**Professor Pedro Mendes
Computational Systems Biology**

This implements some of COPASI's functionality through web services. The source code is available at github.

Written by Joseph Dada.

COPASI is a free and open source software for modelling, simulation and analysis of biochemical networks. It is one of the most used simulators for SBML models.

A collaborative project ongoing since 2000, together with many people at Virgina Tech and University of Heidelberg.

This is a web-based tool for running computationally intensive COPASI simulations on high-throughput and cloud-based computing pools. This includes systems based on HTCondor, SGE, the Amazon Elastic Compute Cloud and others.

Written by Ed Kent.

This is a multiscale simulator for populations of cells where each cell is described by a full ODE model. It uses COPASI for the molecular level and an agent based simulator for the cell level.

Written by Joseph Dada.

A popular biochemical pathway simulator. Gepasi is no longer maintained, you should instead use COPASI.

MEG transforms one Gepasi model into another that corresponds to many replicates of the first, useful to construct multicellular models.

Both written by Pedro Mendes

This is a toolbox to create metabolic reconstructions in SBML format. It balances the network for mass and charge and can locate reactions in subcellular compartments.

Written by Neil Swainston.

Software for partial correlation analysis for the purpose of biochemical network inference from large scale observations, such as obtained with microarrays and metabolic profiling (*Bioinformatics* paper here).

Windows version | Linux version (both 32 bit)

Written by Alberto de la Fuente.

This is an algorithm to measure the distance between two multivariate time series with different dimensions (*PLoS One* paper here). Matlab code that implements this algorithm is available here.

Written by Avraam Tapinos.

Software for multivariate analysis (eg functional genomics data). Does PCA, Partial Correlation Analysis, Correlation Analysis, Discriminant Analysis, GA-DFA, NMF, etc. Ometer is driven by a plain command line interface, which allows it to be called directly from scripts.

Written by Pedro Mendes