Displaying complex metabolic-map diagrams, for Web browsers, and allowing users to interact with them for querying and overlaying expression data over them is challenging.Cellular Overview. The main characteristic of this application is the zooming user interface enabling the user to focus on appropriate granularities of the network at will. Various searching commands are available to visually highlight sets of reactions, pathways, enzymes, metabolites, and so on. Expression data from single or multiple experiments can be overlaid on the diagram, which we call the Omics Viewer capability. The application provides Web services to highlight the diagram and to invoke the Omics Viewer. This application is entirely written in JavaScript for the client browsers and connect to a Pathway Tools Web server to retrieve data and diagrams. It uses the OpenLayers library to display tiled diagrams.We present a Web-based metabolic-map diagram, which can be interactively explored by the user, called the Biocyc.org: The Cellular Overview is accessible under the Tools menu.This new online tool is capable of displaying large and complex metabolic-map diagrams in a very interactive manner. This application is available as part of the Pathway Tools software that powers multiple metabolic databases including  Web-based applications using a zoomable user interface (ZUI) are becoming a familiar approach to allowing people to comprehend complex information spaces. Examples include zoomable genome browsers ,2 and GoMetabolic network models are now available for hundreds of organisms due to the high rate of genome sequencing and the availability of software tools for reconstructing metabolic network models from genome sequence information -5. We aiE. coli that was available through the desktop version of Pathway Tools only. Later the diagram was generated by an automated layout algorithm, which allowed organism-specific diagrams to be generated for multiple organisms [The Pathway Tools software has had a metabolic network diagram available for a number of years. This diagram has evolved through many forms. It started as a manually assembled diagram for rganisms . Zoomingpathway.yeastgenome.org), MouseCyc [This article describes the information content of the Cellular Overview, and our new implementation of the diagram that makes use of modern Web technologies including JavaScript, AJAX, and OpenLayers. This Web-based diagram is now available for BioCyc, and once installed will be available for other Pathway Tools based metabolic databases (DBs) such as YeastCyc  represents a single metabolite or protein, and each blue line represents a single bioreaction. Within the cytoplasmic membrane, the small-molecule metabolism of the organism is depicted in several regions. The glycolysis and the tricarboxylic acid (TCA) cycle pathways, if present, will be placed in the middle of the diagram to separate predominately catabolic pathways on the right from pathways of anabolism and intermediary metabolism on the left. The majority of pathways operate in the downward direction. Signal transduction pathways, if present, run along the bottom left side of the diagram.The Cellular Overview diagram depicts the biochemical machinery of an organism as described in a PGDB (Pathway/Genome Database) generated by Pathway Tools. Figure Pathways are grouped into related clusters as indicated by the shaded regions . The large grid of individual reactions at the right of the diagram represents reactions of small-molecule metabolism that have not been assigned to any pathway. Periplasmic pathways and reactions are shown on the right side in between the two membranes. Transporters and other membrane proteins are embedded in the appropriate membrane. The same metabolite may appear in multiple locations in the diagram because of the high connectivity of the network. Showing all such connections between metabolites would result in so many visible edges as to render the diagram useless.BioCyc.org Web site, reflecting the 1,000 genomes available from BioCyc.The Cellular Overview is organism specific, meaning that the pathways, reactions, metabolites, and proteins shown in a version of the diagram for a given organism reflect the biochemistry of that organism as described by its PGDB. For example, 1,000 different incarnations of this diagram are available from the We define multiple semantic zoom levels for the diagram. At higher magnification levels, new information appears in the diagram such as the names of pathways, genes, enzymes, and metabolites.BioCyc.org and selecting the Cellular Overview command under the Tools menu in the top menu bar. Notice that the desired organism should be selected, using the link change in the right-upper corner of the Web page next to the current organism name, before executing the Cellular Overview command. Similarly, selecting a new organism using the change link requires to execute the command Cellular Overview to display the cellular overview of the newly selected organism. Online documentation is available at bioCyc.org under the Help menu, command Website User Guide.We give a brief description of the capabilities of the Cellular Overview. The reader is invited to experiment with the diagram by pointing a Web browser to Once the Cellular Overview diagram is displayed, the most common operation is to move it left or right (panning), or up or down, since sometimes the entire overview cannot fit in the Web page. This can be done by holding down the left mouse button in a blank area and then moving the mouse in the desired direction. To zoom-in or zoom-out, click the icon in the form of a ladder on the left of the overview Web page. Each step of the ladder is a zoom level. By increasing the zoom level , names of compounds, enzymes, reactions, and pathways are eventually displayed.In this article, 'searching' and 'highlighting' are synonymous terms. There are several commands to search for reactions, pathways, enzymes, genes, and compounds. The search commands are available from the right-click menu and from the Cellular Overview menu from the top menu bar.overlay, which is a separate graphical plane in which JavaScript code can draw rectangles, circles, and lines of different colors. The list of overlays is shown in the Layer Switcher panel on the right of the Overview Web page. From this panel you can activate or deactivate (render invisible) specific overlays.When a search is done  provides two searching mechanisms for pathways in the cellular diagram: by name or frame ID, or by a substring search. The substring search is based on the name, synonyms, and frame ID of the pathways. Highlighting is done on the reaction(s) of the pathway(s) found.• Highlight Reaction(s) provides four searching mechanisms for reactions in the cellular diagram: by name or frame ID, by substring, by EC number, or by enzyme name. The substring search is based on the name, synonyms, and frame ID of the reactions. Highlighting is done on the reaction(s) found.• Highlight Gene(s) provides three searching mechanisms for genes in the cellular diagram: by name or frame ID, by substring, or from a file.• Highlight Enzyme(s) provides two searching mechanisms for enzymes in the cellular diagram: by name or frame ID, or by a substring search. The substring search is based on the name, synonyms, and frame ID of the enzymes. This substring search is identical to the reaction search based on enzymes. Highlighting is done on the reactions and proteins corresponding to the enzyme(s) found.• Highlight Compound(s) provides two searching mechanisms for compounds in the cellular diagram: by name or frame ID, or by a substring search. The substring search is based on the name, synonyms, and frame ID of the compounds. Highlighting is done on the compound(s) found.• Mousing over a Cellular Overview icon  displays information about the object in a small tooltip popup. Several tooltips can be opened at the same time. Figure In Omics Viewer mode, the Overview is painted with a large dataset for visual analysis of that data in a global metabolic pathway context. Genes (in the case of a gene expression experiment) and proteins (in the case of a proteomics experiment) that are involved in metabolism are mapped to reaction steps in the Cellular Overview, and the range of data values levels in a given experimental dataset is mapped to a spectrum of colors. Reaction steps in the Cellular Overview are colored according to the corresponding data value. Similarly, for metabolomics experiments, compound nodes are colored according to the data value for the corresponding compound. This facility enables the user to see instantly which pathways are active or inactive under some set of experimental conditions.Expression data is provided by the user in the form of a datafile containing a two-dimensional table of numerical values where each row is labeled with a gene, reaction, compounds, or proteins. The columns are tab delimited. A description of the exact format of the datafile and sample datafiles are provided in the online help accessible via the Omics Viewer panel.E. coli K-12. Essentially all the capabilities of the Cellular Overview are still available in the Omics Viewer, including searching, tooltips, zooming-in, and zooming-out. The user can stop the Omics Viewer animation, go one step forward or backward, and increase or decrease the speed of the animation.Figure http://BioCyc.org/PToolsWebsiteHowto.shtmlThe Cellular Overview offers a simple form of Web services via HTTP GET requests. Two classes of services are offered: one for highlighting , the other for invoking the Omics Viewer. A minimal request includes the zoom level and the unique organism identifier. Full documentation for these services is available at An Omics Viewer request specifies a datafile and parameters that influence how the Omics Viewer interprets the data, such as a specification of which columns within the datafile are to be displayed on the Cellular Overview. Two protocols are provided to retrieve the datafile: the http and file protocols.openlayers.org). The Yahoo user interface library (YUI Library) is also used for some of the widget dialogs, tooltips, and asynchronous communication with the server.The Web based Cellular Overview is implemented using mostly the open software OpenLayers , this implies that no software installation is required to run OpenLayers. The