFL): Fibrolytic bacterium

2. Molecular Docking Integration: Integrated structure-based molecular docking predictions directly into the metabolic model by:

   • Converting binding affinities (Kd) to inhibition factors
   • Accounting for drug-enzyme binding saturation
   • Applying relaxation factors to bridge in silico predictions and in vivo reality
   • Mapping docking results to metabolic reactions

3. Combined Approach: Simultaneously applies:

   • Direct enzyme effects from docking (structure-based predictions)
   • Indirect community effects from literature (community interactions, substrate availability)

4. Metabolite Exchange: Models inter-species metabolite sharing (H₂, CO₂, formate, amino acids, etc.) to create a true community model rather than three separate models.

Installation

Prerequisites

• Python 3.7 or higher
• GAMSpy Academic License (free for academic use)

Step 1: Install Python Packages

pip install pandas python-libsbml gamspy

Step 2: Register for GAMSpy License

1. Register for a free academic license at: https://academic.gams.com/
2. Install your license:

gamspy install license <your-gamspy-license>

Running the Model

Basic Usage

1. Navigate to the project directory:

cd CowmunityModel

2. Run the main script:

python main.py

3. Select a treatment option when prompted:
   • 0: No treatment (baseline)
   • 1: Imidazole
   • 2: L-Carnitine
   • 3: Methyl Jasmonate
   • 4: Propylpyrazine

What Happens When You Run

The model will:

1. Load and process the three SBML metabolic models
2. Set up community exchange constraints
3. Apply treatment-specific constraints (docking-based + literature-based)
4. Solve the bilevel optimization problem
5. Save results to results/ directory
6. Display key outputs (biomass, methane flux)

Output

Results are saved in results/variable_methane_{treatment}_treatment/:

• mgk_records.csv - All reaction fluxes for M. Gottschalkii
• prm_records.csv - All reaction fluxes for P. ruminicola
• rfl_records.csv - All reaction fluxes for R. flavefaciens

Project Structure

CowmunityModel/
├── main.py                          # Main entry point
├── Cowmunity.py                     # Core model implementation
├── cow.txt                          # ASCII art (optional)
├── model files/                     # SBML metabolic models
│   ├── M. gottschalkii.xml
│   ├── P. ruminicola.xml
│   └── R. flavefaciens.xml
├── docking_integration/              # Docking integration module
│   ├── apply_docking_constraints.py
│   ├── parse_docking_results.py
│   └── enzyme_reaction_mapper.py
└── docking_data/                     # Docking results
    └── cleaned_docking_results.csv

Key Features

• Bilevel Optimization: Outer problem maximizes community biomass; inner problems optimize individual species
• Structure-Based Predictions: Uses molecular docking data to predict enzyme inhibition
• Community Interactions: Models metabolite exchange between species
• Combined Constraints: Integrates docking predictions with literature-based effects
• Flexible Treatments: Test multiple small molecule treatments

Dependencies

• pandas - Data manipulation
• python-libsbml - SBML model parsing
• gamspy - Optimization framework (requires academic license)

litreture model

• To access litreture based model, simply use the model with litreture name on it.

Model Units

• Fluxes: mmol/gDCW·hr (millimoles per gram dry cell weight per hour)
• Biomass: gDCW/gDCW·hr (grams dry cell weight per gram dry cell weight per hour)
• Methane: mmol/gDCW·hr (can be converted to ml/gDCW·hr)

Citation

If you use this model, please cite us:

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Authors

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Troubleshooting

GAMSpy License Issues

• Ensure you have registered for an academic license
• Check that your license is properly installed: gamspy license status

Missing Files

• Ensure all SBML model files are in the model files/ directory
• Check that docking_data/cleaned_docking_results.csv exists for docking-based treatments

Solver Issues

• Default solver is IPOPT. If issues occur, ensure IPOPT is properly installed with GAMSpy
• Model solve time is limited to 20 seconds by default (can be modified in Cowmunity.py)

Contact

For questions or issues, please contact [email protected]