Project for the translation of SBML files into BNGL files. You can find more information about the project here.

Download Latest Test Builds

These builds are the from the head of master and are not guaranteed to be stable. Use at your own risk.

• Linux
• OSX
• Windows x64

Requirements

You should use Python 3.5 or later. A number of Python libraries need to be installed, but these can be automatically installed in a Python virtual environment, which is exactly what the makefile does under Linux and OSX or what the PowerShell script does for Windows.

Ubuntu

sudo apt-get install -y python3-dev
sudo apt-get install -y python3-venv

Windows 10

You'll need to install Python 3. We recommend using Anaconda for this.

Optional for Developers

If you want to run ./atomizer/SBMLparser/sbmlTranslator.py directly, you can install all the Python requirements at the system level by doing:

cd SBMLparser
sudo pip3 install -r requirements.txt

Alternatively, you can do this at the user level:

cd SBMLparser
pip3 install --user -r requirements.txt

Installation

OSX and Linux

From the top-level directory, type the following:

make; make install

This will create an sbmlTranslator binary in ./atomizer/bin. This is made using PyInstaller.

Installation for Windows

Right click on ./atomizer/build_sbmlTranslator_win.ps1 and select Run with PowerShell.

Execution

./sbmlTranslator -i /path/to/sbml/file [-a] [-o output.bngl]

Optional arguments

• [-a] activates the atomizer. Otherwise only a flat translation will be provided (no molecular structure)
• [-p] activates pathway commons querying. An internet connection is required
• [-b <path/to/bionetgen>] Enables post atomization analysis.
• See [-h] for a full list of arguments

Directory Structure:

• SBMLparser: Directory containing the main project.

  • sbmlTranslator.py: Entry level file

  • atomizer: Contains the main implicit assumption extraction code

    • analyzeSBML.py: lexical analysis engine
    • detectOntology.py: helper file for the lexical analysis engine (levenshtein distance, convention discovery, etc.)
    • analyzeRDF.py: extracts annotation information and gets it into the atomizer data model
    • resolveSCT.py: Creates the species composition table using lexical analysis, annotation information and the stoichiometry matrix
    • moleculeCreation.py: Creates the structured molecules for use in graph-creation

  • utils: contains several utility scripts used during the atomization process

    • consoleCommands.py: Interfaces with the BioNetGen console
    • pathwaysCommons.py: Queries BioGrid and pathways commons given an RDF annotation entry
• stats: Extracts statistics of a set of atomized BioModels files