performance of JavaScript engines has steadily increased from 2006 to 2010. Nowadays, JavaScript code is compiled to native code to achieve high performance.tilde images: for efficiency, large images are sliced into tiles and the needed tiles are transfered on demand. Its capabilities are similar to the Google Maps Application Programming Interface (API), but the OpenLayers implementation is open source and their licenses differ greatly. For example, the OpenLayers license does not constrain its usage when used in intranets.OpenLayers was designed to display geographical road maps by using OpenLayers takes care of fetching (and prefetching in some cases) the image tiles required to fill the portion of the view port seen by the user. When the user changes the zoom level, OpenLayers requests the appropriate tiles from the Web server. The coordinates of the tiles, as well as the zoom level, are passed as parameters to the Web server. Additional parameters are specified by our implementation to identify the organism. Then, the entire diagram is reconstructed by OpenLayers by simply laying the tiles contiguously. OpenLayers overlays can be made active, inactive, or deleted by one method call. Overlays are employed by the Cellular Overview to implement Omics Viewer highlighting and highlighting of search results.The diagrams for the Cellular Overview can be very large. For example, for some organisms, the diagram can have more than 20,000 pixels in width and height at the highest zoom levels. Such images could not be efficiently transfered from the Web server to a browser. Instead, these images are tiled -- they are sliced in smaller rectangles, the tiles. We use 400 by 200 pixel tiles in the GIF format. The creation of the tiles is done in several steps:1. A general layout is created for all zoom levels.2. The exact layouts at specific zoom levels are computed.3. Based on the layout, one large image with specific colors and text is generated.4. The large image is sliced into as many tiles as needed.The first step is done once for each organism, but steps 2 through 4 are done for each zoom level for each organism. The first step places all possible objects in the diagram but the generation at each zoom level might filter out some objects depending on the space available. The computation of step 1 is considered part of the PGDB of the organism.The second step fixes the exact location of each object in a rectangular coordinate system at a specific zoom level. Some objects are absent at the lower zoom levels. For example, enzymes are not displayed at the lowest zoom level.The third step generates one bitmap image for each zoom level. This operation is independent from the image format used. From that bitmap image a different type of format  could be generated.The fourth step takes the entire image and generates each tile as a GIF file. The file system is used as a cache since the tiles are not regenerated unless the PGDB of the organism is modified, which does not occur often.location of objects in the image. We call this the node data. But this data is needed for tooltips and highlighting--i.e., the result of searching. The tooltips mechanism also needs a textual description of each object in an overview. We call this the frame data.The generation of GIF files as described in Subsection does not provide any data on the The node and frame data is directly used by the JavaScript code running in the browser. Consequently, the server sends this data to the browser as JavaScript data structures ready to be used by the JavaScript code.AJAX (Asynchronous JavaScript and XML) is at the core of many interactive Web applications and this implementation uses it throughout. The Cellular Overview uses AJAX for the following operations:1. For the tooltip mechanism: the Web server is queried for the text data when the mouse stops at any location on the Cellular Overview2. To retrieve the tiles from the Web server (done by OpenLayers)3. For all the searches (highlighting): for the auto completion mechanism, the search itself based on strings, names, and the node data to locate the objects on the diagram4. During the zoom-in and zoom-out operations where the size of the new diagram is required; this might trigger the tiles for that zoom level to be generated by the Web server if the tiles do not exist on the serverThe browser provides the GUI for entering the datafile name and various parameters to control the Omics Viewer. The data is parsed and interpreted by the Web server and a set of objects with their colors is sent back to the browser as JavaScript data. The result can be for a single set of colors if one experiment is requested or multiple sets of colors  if several experiments are requested via the datafile and the parameters specified.Once the JavaScript data is received by the browser, the Cellular Overview creates an OpenLayers layer for each experiment  to do the highlighting. These layers are made active and inactive, by JavaScript code, to create an animation, or if a single experiment is displayed, the sole layer is made active. In summary, no new base diagram is generated, which makes the Omics Viewer very efficient, but overlays are used; once the color data has been transfered to the browser, the animation becomes independent of the Web server, which makes the Omics Viewer efficient.The Cellular Overview has been optimized to balance work between the server and the browser. The server has all the main data; data transfer is generally postponed until needed. The performance of the Cellular Overview is greatly influenced by the speed of the browser's JavaScript engine. The latest versions of Safari and Chrome (Safari 5.0 and Chrome 6.0) are known to be faster than Firefox 3.6 and Internet Explorer (IE) 7 and 8. Currently, we do not recommend the use of IE 7 or 8 as the other browsers, including IE 9, have a much faster JavaScript engine.Here are some performance timings for typical operations of the Cellular Overview using Chrome 6.0:1. First visit to the Cellular Overview, around 4 seconds.2. Zooming-in or -out without any highlighting, around 2 seconds.3. Zooming-in or -out with less than 400 highlighted objects, around 4 seconds (this includes the Omics Viewer). The time increases by about 1 second for each 300 objects to highlight.4. Starting the Omics Viewer with a data file with less than 200 lines, less than 5 seconds. It reaches about 30 seconds with a data file of 5000 lines, which is about the limit of objects that could be named for almost all organisms.5. Searching for objects, less than 4 seconds for cases resulting in less than 1000 objects.6. Tooltip, typically 1 second to open.Notice that all these numbers assume that a server communication occurs; otherwise it is faster, as the browser will be reusing cached data.The Cellular Overview can be used to display the complete set of pathways and biochemical reactions of a single organism. It does not require the installation of special software as it can be accessed with most popular browsers. It enables the user to search and find reactions, proteins, compounds and pathways using various commands. The overview can be displayed at various zoom level with increasing amount of details. It can therefore be useful to navigate the complete reaction networks of an organism with the appropriate level of details. Navigation is made easier by allowing panning in any directions without any constraint.The Cellular Overview can also be used to display expression data over reactions, compounds and proteins. The expression data can be easily provided by the user via an upload mechanism.Our discussion focuses mainly in comparing the Cellular Overview to other similar tools available on the Web.The Pathway Projector (PP)  is a Web1. The Cellular Overview generates organism-specific metabolic map diagrams for each organism, whereas the Pathway Projector always uses the same reference metabolic map diagram. The diagram contains all KEGG pathways, but pathways and reactions are whited out on an organism-specific basis. This approach is extremely problematic because the single reference KEGG Atlas pathway image contains far too much information to be comprehensible at low zoom levels -- so why provide them? The first, and perhaps the second, of the five zoom levels in PP convey essentially no information because every graphical element is too small. Even though that reference pathway map diagram contains too much information now, it must grow further over time. Currently, KEGG assigns only half as many biochemical reactions to metabolic pathways as does the MetaCyc DB ; MetaCyc2. PP visually maps experimental data for genes, enzymes, and metabolites simultaneously onto separate graphical elements, whereas the Cellular Overview does not distinguish genes from enzymes. Although such a distinction could be accomplished via separate time points in an animation.3. PP diagrams do not show pathway names, enzyme names, or gene names; the Cellular Overview does, at different semantic zoom levels.4. The Cellular Overview cannot yet search for pathways between compounds whereas the PP can.5. The Omics Viewer of the Cellular Overview can display an animation of data generated from several experiments by highlighting with key colors. On the other hand, PP displays fixed histograms when processing several experiments and no animation is provided.The iPath (Interactive Pathways Explorer)  Web-baseThe Reactome Web site  has a zoVisANT  and CytoMapMan  providesThe Pathway Tools Cellular Overview provides a fast zoomable user interface to organism-specific metabolic map diagrams. It provides extensive search capabilities, and will paint experimental datasets onto the metabolic map for visual analysis in a pathway context. This tool can be used from popular browsers  without any additional plug-in.The Cellular Overview is available at any Web site running the Pathway Tools Web server. The Pathway Tools software is available for free for academic users and for a fee for commercial users. The Cellular Overview can be accessed using a modern Web browser with JavaScript turned on. It has been tested on Internet Explorer, Firefox, Chrome and Safari.ML participated in the design of the online tool and implemented it. PDK participated in the design of the online tool and managed the project. All authors read and approved the final manuscript.


