Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP)—in conjunction with metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics). Third, Escher harnesses the strengths of web technologies (SVG, D3, developer tools) so that visualizations can be rapidly adapted, extended, shared, and embedded. This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools.
We are now in the age of big data. More than ever before, biological discoveries require powerful and flexible tools for managing large datasets, including both visual and statistical tools. Pathway-based visualization is particularly powerful since it enables one to analyze complex datasets within the context of actual biological processes and to elucidate how each change in a cell effects related processes. To facilitate such approaches, we present Escher, a web application that can be used to rapidly build pathway maps. On Escher maps, diverse datasets related to genes, reactions, and metabolites can be quickly contextualized within metabolism and, increasingly, beyond metabolism. Escher is available now for free use (under the MIT license) at https://escher.github.io.
Citation: King ZA, Dräger A, Ebrahim A, Sonnenschein N, Lewis NE, Palsson BO (2015) Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways. PLoS Comput Biol11(8): e1004321. https://doi.org/10.1371/journal.pcbi.1004321
Editor: Paul P. Gardner, University of Canterbury, NEW ZEALAND
Received: March 18, 2015; Accepted: May 5, 2015; Published: August 27, 2015
Copyright: © 2015 King et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: Funding for this work was provided by: ZK: The National Science Foundation Graduate Research Fellowship (https://www.nsfgrfp.org/) under Grant no. DGE-1144086 AD: The European Commission as part of a Marie Curie International Outgoing Fellowship within the EU 7th Framework Program for Research and Technological Development (EU project AMBiCon, 332020, http://ec.europa.eu/research/mariecurieactions/about-mca/actions/iof/index_en.htm) AE, NS, BOP: A grant from the Novo Nordisk Foundation provided to the Center for Biosustainability at the Technical University of Denmark (http://www.biosustain.dtu.dk/english) NEL: Novo Nordisk Foundation grant NNF 132150-002 (http://www.novonordiskfonden.dk/en), and a grant from the National Institutes of Health #R21 HD080682 (http://www.nih.gov/) BOP: The Department of Energy under Grant no. DE-SC0008701 (http://www.energy.gov/) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
The behavior of an organism emerges from the complex interactions between genes, proteins, reactions, and metabolites. With next-generation sequencing and various “omics” technologies, it is now possible to rapidly and comprehensively measure these components and interactions. These technologies have transformed the scientific process over the past decade. Data acquisition is substantially easier, but data analysis is increasingly becoming the primary bottleneck to discovery. To address the analysis bottleneck, there has been a demand for data visualization tools to complement statistical and modeling methods.
Biological visualizations often fall into categories characterized by biological scale, and the style of a visualization reflects the type of information at that scale. Three-dimensional objects are often used for representing protein structures [1, 2], one-dimensional tracks for genome sequences [3, 4], force-directed graphs for interaction networks , trees for phylogenetic relationships [6, 7]. And, finally, two-dimensional pathway maps have long been a popular visual representation of metabolic pathways and other biological pathways. For each type of visualization, data can be associated with the biological components in the visualization. Visualizing data in this way contextualizes and enriches the dataset for scientists. Data-rich visualizations have been extremely valuable for viewing, interpreting, and communicating data.
A tool for visualizing pathway maps must satisfy a set of core features. The tool must (1) visually represent reactions and pathways clearly and in a way that is biochemically correct, (2) allow users to navigate and search through the visualization, (3) allow users to design and customize pathway maps, (4) allow users to represent diverse data types within the map using visual cues like size and color, (5) provide import and export features so that maps can be stored, shared, and exported to other tools, and (6) provide an application program interface (API) so the tool can be used within data analysis pipelines.
The existing tools that satisfy these core features are all desktop applications. Briefly, these tools include Omix , Cytoscape , CellDesigner , Vanted  with the SBGN-ED add-on , VisAnt  and PathVisio . Desktop applications have many advantages over web applications, including speed, stability, and integration with the operating system, and these merits have made desktop applications more popular.
The advantages of web applications include rapid deployment (no need to download an application or browser plug-in), greater cross-platform compatibility (e.g. mobile devices), flexible sharing, collaborating, and embedding features, as well as easy application development. Recently, a critical mass of performance enhancements and new libraries has made web tools comparable to desktop tools for many applications.
