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Using Bayesian statistical learning to integrate temperature dependence in enzyme-constrained GEMs

Description of folders

  • code/ contains all scripts and detailed descrition can be found in code/README.md.
  • data/ contains all input data needed, including experimental and estimated thermal parameters.
  • models/ contains a list of yeast genome scale models with different settings used in this study.
  • validate_smc_abc/ contains the scripts and simulation results for the validation of SMC-ABC apporach on toy models.

Dependences

numpy                       1.15.0  
pandas                      0.23.4
scikit-learn                0.20.3
seaborn                     0.9.0
jupyter                     1.0.0
cobra                       0.15.3  
Gurobi                      8.0.0

The repository was tested with Python 3.6.7.

Hardware

Since Bayesian approach is computational expsensive, all scripts except ones for visualizaiton have to be done with a computer cluster. Those scripts have been designed for parallel computation. The SMC-ABC approach takes around 3-5 days on a compute node with 32 cores (Intel Xeon Gold 6130 CPU). All visualization scripts take sveral seconds or minutes on a normal PC.

Reproduce the figures

(1) Clone this repository.
(2) Install all required packages. This step takes at most several minutes.
(2) Download the pre-computed results from Zenodo (https://zenodo.org/record/3996543#.X0J1BNP7S3I). Download the results.tar.gz file to the current directory and uncompress with

tar -xzvf results.tar.gz

Then the figures in the manuscript can be reproduced by using jupyter notebooks

  • visualization.ipynb : Figs 2abcdefi, 3ab; 5abde; S5, S6, S7, S10, S11a
  • simulate_with_start_params.ipynb Fig S2abc
  • additional_plots.ipynb: - additional plots (Figs 2gh, S13)
  • visualize_cv.ipynb: Fig S3, S4
  • visualize_temperature_on_enzymes_posterior.ipynb: Fig 3cdef
  • visualize_chemostat_metabolic_shift.ipynb: Fig 4abc, S9
  • expdata.ipynb: Fig 5c, S11b
  • single_enzyme.ipynb: Fig S8
  • Case1.ipynb~Case5.ipynb: Fig S16-S20

Results for experimental validation of ERG1 is available as csv files:

data/OD600_different_passages_40C.csv       
data/OD600_different_passages_42C.csv

One can also recompute those results by following the introductions in code/README.md and the visualized by the above scripts.