SMPDB contains >600 pathways with nearly 75% of its pathways not found in any other database. All SMPDB pathway diagrams are extensively hyperlinked and include detailed information on the relevant tissues, organs, organelles, subcellular compartments, protein cofactors, protein locations, metabolite locations, chemical structures and protein quaternary structures. Since its last release in 2010, SMPDB has undergone substantial upgrades and significant expansion. In particular, the total number of pathways in SMPDB has grown by >70%. Additionally, every previously entered pathway has been completely redrawn, standardized, corrected, updated and enhanced with additional molecular or cellular information. Many SMPDB pathways now include transporter proteins as well as much more physiological, tissue, target organ and reaction compartment data. Thanks to the development of a standardized pathway drawing tool  all SMPDB pathways are now much more easily drawn and far more rapidly updated. PathWhiz has also allowed all SMPDB pathways to be saved in a BioPAX format. Significant improvements to SMPDB’s visualization interface now make the browsing, selection, recoloring and zooming of pathways far easier and far more intuitive. Because of its utility and breadth of coverage, SMPDB is now integrated into several other databases including HMDB and DrugBank.The Small Molecule Pathway Database (SMPDB,  Biological pathways are the wiring diagrams of life. They provide a rich and surprisingly compact view of how genes, proteins and metabolites work together in cells, tissues or organs. In fact, most of today’s life scientists learned their biochemistry and molecular biology by studying richly annotated, carefully hand-drawn pathway diagrams found in books or on wall charts. With the advent of the internet, many biological pathway diagrams started migrating from the printed page to the web. As a result, there is now a plethora of very popular, very high-quality web-accessible pathway databases such as KEGG , the ‘CyIn an effort to make web-based biological pathway data more visually appealing and physiologically relevant, particularly for biomedical applications, we developed the Small Molecule Pathway Database or SMPDB . The firHowever, the first version of SMPDB was not without some shortcomings. In particular, it was difficult to update and maintain, it was limited in size and scope, its pathways lacked a certain degree of standardization, its physiological information on organelles and organs was incomplete and its visual interactivity (zooming and image navigation) was awkward and somewhat restricted. Furthermore, SMPDB pathway diagrams were only downloadable as static images and not available in standard formats such as BioPAX or SBML . While SWith the release of SMPDB 2.0, we believe we have addressed all of these shortcomings. In particular, we have developed tools to simplify SMPDB’s maintenance and enhance its illustration standards. We have also corrected and added much more physiological  information, substantially improved its visual interactivity and quality, created downloadable BioPAX pathway files for all of SMPDB’s pathways and increased SMPDB’s visibility by integrating SMPDB into DrugBank, HMDB and MetaboAnalyst. Finally, we have substantially expanded the size and scope of SMPDB to include >600 pathways (a 70% increase) and have added a large number of drug metabolism and physiological action pathways. With these enhancements, we believe that SMPDB 2.0 will appeal to a much broader community of researchers and will offer users a much more enriched, interactive and informative experience. A detailed description of SMPDB 2.0 follows.When it was initially released, SMPDB (version 1.0) contained 350 hand-drawn pathways. These small molecule pathways were limited to three general categories: basic metabolism, metabolic diseases and selected drug-action pathways. Today, SMPDB 2.0 contains >600 pathways (a 70% increase) and it now includes six kinds of small molecule pathway categories including: basic metabolism, metabolic diseases, drug-action pathways, drug metabolism pathways, metabolite signaling pathways and physiological action pathways. The inclusion of drug metabolism pathways in SMPDB 2.0 was driven by user requests and the rapidly growing interest in drug metabolism in many fields of drug research and clinical metabolomics. The addition of metabolite signaling and physiological action pathways was motivated by the need to handle a larger number of drug-action pathways. It was also brought on by the growing awareness by members of the metabolomics and nutritional science communities that many metabolic pathways act at the level of organs and tissues and not only within cells. In addition to the significant numerical increase in SMPDB’s size and scope, many of the pathway diagrams in SMPDB 2.0 have been enhanced with additional cellular or physiological information. Now nearly every SMPDB pathway includes information about where the reactions occur (cellular compartment(s), organs or tissues), the site/organ of action for drugs (for drug-action pathways) or toxic metabolites (for metabolic disorders) as well as information on key membrane-bound transporters . This kind of physiological or extracellular information is rarely captured or displayed in other online pathway databases. However, because SMPDB is a human-only pathway resource, this sort of physiological, cellular, biochemical or biomedical information is particularly important. Consequently, considerable effort over the past 3 years has been put into capturing and displaying these data.As with its predecessor, SMPDB 2.0 is still strictly focused on curating human-only, small molecule-only pathways. Therefore, SMPDB only displays those pathways where small molecules  play key roles and/or where small molecules represent a significant proportion of functional entities in a given pathway. All of the pathways in SMPDB 2.0 still retain the standard  database functionalities of the original database, including hyperlinked structures (to HMDB or DrugBank), hyperlinked protein images (to UniProt), text searching utilities (TextQuery), chemical searching utilities (ChemQuery), sequence searching utilities (Sequence Search), pathway browsing capabilities (SMP-Browse with filtering options), detailed pathway descriptions, extensive references, metabolite highlighting capabilities , pathway legends and metabolite mapping for metabolomic applications (SMP-MAP). Additional details about these functions and how they can be used are provided in the original SMPDB paper. An updated and detailed comparison of SMPDB 2.0 to its predecessor (SMPDB 1.0) and to other pathway databases is provided in One of the distinguishing features of SMPDB has been the strong focus on providing users with high-quality, artistically pleasing pathway diagrams that were not only correct and informative but also colorful, interactive and richly detailed. This continues to be a major focus for SMPDB 2.0. To this end, a significant effort over the past year has been put into enhancing the visual displays and interactivity in SMPDB. The previous version of SMPDB only offered a three-step zooming capability that was further hindered by the requirement that users navigate through the zoomed-in images using multiple scroll bars. This restricted zooming capability proved to be both awkward and inconvenient for many users. Likewise, the placement of the pathway descriptions and references (above and below the pathway images) limited how much of a pathway could be viewed on a web page or computer screen. In addition, the size of the chemical structures (too large), the text labels  and the pathway arrows (too thin) was often problematic for a number of pathways, depending on what level of zooming was used. The uniformly dark blue background (to indicate an aqueous environment) was also challenging for those wishing to print SMPDB pathways on paper, for preparing slides and for visualizing certain features or distinguishing among subcellular locations.http://en.wikipedia.org/wiki/Google_Maps). This enhancement now allows rapid and continuous zooming using a mouse scroll wheel or through simply clicking on-screen zoom icons. It also allows facile navigation around zoomed-in pathway diagrams in SMPDB 2.0 through a simple click-and-drag operation or through clicking on-screen up/down or left/right arrows located near the on-screen zoom functions. A full-screen view of each SMPDB map is also available. This view can be toggled off and on by clicking the full-screen icon located on SMPDB’s new information and control panel (located on the right side of each pathway map). The information and control panel allows users to easily click on labeled buttons to view text (pathway descriptions and references), highlight (SMP-Highlight), annotate concentration data , download files and adjust image settings. By moving the old text boxes containing pathway descriptions and pathway references to SMPDB 2.0’s information and control panel, more on-screen real