A number of web-based tools exist for visualizing pathway maps: ArrayXPath , Pathway Projector , iPath2.0 , WikiPathways , Biographer , and the BioCyc pathway viewer . However, none of these satisfy all the core features for a pathway map visualization tool.
Escher is a web application for visualizing pathway maps, and it is designed to be a fully featured pathway visualization tool that also harnesses all the advantages of the web. Escher has three key features that distinguish it from all existing pathway visualization tools, including the popular desktop applications. First, Escher makes building pathway maps fast and easy, using the information in datasets and genome-scale models to suggest pathways to the user—with this, pathway map design can be semi-automated. Second, Escher connects genes and enzymes to the reactions they catalyze, so that genomic data can be visualized in the context of the reaction network. We show how Escher can be used to visualize reaction data (metabolic fluxes), metabolite data (metabolomics), and genomic data (transcriptomic data), bridging the gap between these data types. Third, Escher uses the advantages of web technologies so that pathway maps can be adapted, extended, shared, and embedded. We illustrate the export and development features of Escher, including native support for scalable vector graphics (SVG) export, a downloadable tool for converting Escher maps to common standards for representing layouts, and application program interfaces (APIs) for developing new applications that extend the functionality of Escher.
Building pathway maps
To build a pathway map, one first needs a source for the names, stoichiometries, and associated genes for each biochemical reaction in an organism. This information is provided by a constraint-based reconstruction and analysis (COBRA) model, a collection of all the reactions, metabolites, and genes known to exist in an organism (also called a genome-scale model (GEM) or constraint-based model (CBM)) . While COBRA models have generally focused on metabolism, the COBRA modeling approach can be applied to any biochemical reaction network , so Escher could be used to visualize pathways like gene expression and membrane translocation, which are now being incorporated into COBRA models [23–25].
The Escher interface is centered around a canvas for the pathway map (Fig 1A). In the Escher Builder, a number of editing modes are available in the Edit menu; these include tools for navigating the map (Pan mode), selecting and modifying elements (Select mode), adding reactions (Add reaction mode), rotating the current selection (Rotate mode), and adding and editing text annotations (Text mode).
A) The application includes a set of menus with a link to the documentation, a button bar for accessing common features, and a menu for jumping to maps that were built with the same model. B) To build pathway maps, enter the Add reaction mode using the Edit menu or the button bar. Click on the canvas or an existing metabolite to see a search menu. Reactions can be searched by reaction ID, by metabolite, and by gene. When a gene dataset or reaction dataset is loaded, suggestions appear for the reactions with the largest values in the dataset.
In Add reaction mode, a new pathway can be added to the canvas. Clicking on the canvas or an existing metabolite opens the new reaction search box. The search box can find reactions with a number of queries: reaction identifiers (IDs) and display names, metabolite IDs and display names, and gene IDs and names (Fig 1B). (IDs and names are based on those in the COBRA model.) If a reaction or gene dataset is loaded, then Escher provides suggestions of the next reaction to build, sorted by the data value for that reaction (Fig 1B).
With this set of suggestions, a user can quickly build an Escher map based on previous knowledge of the organism or using the suggestion of a dataset. Data-driven map layout is also extremely useful for understanding an organism at the genome-scale—guided by the data, it is possible to find all the elements of a network that are, for example, highly upregulated without any bias toward well known pathways. To add the top suggested reaction, a user can simply press the Enter key. Thus, if a pathway is linear or has high values in a given dataset, then pressing Enter repeatedly will draw a linear pathway that is based entirely on the information in the data and the COBRA model. This process can be repeated to build perpendicular branches from metabolites in the pathway.
The Escher interface includes a general menu, a menu bar for accessing common functions, a tool for switching between maps, and a canvas containing the interactive pathway map (Fig 1A). The Map and Model menus contain import and export functions for maps and COBRA models. The Data menu contains the data loading functions, and the View menu contains zoom options and access to the Settings page.
