GlyCompute

GlyCompute is a Python package designed to generate the results found in the paper: GlyCompute: towards the automated analysis of protein N-linked glycosylation kinetics via an open-source computational framework (https://link.springer.com/article/10.1007/s00216-024-05522-3).

Table of Contents

• Features
• Installation
• Modules
• Examples
• License

Features

• Feature 1: Subgraph generation from glycosylation reaction networks (GRN)
• Feature 2: Automated assembly of kinetic model of protein N-linked glycosylation
• Feature 3: Parameter estimation approach based on Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC)

Installation

Downloading the Project

First, download the project from GitHub. You can do this by cloning the repository:

git clone https://github.com/kf120/GlyCompute_paper.git

Alternatively, you can download the project as a ZIP file and extract it.

Setting Up the Environment with Anaconda

1. Enter the Repository

cd GlyCompute_paper

2. Create and Activate the Conda Environment

conda env create -f environment.yml
conda activate glycompute_env

This command will install the package and all its dependencies.

Verifying the Installation

To verify the installation, open a Python session and try importing the package:

import glycompute
print(glycompute)

If you can import the package without any errors, the installation was successful.

Modules

glycompute.abc

Contains functions for ABC-SMC.

glycompute.graph

Contains functions for graph operations.

glycompute.model

Contains functions for model assembly.

glycompute.pathway

Contains functions for automated glycosylation pathway extraction.

glycompute.simulation

Contains functions for simulation activities.

glycompute.strategy

Contains functions for the design of a stage-wise parameter estimation strategy based on graph topology and domain knowledge.

glycompute.utils

Contains utility functions for ancillary tasks across other modules.

Examples

You can find example scripts in the case_study directory. These scripts demonstrate how to use the different functions provided by the package and can be used to reproduce the results in the paper.

Running Examples

After installing the package, you can run the example scripts directly from the command line:

Example SPF

Navigate to the case_study directory and run:

python example_SPF.py

Example MFR

Navigate to the case_study directory and run:

python example_MFR.py

Example GLW

Navigate to the case_study directory and run:

python example_GLW.py

License

This project is licensed under the MIT License - see the LICENSE file for details.