estate is now available for viewing pathway diagrams. Furthermore, by resizing, recoloring and standardizing the size of the chemical structures, arrows and text in all of SMPDB’s pathway images, most features under most zooming conditions should be much more visible and easily discerned. Using the image settings located in the control panel, users can also adjust the background colors (from blue to white) and the cellular membrane display (simple versus detailed) to facilitate printing, image capture or slide preparation. The recoloring process has also been extended to other parts of SMPDB as well. In particular, all reactions that take place in certain organelles or subcellular locations have now been recolored so that the organelle’s background color serves as the background color for the (zoomed-in) reaction. This should make compartment-specific reactions or pathways far more distinctive and discernable. Another enhancement to SMPDB is now available through the home page where a scrollable ‘carousel’ of SMPDB pathway images is available. This carousel, which can be scrolled through by simply ‘swiping’ the mouse over the images, will allow users to view and select newly loaded or recently updated SMPDB pathway maps. A montage of many of these visualization features and enhancements is shown in For SMPDB 2.0, a completely new visualization engine was built using scalable vector graphics (SVG) and a web interface technology inspired by Google Maps . Each PowerPoint image was then converted to three differently sized PNG images  and manually image-mapped to appropriate database links. Prior to the image mapping process, pathway constituents  were manually identified, their database IDs manually determined and each data point entered in a separate file for each SMPDB pathway. These text files were used to permit text and structure searching as well as compound highlighting (via SMP-MAP and SMP-Highlight). This manually intensive process proved to be arduous, time-consuming and error prone. The many different skills and multiple steps required to generate and post a SMPDB pathway meant that a team of more than 10 differently trained individuals were needed to construct, update and maintain the database. Even with SOPs and a standardized icon library, some individual variability occurred with regard to pathway layout, pathway details, icon placement, directional arrow size and other image features. Additionally, the manual placement of image-mapped hyperlinks meant that some links were placed imprecisely, leading to off-centre effects that became further exacerbated through zooming. If SMPDB pathways needed to be updated or corrected, multiple images still had to be generated and multiple image maps still had to be prepared. If a large number of highly similar pathways were involved (such as drug mechanism pathways) a simple, single molecule update could often take many hours of repetitive, error-prone editing. Because all of SMPDB’s pathways were essentially image-mapped pictures it was not possible to easily render them into BioPAX or SBML formats. As a result, efforts to convert SMPDB pathways into BioPAX or SBML equivalents were stymied.To simplify the updating process and improve the level of standardization and figure rendering consistency for SMPDB 2.0, we developed an online pathway illustration and editing tool, called PathWhiz (manuscript in preparation). PathWhiz, which is a combination of an online digital painting program and a webpage building program, allowed SMPDB curators to digitally render, update and annotate pathways in a fraction of the time that the old ‘analog’ approach required. PathWhiz also allowed our curation team to produce far more consistently rendered pathways and consistently sized structures that looked as if they all came from a single artist. PathWhiz uses a pull-down library of standard image icons for membranes, membrane compartments, organs, tissues, organelles, membranes and proteins to help accelerate the pathway illustration process. It also has a standard set of arrows and arrow-drawing utilities that allow all SMPDB reaction components to be linked or connected in a uniform, visually pleasing fashion. Because PathWhiz also captures icon placement and annotation information digitally, it allowed all hyperlinks to be precisely mapped, all pathway images to be rendered as SVG and all pathway component information  to be readily captured. The updating process is also made much easier as all changes in one pathway  can be digitally transferred to all other similar pathways at the click of a button. The digital nature of the drawing and icon placement process through PathWhiz meant that it also became very easy to convert all SMPDB 2.0 pathways into BioPAX format. Conversion of the SMPDB pathways to SBML is underway now and should be completed before 2014. Thanks to PathWhiz, all SMPDB pathways have been significantly updated, corrected and improved. Likewise many new pathways were able to be added in a fraction of the time (compared to the old approach), allowing a significant increase in both the quantity and quality of pathways in SMPDB 2.0.Another important, and increasingly unique, feature of SMPDB is the open availability of its data. In particular, SMPDB supports both data downloads and image downloads for all of its pathways. The data files include protein and gene sequences for all the annotated proteins and genes in SMPDB. SMPDB downloads also include all metabolite and drug structures (in SDF format) as well as CSV files containing different combinations of compound names or protein names linked to pathways and database identifiers. The image files (in SMPDB 1.0) included both the original PowerPoint files and the differently sized  PNG files corresponding to each pathway. With the release of SMPDB 2.0 the same kind of data download information is still available, although the number of sequences, structures and database links is now substantially greater. In addition, SMPDB’s data downloads now include all of its pathway information in BioPAX format. BioPAX is an RDF/OWL-based standard exchange language designed to compactly represent biological pathways at the molecular and cellular level . The avaAs noted earlier, every pathway in SMPDB 2.0 has been redrawn using the new PathWhiz rendering tool. This effort allowed us to revisit all of SMPDB’s pathways and to update them with new information and to correct, improve or reformat preexisting pathways so that they were more consistent and informative. Many pathways were improved through the addition of more organelle and reaction compartment information. Others were enhanced through the addition of target organ information and protein transporter information. Likewise images that were too small, arrows that were too thin and pathways that were too convoluted were corrected and/or simplified. In redrawing old pathways and rendering new pathways in SMPDB 2.0, the same level of quality assurance, the same referential resources and the same kinds of data checking/validation routines were used as described in the original SMPDB paper . In partWhen SMPDB was first released, it was primarily a stand-alone database that simply accessed other databases through its hyperlinks to HMDB, DrugBank and UniProt. Over the past 2 years, efforts have been directed at integrating SMPDB into a number of other popular online databases, so that effectively other databases now access SMPDB. In particular, SMPDB is now linked into the pathway data fields of the latest version of HMDB  and the Saccharomyces cerevisae, Escherichia coli, Arabidopsis and Drosophila. With the latest additions and enhancements in SMPDB 2.0, we believe that SMPDB has reached a critical threshold giving it sufficient breadth, depth and interconnectivity so that it will appeal to a much larger community of users. Overall, SMPDB 2.0’s new, colorful, informative, artfully designed pathway diagrams, combined with its wide range of visualization, annotation and querying tools should provide users a much more enriched, interactive and informative pathway viewing experience.SMPDB continues to expand with new pathways being added at an almost daily rate. Over the coming 2–3 years, we expect that the number of pathways in SMPDB will likely double. It is likely that the greatest growth in SMPDB’s pathways will be in the drug and drug metabolism pathways, as there are literally thousands of reactions, reactants and pathways that are readily available in the literature. With the impending release of PathWhiz (SMPDB’s pathway drawing tool and editor), we are hopeful that many of SMPDB’s new pathways will actually be added by interested community members. It is also expected that PathWhiz will enable SMPDB-like pathways and pathway databases to be easily generated for other model organisms such as Canadian Institutes for Health Research (CIHR), Alberta Innovates Health Solutions (AIHS) and Genome Alberta, a division of Genome Canada. Funding for open access charge: Genome Canada.Conflict of interest statement. None declared.