Three types of data can be visualized on an Escher map: reaction data, metabolite data, and gene data. And Escher supports visualizing a single dataset, or visualizing the comparison of two datasets using a number of comparison functions (log, log2, and difference). The Settings page includes a detailed set of options for coloring and sizing elements based on statistical features of a dataset (min, max, quartiles, mean). Here, examples are provided for each data type, and the files required for recreating the visualizations are in the supplementary data.
The resulting figure shows reaction fluxes for fermentation pathways (Fig 2A). It was downloaded as a SVG image with the command Map>Export as SVG, and the text labels of the high flux reactions were made larger for the figure.
A) The results of an in silico flux simulation visualized on the reactions. B) Metabolomics data for E. coli aerobic growth visualized on the metabolites. C) RNA-Seq data showing the shift from aerobic to anaerobic conditions in E. coli. Green represents reactions downregulated in anaerobic growth and red represents gene upregulated in anaerobic growth, based on the log2 of the fold change.
Metabolite concentrations are shown from a dataset recently reported by our research group , which were organized in a CSV file with metabolite BiGG IDs as keys (S2 Data). The example figure shows aerobic metabolite concentrations on a modified map of E. coli central metabolism (S3 Data). To better identify metabolite concentration differences, the metabolite size was changed on the Settings page, and the secondary metabolites were hidden.
The resulting figure provides a high level view of the most abundant metabolites in the network during aerobic growth of E. coli (Fig 2B). It was downloaded as a SVG image with the command Map>Export as SVG, and the text annotations were made larger for the figure.
To demonstrate the use of gene data on an Escher map, transcript abundances for aerobic and anaerobic growth of E. coli were calculated using RNA-Seq datasets from a recent publication . The datasets were downloaded from the Gene Expression Omnibus (GEO) repository  (accession number GSE48324), and fragments per kilobase of exon per million fragments mapped (FPKMs) were calculated using the Cufflinks functions cuffquant and cuffnorm , with appropriate parameters for the library type of the published data. These data were then collected, with locus tags as gene identifiers, in a single CSV file (D4 Data).
To connect genomic data with the reactions on an Escher map, Escher must consider which gene products are responsible for catalyzing each biochemical reaction. This association can be defined using Boolean gene reaction rules (also called gene-protein-reaction associations (GPRs)) . When either of two enzymes can catalyze a reaction—as with isozymes—then these genes are connected with an OR rule. Escher adds the values of two genes connected with an OR rule. When two enzymes are required together for catalysis—as in an enzyme complex—these are connected with an AND rule. Escher can take the mean or the minimum of the two values connected with an OR rule; this option is selected on the Settings page. For a comparison of two datasets, the gene reaction rules are evaluated for each dataset separately, then a comparison is made between the two resulting values (Fig 2C).
The resulting figure shows the shift from aerobic to anaerobic conditions, where green reactions are downregulated anaerobically and red reactions are upregulated anaerobically (Fig 2C). Escher shows the log2 of fold change between the conditions. However, Escher cannot yet display statistical significance for the datasets, so it should be paired with statistical tools (e.g. cuffdiff ).
Design and Implementation
A Python package for Escher is also available (Fig 3A), and this package includes a number of extra features: access to Escher maps from Python terminals and IPython Notebooks, offline access to Escher, a local server with map and model caching, and a Python API for developing applications with these additional features. Accessing maps from Python and IPython Notebook allows Escher to be integrated directly with data analysis and modeling workflows. For example, within an IPython Notebook, the results of an in silico flux simulation can be applied to an Escher map, and the map will be embedded and shared with the notebook. Escher even supports NBViewer for sharing static IPython Notebooks as websites (http://nbviewer.ipython.org).
Map and model database
Escher includes a database of pathway maps and genome-scale models. Pathway maps are currently available for a number of organisms, and new pathway maps will be continually added to the database from our group. The maps in the BiGG database are being converted to the new Escher format . We also accept contributions from the community, and the method for submitting pathway maps is described in the documentation (S2 File).
