BioNetGen, from Los Alamos National Laboratory, generates computational models of cellular signaling networks: http://cellsignaling.lanl.gov/cgi-bin/bionetgen/index.cgi.
Copasi (Complex Pathway Simulator), from the Virginia Bioinformatics Institute, provides stochastic and deterministic simulation and steady-state analysis of biochemical pathways: http://www.copasi.org/tiki-index.php.
IdentiCS (Identification of Coding Sequences from Raw Genome Sequences), from the German Research Center for Biotechnology, reconstructs metabolic networks from unfinished genome sequences: http://genome.gbf.de/bioinformatics/index.html.
Kegg2SBML, from Keio University, converts the KEGG database to SBML format: http://sbml.org/kegg2sbml.html.
LifeDB (Database for Localization, Interaction, Functional Assays and Expression of Proteins), from the German Cancer Research Center, was designed to collect, view, and mine data from a number of sources, including expression data, localization information, proteomics data, and information from functional assays: http://www.dkfz.de/LIFEdb/.
Pathway Hunter Tool, from the University of Cologne, finds the shortest path between given metabolites in a biological network: http://www.pht.uni-koeln.de:8786/PHT/.
PaVESy (Pathway Visualization Editing System), from the Max Planck Institute of Molecular Plant Physiology, is an SQL-based relational system for editing and visualizing biological pathways: http://pavesy.mpimp-golm.mpg.de/PaVESy.htm.
PySCeS (Python Simulator for Cellular Systems), from the University of Stellenbosch, performs a stoichiometric matrix analysis, calculates the time course and steady state, and does a complete control analysis for a network of coupled reactions: http://glue.jjj.sun.ac.za/~bgoli/pysces/.
ScrumPy, from Oxford Brookes University, is a Python-based molecular modeling package: http://mudshark.brookes.ac.uk/ScrumPy/.