eciML1515

The process for enzyme-constrained model construction.

About

The pipeline was written and tested with Python 3.8. The core libraries essential for the pipeline including: cobra, plotly(draw figures), and related packages.

Installation

1. create ECMpy environment using conda:

$ conda create -n ECMpy python=3.8

2. install related packages using pip:

$ conda activate ECMpy
$ pip install cobra
$ pip install plotly
$ pip install -U kaleido
$ pip install nbformat
$ pip install ipykernel
$ python -m ipykernel install --user --name ECMpy --display-name "ECMpy"

Steps to reproduce the analysis in the publication

Download all data and analysis code from github (directlt download or use git clone).

$ cd /file path/project save path/
$ git clone https://github.com/tibbdc/ECMpy.git

All results can be reproduced by executing the Jupyter Python notebooks:

• 01.iML1515_modification_workflow.ipynb

  • get new iML1515 model corrected GPR relationships, reaction direction and EC number.

• 02.construct_raw_eciML1515.ipynb

  • get raw eciML1515 using machine learning data.

• 03.construct_final_eciML1515.ipynb

  • modify raw eciML1515 using enzyme usage and C13 data.

• 04.simulation.ipynb

  • simulation results involved in the article.

• 05.get_enzmodel_by_autopacmen.ipynb

  • build the GECKO and sMOMENT model of iML1515 in the article.