Vol. 22 no. 5 2006, pages 628–629
doi:10.1093/bioinformatics/btk042BIOINFORMATICS APPLICATIONS NOTE
Systems biology
Tools for the SBML Community
Colin S. Gillespie1,
, Darren J. Wilkinson1, Carole J. Proctor2, Daryl P. Shanley2,
Richard J. Boys1 and Thomas B. L. Kirkwood2
1School of Mathematics and Statistics, University of Newcastle, Newcastle upon Tyne, NE1 7RU, UK and
2Henry Wellcome Laboratory for Biogerontology Research, School of Clinical and Medical Sciences,
Gerontology, University of Newcastle, Newcastle upon Tyne, NE4 6BE, UK
Received on September 22, 2005; revised on December 7, 2005; accepted on January 4, 2006
Advance Access publication January 12, 2006
Associate Editor: Thomas Lengauer
ABSTRACT
Motivation: SBML is quickly becoming the standard format to
exchange biochemical models. The tools presented in this paper are
loosely-coupled, and are intended to be incorporated into SBML
aware applications. The rationale for this is to reduce the amount
of repeated work carried out within the community and to create
tools that offer a greater number of features to the end-user.
Availability:All toolsdescribedareavailable fromhttp://www.basis.ncl.
ac.uk/software and are licensed under GNU General Public License.
Contact: c.gillespie@ncl.ac.uk
INTRODUCTION
In recent years an increasing emphasis has been placed on mod-
elling biochemical networks with the aim of informing biologists
about their complex functioning and to aid in designing experi-
mental strategies. There is no unique correct method for model-
ling; rather there are a large number of techniques and theories
which provide tools for exploring model systems. Owing to the
numerous modelling strategies available it is unlikely that any
one group can develop a set of tools to carry out all possible anal-
yses. Hence, an effort has been made to develop a standard format,
the Systems Biology Mark-up Language (SBML) (Hucka et al.,
2003), which captures all of the necessary information relating to
a model in a XML format. This allows the model to be imported into
and exported out of a variety of tools. The SBML is quickly becom-
ing the lingua franca for development and sharing models of bio-
chemical networks. Currently, there are a large number of tools
that support SBML, however they tend to be designed as largely
stand-alone, platform-specific applications.
In this paper we describe a new open source collection of mod-
elling tools that can be combined with existing applications to
increase usability. Although the tools can be used by themselves,
they are intended to be included into other programs.
TOOLS
A list of the tools provided is as follows:

A Python library (pysbml). This library provides a console
based modelling system in Python, a tool for the visualization
of an SBML model (Fig. 1), and a tool for converting an SBML
model to an HTML file. More information on both installing
and using pysbml can be found in the documentation, available
on-line at the authors’ website.

A stochastic simulator written in ANSI C (gillespie2). The algo-
rithm executes the standard Gillespie algorithm (Gillespie,
1977). A swig interface has also been provided to allow the
simulator to be imported into Python.

SBML-shorthand provides a shorthand notation for SBML that
is much easier for humans to read and write than full SBML.
The full specification for SBML-shorthand and a conversion
tool is available at the authors’ website.

Additionally, a variety of tools have been exposed as web-
services, these include model visualization (Fig. 1), validation
and conversion to an HTML document for display within a
web browser. The WSDL file for these services can be found
at http://www.basis.ncl.ac.uk/sbml.wsdl
The library pysbml is written in Python. Python is a dynamically
typed, object-oriented, interpreted programming language useful for
a broad range of tasks, ranging from file parsing to rapid develop-
ment of large applications. Compared with most programming lan-
guages, Python is easy to learn and is becoming the language of
choice for many novice programmers (who quickly discover the
power of Python for scientific computing). Owing to this, Python is
an ideal choice as a ‘glue’ language in large projects. The library can
easily be installed in the standard manner and provides access to
the simulator, visualization function and model builder.
The stochastic simulator (gillespie2) is built using the efficient
GNU scientific libraries and libSBML. It currently supports local
and global parameters, events (without delays) and assignment
rules. Although the simulator can be called from the command
line, it is envisaged that the simulator will form part of larger
tool, and has a programmers API. For example, the simulator is
being used as a component of the Biology of Ageing e-Science
Integration and Simulation system currently being developed
here at Newcastle [Kirkwood et al. (2003), and see Proctor et al.
(2005) for a biological application of this system].
The library also includes the SBML-shorthand to SBML and
SBML to SBML-shorthand Python conversion tools. These are
useful for rapidly building and editing models destined for
SBML encoding. They are particularly well-suited to building
SBML models designed for discrete stochastic simulation.
To whom correspondence should be addressed.
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We have exposed several methods as web-services. This includes
an SBML validation tool, a visualization tool and an SBML to
HTML converter. Using web-services, these tools can be accessed
in a language and platform independent manner. For example,
although the services were built using Python on Linux, they can
be called using Perl or Java on Windows. Example scripts of how
to call the services are given at the authors’ website.
APPLICATION AREAS
We do not envisage that all the tools presented in this application
note would be incorporated into any one SBML application. Rather,
we foresee users ‘picking and choosing’ the tools which they
require.
For example, pysbml compliments the PySCeS toolkit (Olivier
et al., 2005). PySCeS, also written in Python, focuses mainly on
deterministic and steady-state behaviour, with additional support
for metabolic control analysis. Since both tools are written in
Python, combining them is straightforward. Other attractive Python
toolkits are ScrumPy (http://mudshark.brookes.ac.uk/ScrumPy)
and SloppyCell (http://sloppycell.sourceforge.net). Furthermore,
pysbml can be easily incorporated into the Systems biology work-
bench (SBW) via the SBW Python API (Sauro et al., 2003).
Another tool that is freely available is ODESolver. This provides
a very fast and efficient library for solving continuous time ODEs
and is a natural companion to the stochastic simulator, gillespie2.
Since the web-services can be accessed by most platforms and
languages, possible usage is wide and varied. A simple example
would be to use standard PHP modules to dynamically validate
and visualize SBML models.
SUMMARY
We believe that our tools provide a valuable addition to the suite
of applications that are becoming available for the exploration and
understanding of biochemical networks. By providing multiple
tools in smaller packages, rather than one large package, we enable
users to ‘pick-and-choose’ according to their needs and usage. Fur-
thermore, it is becoming possible to combine multiple libraries for
modelling using SBML with little effort. However, there is still a
need for further development and research.
ACKNOWLEDGEMENTS
We thank the BBSRC, MRC, DTI and Unilever for financial
support and Jo Mathews for discussions relating to visualization
software. We also thank the anonymous referees for their helpful
comments for improving this note.
Conflict of Interest: none declared.
REFERENCES
Gillespie,D.T. (1977) Exact stochastic simulation of coupled chemical-reactions.
J. Phys. Chem., 81, 2340–2361.
Hucka,M. et al. (2003) The systems biology markup language (SBML): a medium for
representation and exchange of biochemical network models. Bioinformatics, 19,
524–531.
Kirkwood,T.B.L. et al. (2003) Towards an e-biology of ageing: integrating theory and
data. Nat. Rev. Mol. Cell Biol., 4, 243–249.
Olivier,B.G. et al. (2005) Modelling cellular systems with PySCeS. Bioinformatics,
1, 560–561.
Proctor,C.J. et al. (2005) Modelling the actions of chaperones and their role in ageing.
Mech. Ageing Dev., 126, 119–131.
Sauro,H.M. et al. (2003) Next generation simulation tools: the Systems Biology Work-
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Fig. 1. Diagram of a simple biochemical network produced by the visualization tool.
Tools for the SBML community
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