Escher represents biochemical reactions as transformations from a set of reactants to a set of products, and each reaction can be assigned enzymes using a Boolean gene reaction rule. Thus, Escher uses a well-defined representation of the biochemical network, but the scope of the Escher notation is much more specific than community standards such as Systems Biology Graphical Notation (SBGN)  and Systems Biology Markup Language (SBML) with the layout extension [35–37]. Escher can be exported to both formats using the EscherConverter application (Fig 4). EscherConverter is written in Java™, and it is available as a standalone executable file (S3 File) that includes a graphical user interface with graph drawing capabilities and a command-line interface. Files can be opened through drag and drop or the file menu, and a history of up to 10 recent files is stored. Several user preferences allow flexible customization of the file conversion. The conversion to SBML and SBGN-ML (the XML implementation of SBGN) relies heavily on JSBML  and libSBGN .
Integrating Escher with analysis workflows
The Escher Python package, which is available from the Python Package Index (PyPI, https://pypi.python.org), can be used to integrate Escher maps with data analysis and simulation workflows. Using the available functions, datasets can be applied to Escher maps, and the resulting maps can be saved as standalone web pages, saved as JSON or SVG, or exported using the EscherConverter as a command line utility. The Python package works directly with COBRA models using COBRApy . It also includes functions for modifying all of the Escher map settings, including the color and size scales for all elements.
The Python package also includes a simple web server to run Escher locally. The web server caches maps and models for offline use, and users can also add maps to the cache directory so that they appear in the local web application. The following commands will install the package, print the location of the local cache directory, and run the Escher server:
- # install escher
- pip install escher
- # print the cache directory
- python -c “import escher; print escher.get_cache_dir()”
- # run the local server (available at http://localhost:7778)
- python -m escher.server
Developing with Escher
Availability and Future Directions
Escher version 1.1 is now available. Bug fixes and new pathway maps will be released regularly, and a number of Escher applications are currently in progress. Escher releases will follow the Semantic Versioning guidelines (http://semver.org) so that application developers can rely on new versions of Escher to be backwards compatible.
The Escher approach to web visualization
A major focus during development of future Escher versions will be to generalize and improve the approach to web visualization. As discussed in the Introduction, there are many types of biological visualizations that contribute to our interpretation of “omics” datasets. Successful user interface designs should be applicable to all of these visualization types, with modifications for the specific needs of a tool. As web platforms become ubiquitous for application development, it is important to consider what elements might be shared across a suite of visualization tools. This would make development of new tools easier, and improve interoperability between tools. For example, a genetic dataset in Escher could link directly to a visualization of the dataset on a genome browser.
The BiGG database
Escher will be included in the next release of the BiGG database . The BiGG database is a repository for COBRA models developed in the Systems Biology Research Group at the University of California, San Diego. BiGG already includes static pathway maps for many models in the database. Escher maps will be embedded in the web pages for models, reactions, and metabolites so that users can quickly see the network context of a biological component, and the maps will be available on both the BiGG and Escher websites.
A community effort
The Escher framework is highly amenable to improvements, such as new visual features. Example improvements include compartment membranes, representations of regulation and signaling such as those in the SBGN specification, better statistical tools for analyzing and comparing various data types, more import and export options, and direct integration of other visualizations (such as protein and metabolite structures). Because Escher is an open-source project, contributions from the community—bug fixes, use cases, code contributions, etc.—will be encouraged and will be an important factor in making Escher a sustainable, long-term solution to the challenges of visualizing biological pathways.
Availability and Requirements
- Project name: Escher
- Project home page: https://escher.github.io
- Project source: https://github.com/zakandrewking/escher
- Open-source license: MIT license
- Operating systems(s): Platform independent
- Other requirements: none
- Any restrictions to use by non-academics: no limitations
This source code is for Escher version 1.1.2. The latest Escher source code can be cloned or downloaded from https://github.com/zakandrewking/escher.
S2 File. The Escher documentation as a PDF file.
This documentation is for Escher version 1.1.2. The latest Escher documentation can be found at https://escher.readthedocs.org.
S3 File. The executable EscherConverter.
Requires Java™ version 1.8 or higher. The latest version of EscherConverter is available on the Escher homepage at https://escher.github.io.
S1 Data. The in silico flux predictions used to demonstrate reaction data visualization.
S2 Data. The metabolomics dataset used to demonstrate metabolite data visualization.
