Readme.txt

yETFL
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To construct ETFL model for the yeast, Saccharomyces cerevisiae.
 
Paper: Omid Oftadeh, Pierre Salvy, Maria Masid, Maxime Curvat, Ljubisa Miskovic and Vassily Hatzimanikatis. A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics

Requirements
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For this package the following packages are required:
- cobra (https://opencobra.github.io/cobrapy/)
- pytfa (https://github.com/EPFL-LCSB/pytfa)
- etfl (https://github.com/EPFL-LCSB/etfl)

The code can be run on different types of OS including UNIX and Windows.

You will need Git LFS to properly download some binary files:

git lfs install
git lfs pull

We recommend the use of Docker to set up a container instead of running the codes directly. To solve the genome-scale models, a licensed commercial solver, either Gurobi or CPLEX, should be installed. The free academic versions can be obtained through Gurobi for Acedemics and Researchers (https://www.gurobi.com/academia/academic-program-and-licenses/) or IBM Academic initiative (https://developer.ibm.com/academic/). Alternatively, to test the general performance of the code, the scripts in tests/ subdirectory can be run with the free solver GLPK. For further details, see README.rst.

Data
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Yeast-specific data should be located in the repository file. This can be downloaded from https://zenodo.org/record/4778047#.YKfF8OvRY6U and then, manually located in the repository. Alternatively, the fetch_data_zenodo can be run to do it automatically. For this, run in terminal:
./fetch_data_zenodo

You might need to enter the password for your system to permit the code to unzip the downloaded files.


Generating yETFL models
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To create the yETFL models, run the script helper_gen_models_yeast.py. The output will be saved automatically in json format.

Analysing the models
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To benchmark the growth rate at different uptake rates, run the script benchmark_yeast_models.py. The output will be automatically saved for different uptake rates in a csv file.
To simulate the Crabtree effect, run the script crabtree_cheby_parsim.py. The output will be automatically saved for different uptake rates in a csv file.
To do gene essentiality, run the script gene_essentiality.py. The output will be automatically saved for different genes in a csv file.
 
The run time for the simulations might be different based on the system performance. However, the run time is typically in the order of ~hours.