https://github.com/stelmo/differentiablemetabolismcode

https://github.com/stelmo/differentiablemetabolismcode

Last synced: 12 days ago
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• Host: GitHub
• URL: https://github.com/stelmo/differentiablemetabolismcode
• Owner: stelmo
• License: apache-2.0
• Created: 2022-05-24T09:03:34.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2022-12-26T17:49:24.000Z (almost 2 years ago)
• Last Synced: 2024-07-30T18:13:18.689Z (5 months ago)
• Language: Julia
• Size: 574 MB
• Stars: 1
• Watchers: 4
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md
  • Citation: CITATION.bib

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README

        
# DifferentiableMetabolismCode

 

The code in this repo can be used to reproduce all of the results in the [associated paper](https://www.sciencedirect.com/science/article/pii/S1096717622001173)

Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models

(preprint [here](https://www.biorxiv.org/content/10.1101/2022.07.11.499575v1)).

There are three main results in the paper:

1. Sensitivity analysis of an enzyme constrained GECKO model

2. Gradient descent on measurements to fit enzyme turnover numbers

3. Sensitivity analysis on a model that incorporates full Michaelis-Menten kinetics

Note, the Julia environment used for the analyses can be recreated by activating

the `Project.toml` file, as described

[here](https://pkgdocs.julialang.org/v1/environments/). The directory `data`

contains all the data (turnover numbers, Michaelis constants, experimental data,

etc.), as well as the scripts used to parse them into the format used in this

work. The directory `model_construction` contains scripts used to download the

metabolic models, as well as add stoichiometric data for enzyme subunits, enzyme

turnover numbers, and enzyme masses to these models (called `fixed_models` in the 

context of this work).

If anything is unclear, please file an issue on this repo.

## 1. Sensitivity analysis of a GECKO model 

The directory `analyses/gecko` contains all the scripts used to generate the

associated results. In particular, the script `gecko_iml1515.jl`

can be used to run the sensitivity analysis of the flux and concentration

predictions to enzyme turnover numbers. Note, it will attempt to save the images

created in the script to another folder not contained in this repo (the repo

used to write the paper), so comment those image file saving lines out.  

## 2. Gradient descent to fit turnover numbers 

The directory `analyses/gd_gecko` contains all the scripts used to generate the

associated results. The script `cluster.jl` can be used to run the gradient

descent algorithm for a specific `master_id`, which is the ID of the respective

experimental data. Valid IDs are taken from the data source (see the paper), but

include `WT1#B1`, `WT2#B2`etc.

## 3. Sensitivity analysis of a Michaelis-Menten constrained model

The directory `analyses/crispr` contains all the scripts used to generate the

associated results. In particular, the file `michaelis_menten.jl` can be used to

generate the results, and `plot_mm_all.jl` will plot all the sensitivities. In

contrast, `plot_mm_few.jl` can be used to plot a representative selection of the

results (which is easier to  inspect).