CellDesigner is a diagram editor developed by the Systems Biology Institute for drawing process diagrams (Kitano et al., 2005, PMID 19668183) using the graphical notation proposed by Prof. Hiroaki Kitano, and stored in SBML format. CellDesigner supports the development of mathematical models and is integrated with SBML ODE Solver, SBML Simulation Core and Copasi. The entities on a diagram can be annotated and linked to various databases.
CellDesigner supports a system of symbols based on a draft of the Systems Biology Graphical Notation (SBGN) Process Description language Level 1 proposed in 2008 (more information can be found here). SBGN Viewer tool in CellDesigner can be used to see a diagram in the current version of SBGN (Le Novère et al., 2009, PMID 19668183).
Newt is developed to make it easy to design SBGN diagrams: rich yet minimalistic user-friendly UI; support for developing maps from scratch; automatic layout facilities; full support for complexes, compartments and submaps; state-of-the-art complexity management through hide-show and collapse-expand functionalities; advanced diagramming with grid and alignment support, resizing and styling map objects, and more.
SBGN-ED is an open-source SBGN editor which allows creating, editing and exploring diagrams in all three SBGN languages: Process Description, Activity Flow and Entity Relationship (Czauderna et al., 2010, doi:/10.1093/bioinformatics/btq407). It allows validation of the syntactical and semantical correctness of created or edited maps. Already existing non-SBGN maps from the KEGG database can be translated into SBGN PD maps including automatic layout. Translation of PD to AF maps and visualisation of SBML models in SBGN PD are also provided. Additionally, the tool allows exporting of SBGN maps into several file and image formats including the SBGN-ML format.
SBGN-ED is an add-on of the VANTED framework. VANTED is an integrative and extendable framework for systems biology applications which aims at the integration, analysis and visual exploration of experimental data in the context of biological networks as well as the modelling, simulation and analysis of molecular biological processes.
yEd from yWorks GmbH is a freely available graph editor written in Java that runs on Windows, macOS, and Linux/Unix. It is a powerful desktop application that can be used to quickly and effectively generate high-quality diagrams.
yEd uses the XML-based GraphML format to load/save diagrams and additionally supports import from Excel spreadsheets (.xls, .xlsx) and arbitrary XML (via XSLT). Creating diagrams manually is easy and fun with the intuitive user interface, and a large collection of powerful layout algorithms allows to automatically arrange nodes and edges. Diagrams can be exported to bitmap and vector formats: PNG, JPG, and SVG, PDF.
Since version 3.17.1 yEd provides a palette section for Systems Biology Graphical Notation glyphs and new arrow styles to support SBGN catalysis and necessary stimulation arcs.
Now diagrams can be created in the intuitive general-purpose yEd Graph Editor using the SBGN Palette available since version 3.17.1, and, thanks to the converter, the outcome could be offered in the standard Systems Biology format. The ySBGN tool supports keeping annotation information in the SBGN-ML format.
For reporting issues please use ySBGN GitHub Issues page.
The MINERVA platform (Gawron et al., 2016, PMID 28725475) allows interactive visualisation and exploration functionalities based on Google Maps API, and provides a suite of advanced tools for data exploration. The platform allows the upload and the visualisation of custom experimental datasets, including multi-omic entries and user-defined color-coding of elements and interactions. A dedicated query system allows displaying targets for drugs of interest on hosted maps. The commenting system enables users to directly annotate the explored content and pin their remarks to specific elements or interactions. Finally, a set of export options allows downloading the parts, or the entirety of the hosted maps as models in SBGN-compliant format, tab-delimited network format, or images.
The integrated NaviCell web-based toolbox allows to import and visualise heterogeneous omics data on top of the ACSN maps and to perform functional analysis of the maps. NaviCell web-based toolbox is also suitable for computing aggregated values for sample groups and protein families and mapping this data onto the maps. The tool contains standard heatmaps, barplots and glyphs as well as the novel map staining technique for grasping large-scale trends in numerical values projected onto a pathway map. The combination of these flexible features that provides an opportunity to adjust the modes of visualisation to the data type and achieve the most meaningful picture. The tool is embedded into the ACSN environment, the user can upload their omics data online and visualise it in the context of the ACSN that allows to figure out what are molecular processes specifically deregulated in the analysed sample or group of samples (Kuperstein et al., 2013, PMID 24099179; Bonnet et al., 2015, PMID 25958393).
BiNoM (Biological Network Manager; Bonnet et al., 2013, PMID 23453054) is a Cytoscape plugin, developed to facilitate the manipulation of biological networks represented in standard systems biology formats (SBML, SBGN, BioPAX) and to carry out studies on the network structure. BiNoM provides the user with a complete interface for the analysis of biological networks in Cytoscape environment.
BiNoM offers functions for the import-export of some standard systems biology file formats (import from CellDesigner, BioPAX Level 3 and CSML; export to SBML, CellDesigner and BioPAX Level 3), and a set of algorithms to analyse and reduce the complexity of biological networks. BiNoM can be used to import and analyse files created with the CellDesigner software. BiNoM provides a set of functions allowing importing BioPAX files, but also to search and edit their content. BiNoM also implements a collection of powerful graph-based functions and algorithms such as data visualisation, path analysis, decomposition by the involvement of an entity or cyclic decomposition, sub-networks clustering and decomposition of a large network in modules.
HiPathia is a web tool for the interpretation of the consequences that changes in gene expression levels and/or genomic mutations occurring within signalling pathways can have over cell functionality. HiPathia models the signal transduction along stimulus-response circuits defined within signalling pathways in a more efficient way than other previous proposals (Amadoz et al., 2018, PMID 29868818). The algorithm transforms low-informative gene expression and/or genomic variation data into stimulus-response signalling circuit activities (Hidalgo et al., 2017, PMID 28042959). Changes in signalling circuit activities can directly be related to the corresponding changes in cell functionalities triggered by them. Signalling activity has proven to be superior to conventional biomarkers in predicting patient outcomes (Hidalgo et al., 2018, PMID 30134948) or drug sensitivity (Amadoz et al., 2015, PMID 26678097). Moreover, signalling pathway modeling help to uncover disease mechanisms (Sebastian-Leon et al., 2014, PMID 25344409) and molecular mechanisms behind relevant biological phenomena (Ferreira et al., 2018, PMID 29440659), as well as can be used to simulate therapeutic interventions and drug effects (Salavert et al., 2016, PMID 27137885). In addition to the web server, Hipathia is available as a Bioconductor R package.
Metabolizer is a web-based application that offers an intuitive, easy-to-use interactive interface to analyse differences in pathway metabolic module activities. Metabolic modules are conserved parts of metabolism which start with a substrate(s) and ends with a product(s) (Muto et al., 2013, PMID 23384306). Metabolizer can also be used for class prediction and in silico prediction of knock-out or drug (alone or in combination) effects. Moreover, Metabolizer can automatically predict the optimal knock-out intervention for restoring a diseased phenotype. Metabolic activities have been used to predict patient survival and to detect new therapeutic targets with high accuracy (Cubuk et al., 2018, PMID 30135189).