A common class of biomedical analysis is to explore expression data from high throughput experiments for the purpose of uncovering functional relationships that can lead to a hypothesis about mechanisms of a disease. We call this analysis expression driven, -omics hypothesizing. In it, scientists use interactive data visualizations and read deeply in the research literature. Little is known, however, about the actual flow of reasoning and behaviors (sense making) that scientists enact in this analysis, end-to-end. Understanding this flow is important because if bioinformatics tools are to be truly useful they must support it. Sense making models of visual analytics in other domains have been developed and used to inform the design of useful and usable tools. We believe they would be helpful in bioinformatics. To characterize the sense making involved in expression-driven, -omics hypothesizing, we conducted an in-depth observational study of one scientist as she engaged in this analysis over six months. From findings, we abstracted a preliminary sense making model. Here we describe its stages and suggest guidelines for developing visualization tools that we derived from this case. A single case cannot be generalized. But we offer our findings, sense making model and case-based tool guidelines as a first step toward increasing interest and further research in the bioinformatics field on scientists’ analytical workflows and their implications for tool design. A common class of exploratory analysis is to examine the functions of differentially expressed genes from high throughput experiments using interactive protein-protein interactions networks, visual pathways, and other bioinformatics applications. The goal is to generate a hypothesis about molecular mechanisms of a disease. We call this analysis expression-driven, -omics hypothesizing. Despite the availability of many interactive data visualizations to support the graphic portions of this analysis most of the tools do not adequately address scientists’ actual analytical needs and practices. They need to be more useful and usable and better integrated .In other domains, improvements in data visualization designs have relied on models of analysts’ actual sense making for a complex analysis . A senseIn the visual analytics research literature Pirolli and Card’s sense making model of intelligence analysis is a seminal model that has led to positive tool designs and has inspired other models in different domains -12. UnfoOur goal is to empirically capture the end-to-end sense making flow of expression-driven, -omics hypothesizing. Working towards this goal is a continuous project. Here we report on a preliminary model that we derived from an initial field-based observational study. Observational research is an appropriate first step when knowledge is slight - as it is with this workflow . The benWe studied in-depth one biomedical researcher as she engaged in multiple stages of expression-driven, -omics hypothesizing over a six month span. To our knowledge, our results are the first end-to-end description of sense making for this class of analysis – that is, its actual looped analytical processes and intentions; behaviors and reasoning with visualizations; engagement with numerous tools ; research into the literature coupled with visual analytics; and various modes and content of note-taking. We recognize that this one case cannot be generalized. Yet we believe that it is a first step and can inform further research toward this end. Moreover, to lend reliability to our findings we tied them whenever possible to relevant results from our own prior research and from studies by others -18. ThisWe abstracted six stages of end-to-end sense making and roughly quantified the duration of each based on the case study researcher’s demonstrated and reported engagement. To represent these stages visually, we adapted Pirolli and Card’s graphic representation of a sense making model expression-driven, -omics hypothesizing  graphic framework for capturing and naming visual analytic tasks . This fr13 graphiAs a caveat, neither our prose descriptions nor figures of stages can fully capture the iterative and opportunistic analysis that actually occurs within and across stages. Nor do they do justice to researchers’ interleaving of metacognitive processes with logical analysis. Metacognitive processes involve efforts to manage evolving knowledge; monitor progress; and validate sources, data, and interpretations. For a narrative retelling of the exploratory analysis and scientific context see Additional file In the case study, calculations of weeks spent in each sense making stage  but just a week of overall time.The calculations show that for 87 percent of the sense making flow the researcher was involved in the other four stages. Notably, it was in these other stages that she did the lion’s share of explanatory reasoning, with occasional returns to the visualization programs and views that had helped prepare her for this reasoning.Stage 1: Characterize genes conceptually. The analytical goal of this stage is to reduce the initial expression genes to a subset based on functional attributes relevant to the research problem  networks tool on a list of pre-processed  expression genes. The tool displays interactors and annotates genes (nodes) and interactions (edges) by attributes and/or derived values. Researchers read the graph and learn what gene products interact directly and indirectly [– directly .Forage for information on conceptual traits of nodes and edges. Researchers use a tool’s dynamically linked data tables and network to see the annotations of nodes and edges [– nd edges . They seFilter on edge attributes: Networks often can be reduced dramatically by filtering to only select edge attributes, e.g. sources citing interactions (e.g. pathway and disease databases), evidentiary strength (e.g. cited by three or more articles) or directionality. The graph then becomes more tractable [– ractable .Group, sub-group, and re-group nodes by function and filter by function, as needed: Part of foraging for relevant attributes involves sorting tables and arranging networks into similarly annotated subnetworks. Much experimenting ensues to determine the most relevant groupings and to get clean borders. Many changes in layout occur [– ut occur . ResearcOvercome obstacles to achieving the right sized subset for further inquiry. If a subnetwork of interest (e.g. a metabolism group) is highly connected it is difficult to subdivide further. To reduce highly connected networks researchers may read the member genes into another more stringently curated program. The high stringency reduces the number of interactions. Conversely, for sparse networks researchers may expand nodes to nearest neighbors and repeat “Characterize” methods on results [–  results .Stage 2: Contextualize related genes in biological events. Using the subset selected in the previous stage the goal is to uncover broad outlines for a few biological stories involving some of the interactors in the subset. Researchers examine pairs of interactors , one pair at a time, in pathway contexts and biological events. This stage also has the goal of retrieving relevant citations during these contextual explorations. Citations collected now expedite literature searches later in subsequent stages.This stage is more complex than “Characterize.” It involves close analysis of high dimensional, multi-scale relationships coupled with ongoing assessments of their relevance, potential novelty, cumulative implications, and credibility  and sub-pathway (s), with the query gene (s) overlaid and highlighted on them [–  on them .Examine the role each pairwise interaction plays in context. Researchers examine interactors at multiple scales and relate them to up and down stream regulatory events, other molecular pathway members, post-translational modifications, and directional  interactions and outcomes.– Validate the distinctiveness of sub-pathways and information they carry on interactors’ roles. Researchers need to understand and validate how the sub-pathways they explore relate to each other, what differs between them, and what criteria a tool uses to sub-divide pathways. To do so, they access external information and place views of sub-pathways and parent pathways side-by side to compare them. Additionally, they refer back to the networks and earlier notes from the “Characterize” stage (Stage 1) to corroborate the interacting molecules with pathway views.– Manage perceptual orientation and trains of thought. Maintaining focus and coherent thought is taxing when examining high dimensional data and multiple scales [– e scales . If reseForage textual information for interactors that suggest an interesting biological story. To put together “pathway stories,” researchers opportunistically shift between textual and visual modes. Ideally, they move seamlessly between visualization tools, PubMed abstracts, NLP annotations, and full texts. They delve into domain content, examining, for example, how normal and abnormal events differ, how transcriptional events affect or respond to translational modifications, and what trigger conditions occur.– Pursue promising unexpected leads by cross-referencing different or earlier tools. Researchers welcome unexpected and potentially interesting findings. They often examine their implications by iterating back to prior moves within the current or prior stage [– or stage .Manage evolving knowledge and ideas by externalizing them in notes and diagrams. To keep track of insights about possible story outlines as they evolve researchers take notes. They may annotate network print-outs, copy and paste excerpts from abstracts, other texts, and citations into laboratory notebooks; and enter additional notes in laboratory notebooks. They format copied data, as needed.– Stage 3 Read for background understanding and biological stories. Reading in earnest now begins The goal is to internalize background knowledge as mental models. These mental models are necessary for subsequently constructing convincing narratives ; and re-read articles several times.