COMBINE_reproducible_biomodeling_workflows

COMBINE_reproducible_biomodeling_workflows

All materials associated with the tutorial for the International Conference on Systems Biology (ICSB) and COMBINE 2022 events entitled, "Creating reproducible biochemical modeling workflows", are contained in this repository.
Explore the docs »

View Demo · Report Bug · Request Feature

Table of Contents

1. About The Project
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Contributing
5. License
6. Contact

About The Project

While scientific disciplines revere reproducibility, many studies - experimental and computational alike - fall short of this ideal and cannot be reproduced or even repeated when the model is shared. For computational modeling of biochemical networks, there is a dearth of formal training and resources available describing how to practically implement reproducible methods, despite a wealth of existing tools and formats which could be used to support reproducibility. This chapter points the reader to useful software tools and standardized formats that support reproducible modeling of biochemical networks and provides suggestions on how to implement reproducible methods in practice. Many of the suggestions encourage readers to use best practices from the software development community in order to automate, test, and version control their model components. A Jupyter Notebook demonstrating several of the key steps in building a reproducible biochemical network model is included to supplement the recommendations in the text.

You can also try out the study on Google Colab: https://colab.research.google.com/drive/1wddLftHNhetbozZY29r2HRkzQLl1F_fs?usp=sharing

Getting Started

To get a local copy up and running follow these simple steps.

Prerequisites

All software dependencies required to run the code contained in this repository using the same versions employed during development.

• Python 3.8.2
• pip packages

  pip install bioservices==1.7.11

  pip install tellurium==2.1.6

  pip install matplotlib==3.3.4

  pip install IPython==7.12.0

  pip install libsbgnpy

  pip install sbmllint

  pip install sbmlutils~=0.4.9

  pip install phrasedml~=1.1.1

  pip install h5py~=3.1.0

  pip install pandas~=1.2.2

  pip install seaborn~=0.11.0

  pip install scikit-learn~=0.24.1

  pip install lmfit~=1.0.1

Installation

Clone the repo

git clone https://github.com/vporubsky/COMBINE_reproducible_biomodeling_workflows.git

Usage

This is intended as an introduction to reproducible biochemical modeling in Python.

Data Aggregation

Documentation, Version Control, and Annotation

Simulation

Parameter Estimation

Verification and Validation

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Veronica Porubksy - verosky@uw.edu

Project Link: https://github.com/vporubsky/COMBINE_reproducible_biomodeling_workflows