S3 Data. The Escher map used to demonstrate metabolite data visualization.
We would like to thank Joshua Lerman, Pierre Salvy, Amoolya Singh, Daniel Dougherty, Erin Wilson, Maxime Durot, and all the scientists at Amyris, Inc., for their invaluable help and guidance during the development of Escher.
Wrote the paper: ZAK. Designed the user interface: ZAK, AD. Designed import/export functionality: ZAK, AD, AE, NS. Authored the core Escher library: ZAK, AE. Defined the broad goals and software features: ZAK, AD, NEL, BOP.
- 1. Arnold K, Bordoli L, Kopp J, Schwede T (2006) The SWISS-MODEL workspace: A web-based environment for protein structure homology modelling. Bioinformatics 22: 195–201. pmid:16301204
- 2. Herráez A (2006) Biomolecules in the computer: Jmol to the rescue. Biochem Mol Biol Educ 34: 255–261. pmid:21638687
- 3. Skinner ME, Uzilov AV, Stein LD, Mungall CJ, Holmes IH (2009) JBrowse: A next-generation genome browser. Genome Res 19: 1630–1638. pmid:19570905
- 4. Karolchik D, Barber GP, Casper J, Clawson H, Cline MS, et al. (2014) The UCSC Genome Browser database: 2014 update. Nucleic Acids Res 42: 764–770.
- 5. Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T (2011) Cytoscape 2.8: New features for data integration and network visualization. Bioinformatics 27: 431–432. pmid:21149340
- 6. Letunic I, Bork P (2007) Interactive Tree Of Life (iTOL): An online tool for phylogenetic tree display and annotation. Bioinformatics 23: 127–128. pmid:17050570
- 7. Huson DH, Richter DC, Rausch C, Dezulian T, Franz M, et al. (2007) Dendroscope: An interactive viewer for large phylogenetic trees. BMC Bioinformatics 8: 460. pmid:18034891
- 8. Droste P, Nöh K, Wiechert W (2013) Omix—A visualization tool for metabolic networks with highest usability and customizability in focus. Chemie-Ingenieur-Technik 85: 849–862.
- 9. Funahashi A, Matsuoka Y, Jouraku A, Morohashi M, Kikuchi N, et al. (2008) CellDesigner 3.5: A Versatile Modeling Tool for Biochemical Networks. Proc IEEE 96: 1254–1265.
- 10. Rohn H, Junker A, Hartmann A, Grafahrend-Belau E, Treutler H, et al. (2012) VANTED v2: a framework for systems biology applications. BMC Syst Biol 6: 139. pmid:23140568
- 11. Czauderna T, Klukas C, Schreiber F (2010) Editing, validating and translating of SBGN maps. Bioinformatics 26: 2340–2341. pmid:20628075
- 12. Hu Z, Chang YC, Wang Y, Huang CL, Liu Y, et al. (2013) VisANT 4.0: Integrative network platform to connect genes, drugs, diseases and therapies. Nucleic Acids Res 41: 225–231.
- 13. Kutmon M, van Iersel MP, Bohler A, Kelder T, Nunes N, et al. (2015) PathVisio 3: An Extendable Pathway Analysis Toolbox. PLOS Comput Biol 11: e1004085. pmid:25706687
- 14. Chung HJ, Kim M, Park CH, Kim J, Kim JH (2004) ArrayXPath: Mapping and visualizing microarray gene-expression data with integrated biological pathway resources using Scalable Vector Graphics. Nucleic Acids Res 32: 621–626.
- 15. Kono N, Arakawa K, Ogawa R, Kido N, Oshita K, et al. (2009) Pathway projector: Web-based zoomable pathway browser using KEGG Atlas and Google Maps API. PLoS One 4: e7710. pmid:19907644
- 16. Yamada T, Letunic I, Okuda S, Kanehisa M, Bork P (2011) IPath2.0: Interactive pathway explorer. Nucleic Acids Res 39: 412–415.
- 17. Kelder T, van Iersel MP, Hanspers K, Kutmon M, Conklin BR, et al. (2012) WikiPathways: Building research communities on biological pathways. Nucleic Acids Res 40: 1301–1307.
