The rapid development of Systems Biology and concomitant increase in the use of various 'omics technologies to generate large amounts of data has led computer modelling and simulation to play a central role in the study and understanding of biological phenomena. Such models can range in size from “small” detailed kinetic models (e.g. can be solved as ordinary differential equations) or “large” genome scale reconstructions (that typically use constraint based optimization methods).
A variety of simulation software is available for the analysis of such models (e.g. COPASI and COBRA [1,2]). Even with the development of general purpose modelling toolkits each has evolved and specialized in its own way e.g. the use of a GUI, command line or interactive terminal. More significantly they have developed their own strengths and weaknesses with respect to algorithm selection and implementation.
Our Python Based Systems Biology Tools
PySCeS: The Python Simulator for Cellular Systems PySCeS (http://pysces.sf.net) 
PySCeS is an Open Source, cross-platform modelling workbench for ordinary differential equations developed for use as an advanced research tool. Making use of F2Py, Numpy and SciPy its scripted interface and modular design makes it easy to extend its functionality. The most recent extension being the integration of stochastic simulation capabilities provided by StochPy (http://stompy.sf.net)
FAME: The Flux Analysis and Modelling environment (http://f-a-m-e.org) 
Genome scale reconstructions are typically models of an entire cells metabolism or reaction network. These reaction networks comprising hundreds to thousands of components are generally modelled using methods typical of Operations Research e.g linear and mixed integer optimization. FAME utilizes a PHP browser based GUI that connects via SOAP web-services to PySCeS-CBM (http://pysces.sf.net/cbm), a Python based optimization toolkit providing a framework for model construction and analysis.
Current and future research
While these tools individually address many current modelling challenges there is a constant need for further development and integration that is necessary to keep abreast with the latest developments in a rapidly developing field. For example, the extension of constraint-based modelling to include the analysis of ecosystems of interacting micro-organisms and the development of strategies that combine both both constraint-based modelling strategies is one of the focuses of our research. In our experience Python is uniquely suited for the development and implementation of new modelling strategies.
- Hoops, S., S. Sahle, et al. (2006). "COPASI - a COmplex PAthway SImulator." Bioinformatics 22 (24): 3067-3074.
- Schellenberger, J., R. Que, et al. (2011). "Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0." Nat. Protocols 6 (9): 1290-1307
- Olivier, B. G., J. M. Rohwer, et al. (2005). "Modelling cellular systems with PySCeS." Bioinformatics 21 (4): 560-561.
- Boele, J., B. G. Olivier, et al. (2012). "FAME, the Flux Analysis and Modeling Environment." BMC Systems Biology 6 (1): 8.