Tango

TANGO is a project using numerical-based strategy reconciling multi-omics data and metabolic networks for characterising bacterial fermentation in cheese production composed of 3 species : P. freudenreichii, L. lactis and L. plantarum.

Please read the research paper for details about the models and the global modeling strategy.

Install

The script have been used under a Conda virtual environment. To install Conda on your computer, please visit the Conda installation webpage. Once installed, in a terminal, run

conda env create -f environment.yml 

Overall strategy of the Tango models

• Get metabolic models of the 3 species, and refine them to be consistent with biological knowledge. The refined models are provided in the folder 'metabolic_models'.
• Calibrate all individual model based on growth, pH and dosage time series obtained on individual growth culture in milk.
• Assemble individual model into a community model, the output of which can be compared to community growth culture.

Run simulations

Simulations can be run with the command line interface.

running individual model optimization

By setting the option -optim to True, an optimization is launched. The optimization parameters can be set in the configuration file in 'pipeline/config_file/config_optim.yml'. Here, the freudenreichii model is optimized. By changing the model name in the command line, the model used for optimization can be changed.

python python -m src.tango_models sim -mp metabolic_models/freudenreichii.sbml -cp pipeline/config_file/config_culture.yml -dp pipeline/config_file/config_dynamic.yml -sp pipeline/config_file/config_optim.yml -com False -optim True -CobraSolver glpk

running individual model

An individual model is launched by setting the option -optim to False. The individual model dynamical parameters can be set in the configuration file in 'pipeline/config_file/config_dynamic.yml'. Common parameter to individual and culture growth experiments are defined in the 'pipeline/config_file/config_culture.yml'. By changing the model name in the command line, the model used for the simulation can be changed.

python -m src.tango_models sim sim -mp metabolic_models/freudenreichii.sbml -cp pipeline/config_file/config_culture.yml -dp pipeline/config_file/config_dynamic.yml -com False -optim False -CobraSolver glpk

python -m src.tango_models sim sim -mp metabolic_models/lactis.sbml -cp pipeline/config_file/config_culture.yml -dp pipeline/config_file/config_dynamic.yml -com False -optim False -CobraSolver glpk

python -m src.tango_models sim sim -mp metabolic_models/plantarum.sbml -cp pipeline/config_file/config_culture.yml -dp pipeline/config_file/config_dynamic.yml -com False -optim False -CobraSolver glpk

For Freudenreichii, another experiment with different initial condition has been done for metabolite dosage. This experiment can be model with the optiono -fsim.

python -m src.tango_models sim sim -mp metabolic_models/freudenreichii.sbml -cp pipeline/config_file/config_culture.yml -dp pipeline/config_file/config_dynamic.yml -com False -optim False -CobraSolver glpk -fsim metabolites

running community models

For running dFBA in community, set -com True, and specify the list of models in the community. The community model dynamical parameters can be set in the configuration file in 'pipeline/config_file/config_dynamic_com.yml'.

python -m src.tango_models sim sim -mp metabolic_models/freudenreichii.sbml metabolic_models/lactis.sbml metabolic_models/plantarum.sbml -cp pipeline/config_file/config_culture.yml -dp pipeline/config_file/config_dynamic_com.yml -sp pipeline/config_file/config_optim.yml -com True -optim False -CobraSolver glpk

plot simulations

Several plotting tools are available.

python src/plot_indiv.py
python src/plot_com.py
python src/plot_flux.py
python src/plot_goodness_of_fit.py
python src/plot_switch_pathways.py
python src/plot_transcripts.py

How to cite

Please cite :

• Growth and pH data used in TANGO : Cao, W., Aubert, J., Maillard, M. B., Boissel, F., Leduc, A., Thomas, J. L., Deutsch, S. M., Camier, B., Kerjouh, A., Parayre, S., Harel-Oger, M., Garric, G., Thierry, A., & Falentin, H. (2021). Fine-Tuning of Process Parameters Modulates Specific Metabolic Bacterial Activities and Aroma Compound Production in Semi-Hard Cheese. Journal of Agricultural and Food Chemistry, 69(30), 8511–8529. https://doi.org/10.1021/acs.jafc.1c01634
• Genomic data : ENA: PRJEB42478
• Metatranscriptomic PUT Link Data Gouv
• Tango Paper : Deciphering bacterial community dynamics and metabolic interactions in cheese through multi-omic data integration