- 18. Krause F, Schulz M, Ripkens B, Flöttmann M, Krantz M, et al. (2013) Biographer: Web-based editing and rendering of SBGN compliant biochemical networks. Bioinformatics 29: 1467–1468. pmid:23574737
- 19. Latendresse M, Karp PD (2011) Web-based metabolic network visualization with a zooming user interface. BMC Bioinformatics 12: 176. pmid:21595965
- 20. Stallman RM (1981). EMACS the extensible, customizable self-documenting display editor. ACM.
- 21. Bostock M, Ogievetsky V, Heer J (2011) D3: Data-Driven Documents. IEEE Trans Vis Comput Graph 17: 2301–2309. pmid:22034350
- 22. Bordbar A, Monk JM, King ZA, Palsson BØ (2014) Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet 15: 107–20. pmid:24430943
- 23. King ZA, Lloyd CJ, Feist AM, Palsson BO (2015) Next-generation genome-scale models for metabolic engineering. Curr Opin Biotechnol 35: 23–29.
- 24. Liu J, O’Brien E, Lerman J, Zengler K, Palsson BØ, et al. (2014) Reconstruction and modeling protein translocation and compartmentalization in Escherichia coli at the genome-scale. BMC Syst Biol 8: 110. pmid:25227965
- 25. O’Brien EJ, Lerman JA, Chang RL, Hyduke DR, Palsson BØ (2013) Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction. Mol Syst Biol 9: 693. pmid:24084808
- 26. Lewis NE, Hixson KK, Conrad TM, Lerman JA, Charusanti P, et al. (2010) Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol Syst Biol 6: 390. pmid:20664636
- 27. Orth JD, Conrad TM, Na J, Lerman JA, Nam H, et al. (2011) A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011. Mol Syst Biol 7: 535. pmid:21988831
- 28. McCloskey D, Gangoiti J, King Z, Naviaux R, Barshop B, et al. (2013) A model-driven quantitative metabolomics analysis of aerobic and anaerobic metabolism in E. coli K-12 MG1655 that is biochemically and thermodynamically consistent. Biotechnol Bioeng 111: 803–815. pmid:24249002
- 29. Bordbar A, Nagarajan H, Lewis NE, Latif H, Ebrahim A, et al. (2014) Minimal metabolic pathway structure is consistent with associated biomolecular interactions. Mol Syst Biol 10: 737. pmid:24987116
- 30. Edgar R, Domrachev M, Lash AE (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30: 207–210. pmid:11752295
- 31. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, et al. (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7: 562–78. pmid:22383036
- 32. Reed JL, Vo TD, Schilling CH, Palsson BØ (2003) An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol 4: R54. pmid:12952533
- 33. Schellenberger J, Park JO, Conrad TM, Palsson BØ (2010) BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics 11: 213. pmid:20426874
- 34. Kitano H, Funahashi A, Matsuoka Y, Oda K (2005) Using process diagrams for the graphical representation of biological networks. Nat Biotechnol 23: 961–966. pmid:16082367
- 35. Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, et al. (2003) The systems biology markup language (SBML): A medium for representation and exchange of biochemical network models. Bioinformatics 19: 524–531. pmid:12611808
- 36. Gauges R, Rost U, Sahle S, Wegner K (2006) A model diagram layout extension for SBML. Bioinformatics 22: 1879–1885. pmid:16709586
- 37. Dräger A, Palsson BØ (2014) Improving collaboration by standardization efforts in systems biology. Front Bioeng Biotechnol 2: 1–20.
- 38. Rodriguez N, Thomas A, Watanabe L, Vazirabad IY, Kofia V, et al. (2015) JSBML 1.0: providing a smorgasbord of options to encode systems biology models. Bioinformatics. In press.
- 39. van Iersel MP, Villéger AC, Czauderna T, Boyd SE, Bergmann FT, et al. (2012) Software support for SBGN maps: SBGN-ML and LibSBGN. Bioinformatics 28: 2016–2021. pmid:22581176
- 40. Ebrahim A, Lerman JA, Palsson BO, Hyduke DR (2013) COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst Biol 7: 74. pmid:23927696