– Read portions of articles selectively. Reading focuses primarily on the article Abstract, Introduction, Discussion, and figures. Information of interest ranges from basic facts, such as gene synonyms, to complex relationships, such as interdependencies and modifications among functional relations. Figures illustrating the roles genes of interest play in these complex relationships are especially useful.– Externalize in-progress knowledge to assure understanding and to communicate it to others. Researchers externalize their evolving knowledge to untangle and clarify the dense and multi-faceted relationships cumulatively obtained from the literature [– terature . The extterature -25. ExteStage 4. Read to explain causes/conditions. Stage 4 has the transformative goal of synthesizing cumulative research and knowledge into a causal and conditional biological story – a story from which to hypothesize later [ze later . ReadingCreate new knowledge at a detailed level. Reading mainly examines Methods and Results sections of primary research articles. Researchers follow chains of citations in articles of most interest. Issues of importance involve nuances of change or slight distinctions, e.g., varying effects that minor changes in durations and protein levels have on pathway events and modifications; or causal differences between distinct phenotypes of a disease.– Forage information for elaborations, confirmations, or validations. Frequent references back to earlier PPI and pathway networks occur to help situate potentially interesting interactors in contexts of other genes and pathways and to validate that emerging insights and inferences are credible [– credible . CognitiExternalize knowledge to relate relationships. In addition to continuing with the same externalizing modes as earlier researchers now also deeply study their sketches . They strive to understand how behaviors captured in different diagrams happen and how they connect to one another. Researchers return to prior articles and network views. They record open research questions into their laboratory notebooks.– Cumulatively build a biological story. Researchers read for and build one story at a time. They stick with one if it seems plausible, novel and credible. Otherwise they move on to another possibility.– Stage 5: Schematize and hypothesize. Researchers construct logical schematics to turn rich narratives into explanations [anations ,25. FromExternalize knowledge as schematics. Schematics consolidate and refine the novel biological story that researchers have been composing analytically. Visually, schematics may comprise a set of interrelated causal maps, each depicting part of the story. Schematics are cleaner than earlier hand-sketched causal maps, and researchers may craft them with drawing programs. The diagrams show functional interdependencies, conditions, and causal mechanisms and outcomes, all leading to an explanation of why disease-related processes occurred in the experimental samples.– Fill in evidential gaps by conducting new analyses and relating new and prior findings. Schematics reveal weaknesses in the biological story, and researchers seek stronger evidence – often from the clinical data. Researchers may statistically analyze clinical/ phenotype traits and then correlate outcomes with certain molecular interactions to strengthen the hypothesis.– Stage 6: Read and re-read to compose write-ups of findings. The goal is to find just the information needed to write up findings in targeted sections of a manuscript of proposal : Filter operations are essential to reducing data to a subset for further analysis, e.g. filtering by stringency, by attributes of individual genes, or attributes of subsets. Operations should allow filtering on orthogonal categories (not just terms/keywords) and on a variety of stringency parameters. Expansion operations are also essential, especially when networks are sparse. Operations should allow selective expansion and collapse. Effective and efficient filtering comes into play in the literature reading stages, as well, but in different ways, as detailed below.1. Provide interoperable data exchange : Programs should make it easy for scientists to move between tools (i.e. using output from one as input to another). Blurring the line between input and output helps achieve a smoother flow of analysis. Correspondingly, functionality to export data in a standard format is crucial; especially considering that in “Characterize” the heart failure scientist spent almost as much online time formatting as thinking.2. Support automated but still flexible network layout operations : Manual network layouts can be extraordinarily time-consuming. Yet scientists’ abilities to construct appropriate mental models and to judge relevance and novelty depend on meaningful and accurate layouts. Tool operations should allow users to specify one or more traits for grouping and get layouts of like grouped with like.3. Support the analysis of different relationships in multivariate and multi-scale data : Tools should afford multivariate what, why, and how inquiries; and multi-scale investigations should not spatially disorient scientific users. Overlays of genes on pathways are supported by various tools but few offer adequate support for making distinctions between sub-pathways and molecular roles within them. Also, transitions in displays across pathway hierarchical levels, for example, must be paced to coordinate with scientists’ mental transitions in changing levels. Tools also need to give scientists control over adjusting size and scale for both focus and context views.4. Provide multiple perspectives on the data, especially to foster comparisons : To facilitate mapping across views, tool functionality should enable scientists to mark features or anchors across tools that are important to the research problem. It should also let them synchronize manipulations across views. Ideally, there should be more links and dynamic selection available across tools, perhaps with tools sharing a consistent knowledge base.5. Support the construction of external representations : Many tools do not provide explicit support for the note-taking, diagramming, or annotating of visualizations that scientists value. Visual analytics tools should provide this support, ideally in ways that let scientists access or query these “externalizations” throughout the ongoing investigation. Invariably, scientists will end up taking notes and crafting sketches in many places. Designs for unifying such traces require more knowledge than currently exists about scientists’ patterns of note-taking and mark-ups for different purposes and representations they commonly use for various types of relationships.6. Support finding and annotating relevant documents (Primary literature): Some visualization tools give access to MEDLINE abstracts, NLP, and citations but rarely do they let scientists see and group potentially relevant texts and/or figures by conceptual relationships. Such functionality would be useful. Also, because it is likely that researchers know the sections of articles they want for a particular stage and purpose of reading, it would be expedient to let them select extracts from desired sections when full text NLP is available.7. Record the investigation history (Iteration and knowledge management): Given the numerous times researchers return to earlier moves and tools for a variety of different purposes, tools need to support researchers in keeping track of the ongoing investigation. Tools should automate the recording of investigation history and let researchers add their own annotations and go back to previous states in an investigation. Additionally, the investigation history should be accessible in an intuitive and easy to use interface and not increase a researcher’s cognitive load.8. The proposed guidelines specify necessary support for efficient, effective, coherent, and complete sense making. They do not endorse, however, any specific tools, suites of tools, features, or user interface designs as being better than others. Moreover, some aspects of sense making fall outside the scope of our research design but are nonetheless critical to sense making success. For example, collaborative interactions and collaborative activities to create externalized representations play important roles in clarifying and deepening researchers’ mental models and subsequent inquiry directions. Also outside our scope is an in-depth look at the distinct content structures and processes in reading as well as in-depth modes modes of reasoning involved in moving from description to biological narrative to explanation and hypothesis. We did not directly observe the synthesis and novel insights during Stages 3 and 4 that were critical to explanatory reasoning. Rather our scope only enabled us to uncover, for example, that narratives became prominent when the researcher in the case had to tease apart dynamic relationships and outcomes (beyond pairwise interactions) that were too complicated to hold in mind at once and understand .We believe that future research needs to continue to build out the stages of sense making and corresponding requirements for user-centered tools. It needs to tie visual analytics as well as semantically rich information from text mining to actual sense making. More empirically-based models of other workflows and more refinements and/or alternatives to our own are needed.Sense making models have proven useful for understanding analysts’ application-level tasks and for developing user-centered tools that can facilitate and enhance these tasks in an integrated way. We believe that the model we presented in this paper is an important first step to introduce sense making models to the bioinformatics community. We do not claim that our model accurately represents every researcher’s workflow for analyzing a set of genes and generating a hypothesis to explain disease mechanisms. Instead, we believe that our model accurately represents a subset of researchers’ workflows and can serve as the foundation for future refinements and developments of competing models. Our proposed model provides a “language” that researchers can now use to define tasks and workflows and to build tools that specifically support those identified tasks and