Prof Darren Wilkinson Professor of Stochastic Modelling
School of Mathematics, Statistics and Physics
Newcastle University

Software / SBML-shorthand and

This page describes a "shorthand" version of SBML that is much easier to read and write by hand than real SBML, together with a software tool that translates the shorthand model description to real SBML.

It isn't really meant as a complete alternative to GUI model-building tools. It is just a useful tool for rapid building of essential model structure. Once the basics are defined, you can translate to SBML and load up in the GUI tool of your choice.


The following is a complete description of a simple enzyme-kinetics model using the shorthand notation.
@model:2.3.1=MichaelisMentenKinetics "Michaelis-Menten Kinetics"
@rr=SubstrateEnzymeBinding "Substrate-enzyme binding"
 Substrate+Enzyme -> Complex
 Complex -> Product + Enzyme
 k2*Complex : k2=3
You can download this example: mm.mod. Using the translation tool below, this model translates to this SBML.

Latest SBML-shorthand specification

All previous versions are available. model translation tool

Note that this translator is written in Python, so you need a working Python environment including libSBML version >= 5.0.0 and the libSBML Python bindings. See the libSBML website for information on installing the libSBML python bindings, but note that on many Linux and similar systems it can be as simple as installing the python-libsbml package with the default OS package manager.

Obviously, as the shorthand specification does not contain all SBML features, there is likely to be loss involved. However, there should be no semantic loss in the round-trip SBML-sh -> SBML -> SBML-sh. ie. doing a conversion followed by a conversion should get back to a shorthand file that is semantically equivalent to the original. works very similarly to, so no separate documentation should be necessary.

Source code and development

This software is now developed on GitHub in the sbml-sh repository.

Darren J Wilkinson Book: Stochastic Modelling for
	      Systems Biology

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