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.
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.
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.
Identifies biomarker using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures.
Deals with two issues affecting the representation of biological processes: the arbitrariness of pathway boundaries and the need to capture knowledge at different levels of detail. PID is a growing collection of human signaling and regulatory pathways curated from peer-reviewed literature and stored in a computable format. PID can allow users to take advantage of high-throughput protein–protein interaction (PPI) data, either by allowing users to upload interaction sets to be added to the novel networks created by PID queries or by querying other data sources.
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.
Generates et simulates rule-based models of biochemical systems. BioNetGen is an open source software that can be used for modeling a wide range of processes such as cell signaling or gene regulation. The application also includes a population-based network simulator that allows users to load simulation arguments from file and to end a simulation thanks to a user-defined logical condition.
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 data-derived statistical network models for 8 human cancers. CL contains several functions for biological interpretability of the network models, such as pathway analysis, drug-target recommendations, survival network analysis; and candidate gene selection. Cancer Landscapes is also a community server that enables you to mark and comment predictions of interest, for instance to plan experiments with collaborators.
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.
Consists of a pathway analysis method based on the impact analysis, which considers the effect of signals from other pathways through “interface genes”. The algorithm combines within-pathway data with the inter-pathway interaction information.
A public tool which provides an intuitive and user-friendly framework for biological pathway analysis of human gene lists. This server integrates pathway-related annotations from several public sources (Reactome, KEGG, Biocarta, etc) making easier the understanding of gene lists of interest.
Allows analysis of phosphoproteomic data in the context of signaling pathways. PHOTON is a phosphoproteomic analysis pipeline that identifies functional proteins and reconstructs signaling pathways through integrated analysis of phosphoproteomic data and protein-protein interaction (PPI) networks. The software is applicable to the analysis of diverse datasets. It is also available as part of the Perseus software for proteomics analysis.
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 computational platform that can be used to integrate information about the connections between genes, proteins, pathways, drugs, and other biological entities. The database is used to combine various existing databases to find biological relationships between the genes of interest and to predict their interactions.
Consists in a Cytoscape plugin for detecting functional modules in integrated networks composed of multiple interaction types. CyClus3D utilizes network motifs to query a 3D spectral clustering algorithm. It can notice modules composed of multiple interaction types that reflect regulatory, signaling or compensatory pathway mechanisms in addition to the stable protein complexes found by traditional clustering algorithms.
A web app to improve the navigation of biological pathways. CellPublisher converts diagrams into fully-featured maps that live inside a browser. The main purpose of this tool is to provide an easier way to navigate with systems biology graphical notation (SBGN) compliant diagrams and share them with a wider audience. In the pathway navigation interface, the majority of screen is devoted to the interactive pathway map.
A multivariate regression model capable of using prior biological knowledge expressed as gene interaction networks to guide the search for associations between gene expression and DNA methylation signatures.
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.
Allows users to identify candidates for signaling pathways in protein interaction networks. FASPAD provides a graphical display of candidates and permits to produce an overlay of several candidates or to show the surrounding network context of a candidate. The software can also be used to find the minimum-weight path in a graph.
Estimates the model parameters from experimental data, as well as to quantify the uncertainty in this estimation. UQSA is a program that can be applied on a relatively large model relevant for synaptic plasticity, using experimental data from several sources. This model is designed to be used in two types of conditions: experimental design and model building.
Permits network reconstruction and quantification of networks in multiple cell lines. CNR can be employed to recognize the most relevant differences between multiple cell lines. It combines incomplete perturbation data with annotation of the nodes that are directly affected by the perturbations to run its analyses. This tool can estimate the effect of a perturbation on the steady-state concentration of the active form of a protein.
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.
Models signaling networks from perturbation data. STASNet is a computational pipeline that uses modular response analysis (MRA) and profile likelihood. The application uses an extended version of MRA that creates semi-quantitative models from snapshot perturbation-data. In addition, the package supplies analysis functions to improve and compare models. It was tested by modeling the effect of a SHP2 knock-out on the MAPK and PI3K signaling network.
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.
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.
Allows users to analyze mutations found in standard variant files from whole exome of genome sequencing experiments in combination with gene expression values. Hipathia proceeds by computing the signal transduction along signalling pathways from transcriptomic data. This software is built on an iterative algorithm that computes the signal intensity passing through the nodes of a network. This approach permits researchers to compute the signal arriving to the functions annotated to each pathway.
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.
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.