workflows as a whole and not only in separation. Therefore, the contribution of our work is not only the sense making model itself but also the introduction of sense making models to the bioinformatics community.For six months we observed and interviewed an IRB-consented biomedical researcher as she worked with expression data to generate a plausible and credible hypothesis about why some non-ischemic heart failure patients failed to respond to beta-blocker treatment. She identified non-responders from her team’s clinical data and explored their richly annotated expression data using a number of bioinformatics and MEDLINE tools. She collaborated offline with her team of heart failure specialists. They generated a hypothesis at the end of the case study, wrote a grant proposal, and began a manuscript.Using Camtasia screen capture software, we gathered 15 hours of video and audio data on the researcher’s interactions with visualization tools and her verbalized thoughts as she worked with the visualizations. We interviewed her once a month for more details about her inquiries and took copious notes. We also interviewed her several times at the end of the case study to validate interpretations and gather more details. . We collected copies of her laboratory notebook  along with 40 pages of other hand-written notes and sketches, and the PubMed articles that she read and annotated. The researcher’s offline collaborations were outside the scope of our analysis.To analyze the visual analytics data, we transcribed the Camtasia tapes and conducted several holistic readings of the transcriptions, interview notes, and relevant portions of the laboratory notebook. We then abstracted patterns and themes relevant to our sense making research goals and informed by relevant theories and frameworks on the cognitive and technological demands of complex problem solving with interactive visualizations . We cateTo analyze the portions of the workflow that involved reading and making sense of PubMed articles we examined the laboratory notebook, annotated articles, hand written notes, and relevant interview responses. We examined 40 abstracts that she cited or copied in her notebook annotated with points of interest and read the corresponding articles for more detail on the issues of interest. We distinguished and characterized types of reading based on analytical intentions, modes of reasoning and note-taking; and focus of attention (i.e. article sections and content). We estimated time on reading-and-thinking based on laboratory notebook entry dates and the researcher’s self-reported durations during interviews. We integrated results from other relevant bioinformatics and visual analytics research with our findings and finalized a sense making model and design guidelines.The authors declare that they have no competing interests.BM designed and carried out the field research, the data collection, and the data analysis. BM and CG collaborated on interpreting findings and determining how to organize and report results. They collaborated on developing, rendering, and refining the sense making model. CG described the implications for tool design. Both authors drafted the manuscript and read and approved the final version.Heart Failure Case Study: Narrative of Progressive Discovery. A detailed narrative specific to the heart failure problem and insights is presented for each stage of sense making. To facilitate recall of each stage and cross-reference from the prose, a simplified rendition of the sense making model is included.Click here for file


CARFMAP is an information-rich interactive tool that enables cardiac biologists to explore the large body of literature in various creative ways. There is surprisingly little overlap between the cardiac fibroblast pathway map, a foreskin fibroblast pathway map, and a whole mouse organism signalling pathway map from the REACTOME database. Among the use cases of CARFMAP is a common task in our cardiac biology laboratory of identifying new genes that are (1) relevant to cardiac literature, and (2) differentially regulated in high-throughput assays. From the expression profiles of mouse cardiac and tail fibroblasts, we employed CARFMAP to characterise cardiac fibroblast pathways. Using CARFMAP in conjunction with transcriptomic data, we generated a stringent list of six genes that would not have been singled out using bioinformatics analyses alone. Experimental validation showed that five genes  are differentially regulated in the cardiac fibroblast. CARFMAP is a powerful tool for systems analyses of cardiac fibroblasts, facilitating systems-level cardiovascular research.The adult mammalian heart contains multiple cell types that work in unison under tightly regulated conditions to maintain homeostasis. Cardiac fibroblasts are a significant and unique population of non-muscle cells in the heart that have recently gained substantial interest in the cardiac biology community. To better understand this renaissance cell, it is essential to systematically survey what has been known in the literature about the cellular and molecular processes involved. We have built CARFMAP ( Therefore, in our Boolean search term, cardiac fibroblasts were put in quotes to avoid retrieving non-cardiac fibroblast articles. The above Boolean search term was input to the NCBI PubMed search field, with the “XML” option as output. For MEDIE, the search term was input to the “subject” field, with the other fields all left blank. For BioIE, the abstracts were written into a text file, and then uploaded to the server. For Whatizit, the list of PubMed IDs was input to the “Place your text/query here” text box. All results were collected as sentences with labelled terms , with threshold raw signal of 1.0. At the end of this process, 6 text files (.txt) were exported for the data normalisation stage.The full R code to reproduce the bioinformatics analysis is available at http://www.r-project.org) using the publicly available Bioconductor packages (bioconductor.org) [read.maimage function to extract the gProcesedSignal values from the GeneSpring exported data files; (2) use avereps.EList function to average the duplicate spots and log-2 transformation; and (3) use normalizeBetweenArrays function to perform quantile normalisation on all arrays.Data normalisation was performed with R (tor.org) . Three plmFit function identifies the genes that have differential expression between 3 CFb samples and 3 TFb samples. At p-value threshold of 0.05, we identified a pool of 3924 differentially expressed entities and these entities were used for fold-change calculation. The fold-changes were converted to numerical colour values (from red to yellow) for CARFMAP visualisation.Differential analysis between CFb and TFb samples was performed using the Bioconductor LIMMA package , which aetc.) and “bio-relations” as the relationships between the bio-entities . We performed algorithm-based text mining by combining three standard tools: MEDIE (http://www.nactem.ac.uk/medie/), Whatizit (http://www.ebi.ac.uk/webservices/whatizit/info.jsf), and BioIE (http://www.bioinf.manchester.ac.uk/dbbrowser/bioie/). These tools were applied in concert in order to redress some specific limitations of each individual approach . Manual curation also ensures the collection of the additional relevant tags related to experimental protocol, the organism being studied, and whether the system is a diseased or homeostatic model .The annotated CFb data forms the building blocks of CARFMAP, with an interactive visualisation interface designed for biologist users . CARFMAPin vivo”, Limited by the existing body of literature, CARFMAP does not yet cover every interaction in CFb, but already the map is too dense to be easily navigated. Thus, filters are available to highlight subsets of the pathway based on selected criteria, greatly improving human-readability. For example, the “extracellular matrix (ECM) remodelling” phenotype is used to highlight relevant nodes, and fade out irrelevant ones . Other tAny cardiac biologist who is interested in exploring the molecular and genetic mechanisms regulating this cell type can utilise the functionalities of CARFMAP. For example, if the interest is on how “Ang II” (angiotensin II) plays a role in “ECM (extra-cellular matrix) remodelling”, this pathway can be traced between these two bio-entities, revealing AT1R (angiotensin II receptor type 1) and MMP-2  as mediators of this function  was comparable to that for CARFMAP, but a major proportion (>80%) of these ~900 PubMed articles do not describe any bio-relation in our automated text mining analysis pipeline. These articles mainly adopt approaches such as epidemiology or clinical trials that do not describe regulatory relationships at the gene or protein levels. The collected bio-relations were converted into a second interactive map  using thIdeally, it would be interesting to derive a pathway for  fibroblasts in other mouse tissues, but there is currently no cell-type specific pathway database available, and it would be a mammoth undertaking with the currently available tools and literature resources. However, if one is looking for organism-wide signalling pathways, there are several online databases describing this information. We thus collected a massive signalling pathway for mouse from the REACTOME database  S2 Fig)S2 Fig). p-value threshold of 0.01  . Interes of 0.01 , leavingetc.). This filtered visualization generates a list of 6 strongly up- and down-regulated gene candidates in CFb  have served as an invaluable resource [Before proceeding with pathway perturbation experiments, it is essential to map out the state-of-art baseline knowledge, connecting the existing evidence into a unified conceptual framework. Most mechanistic models rely on accurate network structure , and manresource , 25, facresource , 27. Howresource , 29, forresource , or the In CARFMAP, we used broad category to describe bio-entities  and bio-relations  to conservatively describe the underlying literature. There is always an inherent risk of false associations or description of the underlying biology from this text mining process, even with manual curation. As such, a typical cardiac biologist user of