1 - 46 of 46 results


Allows to build, visualize and model cellular signal transduction networks. The rxncon language enables the description of these networks. The rxncon software tool interprets the rxncon language and automates export into a range of graphical and mathematical formats. In the rxncon framework, cellular signal transduction networks are described at the same granularity as empirical data. The user defines the network as one reaction list and one contingency list. From these data mathematical and graphical representation can be generated. The network can be modified and extended iteratively, and both visualization and mathematical models can be generated automatically at any time.


Allows merging metabolic and signaling pathways reported in the Kyoto Encyclopaedia of Genes and Genomes (KEGG). MetaboSignal is a network-based approach designed to navigate through topological relationships between genes (signaling- or metabolic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape of metabolic phenotypes. This approach is ideally suited to identify candidate genes in metabotype-Quantitative-Trait Locus (QTL) or to identify biological pathways affected in transgenic models. This approach is ideally suited to identify candidate genes in metabotype-QTL studies (e.g. trans-acting associations), or to identify biological pathways affected in transgenic models.


Identifies long noncoding RNAs (lncRNAs) competitively regulated signal subpathways (LRSP) underlying certain condition. Subpathway-LNCE is an R package that integrates lncRNA- messenger RNA (mRNA) expression profile and pathway topologies. The software can be useful for exploring the regulation function of lncRNAs in human disease. It was evaluated using prostate cancer data sets and KIRC data set and tested by randomly disturbing matched expression profiles and LRSP.


Enables annotation of molecular events including protein–protein interactions (PPIs), enzyme–substrate relationships and protein translocation events either manually or through automated importing of data from other databases. PathBuilder is an open-source web application to annotate biological information pertaining to signaling pathways and to create web-based pathway resources. It includes automatic validation of data formats, built-in modules for visualization of pathways, automated import of data from other pathway resources, export of data in several standard data exchange formats and an application programming interface for retrieving existing pathway datasets.


A network biology-based computational platform designed to integrate transcriptomes, interactomes and gene ontologies to identify phenotype-specific subnetworks. NetDecoder is based on network flow algorithm and formulated as a minimum-cost flow optimization problem to identify and prioritize paths and key regulators within disease specific subnetworks. NetDecoder is designed to capture molecular switches and infer disease-specific networks to better understand pathways and identify key regulators that contribute to a disease phenotype.

SiGNet / Signal Generator for Networks

Enables researchers to create realistic, bespoke benchmarking datasets for the evaluation of signaling network inference. SiGNet users can specify the type and strength of interactions within a signaling network of their own design. This tool includes options for inhibiting or activating nodes, and mimicking experimental perturbation. Serves to reproduce data from real experiments, its simulations are highly accurate, with correlations between real and simulated data.


Provides a simulation software for continuous time Boolean modeling. MaBoSS has been developed to answer specific needs of modelers. It can be combined with its associated environment and used into the following pipeline: (i) construct a logical model, by writing directly the .bnd and .cfg files or by exporting them from ginsim; (ii) run the model, analyze the results within a spreadsheet, and visualize the results; (iii) test parameter sensitivity; (iv) confront the model to known experimental observations (mutations, drug effects), and (v) set up a protocol for comparing experiments to model results for new biological insights.


Permits to interrogate and train signaling networks based on measurements from stimulus-response experiments. sigNetTrainer is based on Integer Linear Programming (ILP) to predict the possible changes of the activation levels of the involved players for a given experiment. It was used to interrogate and (re-)train a manually curated Interaction Graphs (IG) model of EGFR/ErbB signaling against a library of high-throughput phosphoproteomic data measured in primary human hepatocytes.


An information resource about intercellular regulatory communication. EndoNet provides information about hormones, hormone receptors, the sources (i.e. cells, tissues and organs) where the hormones are synthesized and secreted, and where the respective receptors are expressed. The database focuses on the regulatory relations between them. An elementary communication is displayed as a causal link from a cell that secretes a particular hormone to those cells which express the corresponding hormone receptor and respond to the hormone. Whenever expression, synthesis and/or secretion of another hormone are part of this response, it renders the corresponding cell an internal node of the resulting network. This intercellular communication network coordinates the function of different organs. Therefore, the database covers the hierarchy of cellular organization of tissues and organs as it has been modeled in the Cytomer ontology, which has now been directly embedded into EndoNet. The user can query the database; the results can be used to visualize the intercellular information flow.

Worm Single Cell

Infers signalling networks at cellular level for cell fate specification based on multiple data types, including single-cell gene expression data, PPI data, PDI data and Genetic Interaction (GI) data. Worm Single Cell is a framework that works by steps: (i) single-cell gene expression data both in wild-type and mutant are preprocessed and used to infer knockdown genes’ effects at cellular level; (ii) the inferred knockdown genes’ effects along with PDI and GI data are formatted as cause-effect pairs to be subsequently used in pathway inference; (iii) candidate paths are selected in a background network constructed by the PPI and PDI data; (iv) the candidate paths and the cause-effect pairs are utilized to infer pathways with sign and direction for different founder cells; (v) by integrating the inferred pathways, signaling networks at cellular level are reconstructed.