Identifies enriched pathways in focus networks. PEANuT is a Cytoscape plugin designed to annotate protein interaction networks with biological pathway information. The interactome of the organism denotes the background network. The user can choose between the three databases ConsensusPathDB, Pathway Commons and Wikipathways to annotate the network. It was designed to work in concert with the VIPER plugins.
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.
Provides a first estimate of the possible signaling effects of such changes, e.g., drug or microRNA treatments. PathwayLinker minimizes the users' efforts by integrating protein-protein interaction and signaling pathway data from several sources with statistical significance tests and clear visualization.
Detects significantly enriched subpathway by combining classical enrichment analysis and actual perturbation on a given pathway. Sub-SPIA’s process consists of: (1) reconstruction of the gene network from the signalling pathways; (2) mapping of the differentially expressed genes (DEGs) in the constructed gene network; and (3) location of sub-pathways and evaluation of their statistical and perturbation significance.
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.
Investigates biological pathway networks. PathRings is a web application that consists of an extendable interface. This platform provides three main features: (i) a modulable bubble-based interface intending to assist users in dynamic analysis and comparative studies; (ii) a panning navigation enabling the view of a continuous working space and (iii) a hierarchical view of human pathways with a function for overlapping multiple data such as orthologs.
Assists for the reconstruction of small signal transduction pathways based on defined patterns and including microRNAs (miRNAs), genes and transcription factors (TFs). CyTRANSFINDER can identify new biological circuits and process exploratory analysis and therefore doesn’t require expression data. This Cytoscape plug-in works by exploiting signal transduction pathways (STP) discovery among sets of genes.
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.
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.
Allows users to link the models of signaling pathway with gene regulatory networks (GRNs). Sig2GRN represents a plugin for the Cytoscape software. It is able to simulate the dynamics of the signaling pathways and the subsequent time-series gene expression data. This tool can generate the dynamics of all the nodes’ activities in the network. Also, this method can predict the gene expression time-course data given the perturbations to the signaling pathways.
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.
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.
Computes network architecture by extracting latent sources of variance with little predictive value. DIONESUS is a scalable algorithm that uses partial least squares regression including the Variance of Importance in Projection (VIP) score. It was applied to derive the architecture of a prototypical carcinoma cell that overexpresses Epidermal Growth Factor Receptor (EGFR).
Allows inference of arbitrarily complex branching and recombination processes. B-RGPs is a comprehensive framework that infers branching and recombination processes which are arbitrarily complex both in terms of the number of branches, and richness of the underlying dynamics. The software focuses on transcriptional branching, but framework is equally amenable to investigate the dynamics of other branching processes.
Detects missing pathway elements. TPS assembles temporal phosphoproteomic data and generic Protein-protein interaction (PPI) networks accompanied by optional prior knowledge to produce custom pathway representations thanks to a constraint-based approach. The software aims to extend known signaling events and includes non-phosphorylated proteins in the predicted pathway structures.
Applies gene set analysis (GSA) to linked- transcription factor binding data (TFBD) for inferring associations between transcription factors and gene sets. REPA systematically infers relationships between transcription factors (TFs) and gene sets. The software predictions can be used to improve the interpretation of expression profiling studies by suggesting putative regulators underlying the observed transcriptional responses.
Discovers signaling paths under pathways subject to cumulative impact of modestly associated variants. DAPath is a disease associated path analyzer tool for analyzing the cumulative effect of variants on signaling paths. The software allows users to (i) infer which outcomes of the pathway are affected, (ii) understand disease mechanisms, and (iii) identify possible drug targets. It was applied to Behçet’s disease genome-wide association study (GWAS) data and extracted paths in pathways related to inflammation and immunity.
Provides a logic-based framework to reconstruct signaling networks by using phosphoproteomic data and prior knowledge about their connectivity. This algorithm allows cells to be interrogated in the presence or not of drugs or small molecules that inhibit specific interaction. It aims to permit researchers to design complex experiments and dependencies across networks.
Allows analysis, integration, and interpretation of data derived from omics experiments. IPA is a web-based software application which identifies key regulators and activity to explain expression patterns, predicts downstream effects on biological and disease processes, provides targeted data on genes, proteins, chemicals, and drugs and builds interactive models of experimental systems. The analysis and search tools can uncover the significance of data and identify new targets or candidate biomarkers within the context of biological systems.
It is the most advanced standalone pathway analysis application available. Similar to iPathwayGuide, it also considers the size, role, and position of each gene on the pathway as it models high-throughput sequencing data. PathwayGuide offers expert level users the ability to model a wide variety of data, from over 13 organisms. PathwayGuide allows for modeling against KEGG or Reactome Pathways with a broader array of analysis engines.