PySCeS provides a variety of tools for the analysis of cellular systems

• A human readable Model Description Language used to define models.
• The full power of Python available for you to test, build and share your modelling experiments.
• Follow a models evolution over time with solvers like LSODA. Need a more flexible/powerful solver easily extend PySCeS functionality by using the industry standard CVODE (via PySUNDIALS)
• PySCeS includes a selection of non-linear root-finding algorithms that can quickly and efficiently be used to calculate steady state solutions (e.g. HYBRD, NLEQ2).
• Investigate the control and regulation of cellular systems with a completer Metabolic Control Analysis (MCA) module that evaluates elasticities, flux and concentration control coeffcients and response coefficients.
• A structural analysis module for determination of nullspaces and reduced stoichiometric matrix for models up to the genome scale.
• A bifurcation analysis module for the study of systems which exhibit multiple (stable and unstable) steady state solutions (PITCON).
• User friendly methods for generating and 1,2 and n dimension parameter scans.
• Visualise the results of simulations with a unifed, flexible plotting interface that allows for the use of multiple backends e.g. Matplotlib and/or Gnuplot.
• SBML import and export capability is provided by an interface module that converts SBML into the PySCeS MDL and models into SBML.
• User friendly documentation available as docstrings, HTML and PDF.
• PySCeS is developed as Open Source software distributed under its own BSD style licence.
• NEW: try out the new and continuously developing PySCeS and PySCeS-CBM wiki!

PySCeS in use

• S.M. Kelk et al (2012) Optimal flux spaces of genome-scale stoichiometric models are determined by a few subnetworks Scientific Reports
• F. Achcar et al (2012) Dynamic Modelling under Uncertainty: The Case of Trypanosoma brucei Energy Metabolism. PLOS Computational Biology
• J.M. Rohwer (2012) Kinetic modelling of plant metabolic pathways. Journal of Experimental Botany
• W.B. Copeland et al (2012) Computational tools for metabolic engineering. Metabolic Engineering
• P. Thomas et al (2012) Intrinsic Noise Analyzer: A Software Package for the Exploration of Stochastic Biochemical Kinetics. PLOS One

I                            Interested in finding the latest version of PySCeS try the download section.