PathBuilder is an open source software to annotate biological information pertaining to signaling pathways and, with minimal additional effort, to create web-based pathway resources. PathBuilder enables annotation of molecular events including protein-protein interactions, enzyme-substrate relationships and protein translocation events via manual or automatic methods.
LibSBMLSim is a library for simulating an SBML model which contains Ordinary Differential Equations (ODEs). LibSBMLSim provides simple command-line tool and several APIs to load an SBML model, perform numerical integration (simulate) and export its results. Both explicit and implicit methods are supported on libSBMLSim.
SBEToolbox (Systems Biology and Evolution Toolbox) is being developed in MATLAB as a menu-driven GUI software to determine various statistics of the biological network. Some of its features include (but not limited to) algorithms to create random networks (small-world, ring lattice etc..), deduce clusters in the network (MCL, mCode, clusterOne), compute various network topology measures etc…
CNApy is an open source cross-platform desktop application written in Python, which offers a state-of-the-art graphical front-end for the intuitive analysis of metabolic networks with COBRA methods.
OCSANA is a new software designed to identify and prioritize optimal and minimal combinations of interventions to disrupt the paths between source nodes and target nodes
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U900 Institut Curie – INSERM/Mines ParisTech “Bioinformatics and Computational Systems Biology of Cancer
DISTILLER (Data Integration System To Identify Links in Expression Regulation) is a data integration framework that searches for transcriptional modules by combining expression data with information on the direct interaction between a regulator and its corresponding target genes. The framework builds upon advanced itemset mining approaches that have been designed to have good scalability, efficient memory use, and a small number of user parameters. It includes a condition selection or bicluster strategy in which co-expression of genes is required in only a significant subset of the complete condition set. By including this condition selection we can apply the algorithm to large expression compendia where interesting genes are not necessarily co-expressed in all measured conditions. Our approach also makes it straightforward to include any number of data sources related to transcriptional interactions such as additional microarrays, ChIP-chip or motif data.
Syntren (Synthetic Transcriptional Regulatory Networks) is a network generator that creates synthetic transcriptional regulatory networks and produces simulated gene expression data that approximates experimental data. Network topologies are generated by selecting subnetworks from previously described regulatory networks. Interaction kinetics are modeled by equations based on Michaelis-Menten and Hill kinetics. Our results show that the statistical properties of these topologies more closely approximate those of genuine biological networks than do those of different types of random graph models. Several user-definable parameters adjust the complexity of the resulting data set with respect to the structure learning algorithms.