A network-based approach that characterizes known targets in signaling networks using topological features. TENET first computes a set of topological features and then leverages a SVM-based approach to identify predictive topological features that characterizes known targets. A characterization model is generated and it specifies which topological features are important for discriminating the targets and how these features should be combined to quantify the likelihood of a node being a target.


A web tool for the interpretation of the consequences of the combined changes in expression levels of genes in the context of signaling pathways. Specifically, this tool allows the user to identify the stimulus-response subpathways that are significantly activated or deactivated in the typical case/control experiment. PATHiWAYS identifies all the stimulus-response subpathways of KEGG signaling pathways, calculates the probability of activation of each one, based on the individual gene expression values and identifies those with a significant differential activity between the two conditions compared.


Allows users to quickly visualize their data through these analyses: gene analysis, gene ontology, pathway analysis, drug interactions, over-represented diseases and much more. Unlike other pathway analysis applications that assume all genes to be independent, iPathwayGuide considers the size, role, and position of each gene on the pathway as it models high-throughput sequencing data. This advanced approach allows users to quickly prioritize targets and pathways, avoiding false positive and false negative results. iPathwayGuide is easy to use, easy to interpret, and fits all budgets.

ISP / Insulin signalling pathway

A comprehensive model of the insulin signalling pathway, which integrates three models previously available in the literature by using the rule-based modelling (RBM) approach. RBM allows for a simple description of a number of signalling pathway characteristics, such as the phosphorylation of signalling proteins at multiple sites with different effects, the simultaneous interaction of many molecules of the signalling pathways with several binding partners, and the information about subcellular localization where reactions take place. Thanks to its modularity, it also allows an easy integration of different pathways.


A method to infer isoform-specific co-expression networks from exon-level RNA-Seq data, using large dimensional trace. SpliceNet goes beyond differentially expressed genes and infers splicing isoform network changes between normal and diseased samples. It eases the sample size bottleneck; evaluations on simulated data and lung cancer-specific ERBB2 and MAPK signaling pathways, with varying number of samples, evince the merit in handling high exon to sample size ratio datasets.


Provides detailed information about the latest version of the KEGG pathway databases. KEGG.db is an R package that permits access to a set of annotation maps for KEGG assembled using data from the KEGG database. It offers access to annotations data object that maps (i) Enzyme Commission numbers to Gene Ontology (GO) identifiers, (ii) Entrez Gene or Open Reading Frame (ORF) identifiers KEGG pathway identifiers, (iii) GO identifiers to Enzyme Commission numbers and other functions.


Examines signaling networks in the context of tissue gene expression patterns. The tissue gene expression data available through NetAtlas consists of 79 human tissues, 61 mouse tissues, and 44 combined tissues from 3 rat strains. The NetAtlas plugin allows the creation of tissue-defined signaling networks by identifying which components are expressed in particular tissues, which components show tissue-specific expression, and which components within the network are coordinately expressed across tissues.

MT-SDREM / Multi-Task Signaling and Dynamic Regulatory Events Miner

Reconstructs response pathways and temporal regulatory networks. MT-SDREM can capitalize on the many dimensions in complex systems biology datasets by integrating different types of experimental data in each condition. It iterates between finding pathways that connect the upstream proteins that directly interact with an external stimulus and the downstream transcription factors (TFs) that regulate the response and learns dynamic regulatory networks activated by these TFs. It can be used to gain insights into characterizing the human response to viral infection.


Uses the method of Latent Variables to turn differential high-throughput expression data and a functional network into a list of active signaling pathways. CellFateScout is a network-based bioinformatics tool and Cytoscape plugin that explores an expression signature in a network context and then suggests the repositioning of small molecules to emulate the given signature. It can be used to select small molecules for their desired effects. The CellFateScout user interface is implemented in a way that it is easy to grasp for non-computer scientists, but still functional and comprehensive.

Sigmoid / SIGnal MOdeling Interface and Database

Simplifies global modeling of biological systems. Sigmoid supports the process of cycling between model building, hypothesis generation, biological experimentation, and data gathering. It collects the hypothesis and discovery phases of the research process. The purpose of this tool is to provide researchers an useful method to create and explore predictive dynamical models of complex biological systems such as metabolic, gene regulation, or signal transduction pathways in living cells.


An application for the development and analysis of complex mathematical models with emphasis on models of gene signaling pathways. Solvary includes a method that allows defining some of the model variables as deterministic and some as stochastic, reflecting the heterogenic nature of cellular response to external stimuli. It can custom mathematical equation parser that supports various mathematical operators and conditional statements. It has ability to conduct stochastic and deterministic simulations for models build of any number of equations and parameters.


Allows analysis of high-throughput experimental data. MADNet is a data mining and visualization web server that integrates experimental results with the existing biological data in the context of metabolic and signaling pathways, transcription factors and drug targets. The software provides a systems biology approach to complex research problems in a user-friendly interface. It is not only confined to microarray experiments, but can also be used to analyze expression information from different experimental techniques.

PeTTSy / Perturbation Theory Toolbox for Systems

Offers a toolkit for handling large and complex biological models. PeTTSy provides a graphical user interface that allows users to apply a wide range of techniques for sensitivity analysis and running simulations. The platform generates a targeted model that can be used for performing time series analysis, principal component analysis or a user-defined analysis. Results can be saved and returned as plots with separates variables or 3D plots.