BioRECIPE

BioRECIPE (Biological system Representation for Evaluation, Curation, Interoperability, Preserving, and Execution)

The BioRECIPE (Biological system Representation for Evaluation, Curation, Interoperability, Preserving, and Execution) knowledge representation format was introduced to facilitate seamless human-machine interaction while creating, verifying, evaluating, curating, and expanding executable models of intra- and intercellular signaling. This format allows a human user to easily preview and modify any model component, while it is at the same time readable by machines and can be processed by a suite of model development and analysis tools. The BioRECIPE format is compatible with multiple representation formats, natural language processing and modeling tools, and databases that are used by the systems biology community.

Contents

• Features
• Two Formats
• Translations
• Workflow Guide
• Package Structure
• Citation
• Funding
• Support

Features

BioRECIPE supports the features of Standardization, Expressiveness, Scalability, Extensibility, Machine-readability, Human-readability, Support for quantitative data, Annotations and metadata, Compatibility, Collaborative Facilitation, Version control and evolution.

Two Formats

Translations

1. Clone the BioRECIPE repository to your computer.

   git clone https://github.com/pitt-miskov-zivanov-lab/BioRECIPE.git
   

2. Navigate into the directory, install BioRECIPE and its python dependencies.

   cd BioRECIPE
   pip install -e .
   

3. Run the provided notebook (Check Jupyter notebook installation here).

   jupyter notebook examples/use_translators.ipynb
   

Workflow Guide

Package Structure

• setup.py: python file that help set up python dependencies installation and non-python package building
• docs/: containing files supporting the repo's host on Read the Docs
• LICENSE.txt: MIT License
• README.md: it's me!

Citation

• Holtzapple, Emilee, et al. “The BioRECIPE Knowledge Representation Format,” bioRxiv, 2024.

• Sayed, Khaled, et al., “Recipes for translating big data machine reading to executable cellular signaling models,” Machine Learning, Optimization, and Big Data: Third International Conference, MOD 2017, Volterra, Italy, September 14–17, 2017, Revised Selected Papers 3. Springer International Publishing, 2018.

Funding

This work was funded in part by a DARPA Big Mechanism award AIMCancer (W911NF-17-1-0135) and in part by the NSF EAGER award CCF-2324742

Support

For translation and workflow concerns, feel free to reach out to Natasa Miskov-Zivanov: nmzivanov@pitt.edu