CARFMAP should look at a particular sub-pathway in CARFMAP using the filter function, then look up the relevant PubMed IDs to evaluate the integrity of the relationships behind it before proceeding any further.In this study, we have limited our scope to the literature body of cardiac fibroblast to determine the baseline state of the heart as a heterocellular system . While mPathways that are unique to CFb will serve as a fundamental characterisation tool for the cardiac research community. However, while CARFMAP describes signalling activities occurring in the context of CFb, as determined from the best available knowledge, it does not immediately follow that the information is CFb-specific. Thus, multiple essential integrity checks were applied to the collected data. Manual curation was used to identify and resolve differently labelled bio-entities . StatistThese findings were further reinforced when CARFMAP was integrated with the transcriptomes of CFb and TFb . DiffereWhile there can be numerous use cases for CARFMAP, we selected and validated a popular and important scenario in which the map was overlaid with transcriptomic data to identify a set of distinguishing genes for the cardiac fibroblasts. Using a standard bioinformatics pipeline alone, researchers would have obtained between 147 and 1733 differentially expressed genes , most ofetc.), none of the existing databases contained cell type-specific interactions, particularly CFb-specific interactions. Among ~60,000 probes from the CFb-TFb microarray datasets, a standard bioinformatics-driven analysis could not single out the same five genes, regardless of the selection criteria  complex network within which the component cells exist and function, rather than studying the genes or proteins individually, or treating all cell types as equal. This also opens the CFb field to a highly active research field of systems biology, where rapid advances have been made in the recent years. CARFMAP is developed based on cardiac biologists’ input, and therefore serves as a biologist-centric tool for experimental design.S1 Appendix(PDF)Click here for additional data file.S1 FigFilters can be enabled in CARFMAP to show only bio-relations obtained (A) two PubMed articles; (B) experiments performed on Mouse; (C) experiments performed in vivo; and (D) experiments performed on homeostatic organisms.(PDF)Click here for additional data file.S2 Figmus musculus” and retrieving the most general pathway. The network was rendered by CellDesigner with the “organic layout” option.SBML source file obtained by querying the REACTOME database for “(PDF)Click here for additional data file.S3 Fig(A) Transcription networks for two TFs: Gata6 and Hoxd8. Networks were constructed based on ChIP-Seq dataset, obtained from online databases . Node colour indicates fold-change in expression between heart and tail fibroblasts. (B) Venn diagram showing the overlap between two TF networks (Gata6 and Hoxd8) or between 7 TF networks . (C) TF networks constructed based on literature mining (for genes with no available ChIP-Seq datasets). (D) Validation (using microarray expressions) of genes of interest from the experiment design pipeline. Means and standard deviation (n = 3) are shown.(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 TableThe criterion that includes all 5 genes (AbsLogFC>2) and has the smallest number of genes (686) is highlighted in green.(PDF)Click here for additional data file.


Interpreting molecular profiles in a biological context requires specialized analysis strategies. Initially, lists of relevant genes were screened to identify enriched concepts associated with pathways or specific molecular processes. However, the shortcoming of interpreting gene lists by using predefined sets of genes has resulted in the development of novel methods that heavily rely on network-based concepts. These algorithms have the advantage that they allow a more holistic view of the signaling properties of the condition under study as well as that they are suitable for integrating different data types like gene expression, gene mutation, and even histological parameters. For many researchers worldwide, unravelling the biology of human tumors, either primary or metastatic, is a daily practice. The development of high-throughput technologies, such as microarrays and next-generation sequencing, has greatly accelerated our understanding of the complex molecular underpinnings of cancer development and progression. Big sequencing consortia like The Cancer Genome Atlas and the International Cancer Genome Consortium provide the research community with an unprecedented wealth of genomic, epigenomic, transcriptomic, and proteomic details of various tumor and cell types –8. The rMany cancer profiling experiments have the common goal of identifying the signal transduction pathways and processes that characterize tumor biology, with the ultimate aim of discovering novel targets for treatment. The current mass production of molecular cancer profiles has spurred the development of novel tools particularly designed for this purpose. Most of these tools build on the concept of gene set enrichment analysis (GSEA), which evaluates whether the overlap between two gene sets is greater than that expected by chance . AnalyzeHowever, novel biological insights have increasingly called into question the classical representation of processes and particularly pathways as hierarchically structured as well as mostly linear diagrams of protein–protein interactions that are sharply and precisely delineated from the broader cellular transduction network. Instead, the now-prevailing understanding of systems biologists is to think of pathways and processes as warm and fuzzy clouds: warm since their representation is close to the truth but not necessarily exact; fuzzy since the membership of components in a pathway is graded and dynamic, and therefore not all components of a pathway are equally important and might vary; and a cloud since the boundaries of a pathway are not sharply defined because, among other reasons, many of the pathways and processes connect to form a network . This neAt present, it is important to introduce two conceptually different approaches to derive biological meaning from molecular profiles. Therefore, consider a gene expression profile from cancer cells treated with a ligand . The gene expression profile can be regarded as a functional read-out from a set of pathways that are activated upon ligand-receptor interaction and that culminate in the activation of transcription factors, which cause expression changes. Methods to reveal these upstream signaling pathways based on expression changes exist and are henceforth termed “bottom-up” approaches. In turn, expression changes will endorse a biological response, for example the induction of angiogenesis. Methods to translate gene expression changes, or any other molecular profile, into downstream biological responses are henceforth termed “top-down” approaches.The top-down approach is essentially initiated on the identified molecular profile . The list of genes then goes through a sequence of (1) network inference, (2) network topology analysis, (3) pathway identification through GSEA, and (4) pathway prioritization using network and pathway statistics identified in steps 2 and 3. Pathway mutual exclusivity and co-occurrence, revealed by evaluating overlaps between sets of pathway-specific genes, may provide extra guidance during pathway prioritization . In the With respect to the analysis strategies outlined above, four remarks should be made. First, intuitively, the bottom-up approach is better for identifying pathways, whereas the top-down approach is more appropriate to evaluate biological processes. Nevertheless, the top-down approach performs equally well in identifying signal transduction pathway activation secondary to the prior molecular changes. Therefore, these two approaches should not be regarded as mutually exclusive with respect to the pathway-process distinction. Second, networks are extremely suited for data integration . TherefoFinally, the outlined analysis strategy does not represent a novel algorithm. Rather, it presents an analysis philosophy that builds on already existing tools, most of which are freely accessible online, such as BioConductor and R packages, Cytoscape plugins, and Java tools like Expression2Kinases .


The PI3K/Akt/mTOR pathway plays an essential role in a wide range of biological functions, including metabolism, macromolecular synthesis, cell growth, proliferation and survival. Its versatility, however, makes it a conspicuous target of many pathogens; and the consequential deregulations of this pathway often lead to complications, such as tumorigenesis, type 2 diabetes and cardiovascular diseases. Molecular targeted therapy, aimed at modulating the deregulated pathway, holds great promise for controlling these diseases, though side effects may be inevitable, given the ubiquity of the pathway in cell functions. Here, we review a variety of factors found to modulate the PI3K/Akt/mTOR pathway, including gene mutations, certain metabolites, inflammatory factors, chemical toxicants, drugs found to rectify the pathway, as well as viruses that hijack the pathway for their own synthetic purposes. Furthermore, this evidence of PI3K/Akt/mTOR pathway alteration and related pathogenesis has inspired the exploration of computer-aided targeting of this pathway to optimize therapeutic strategies. Herein, we discuss several possible options, using computer-aided targeting, to reduce the toxicity of molecularly-targeted therapy, including mathematical modeling, to reveal system-level control mechanisms and to confer a low-dosage combination therapy, the potential of PP2A as a therapeutic target, the formulation of parameters to identify patients who would most benefit from specific targeted therapies and molecular dynamics simulations and docking studies to discover drugs that are isoform specific or mutation selective so as to avoid undesired broad inhibitions. We hope this review will stimulate novel ideas for pharmaceutical discovery and deepen our understanding of curability and toxicity by targeting the PI3K/Akt/mTOR pathway. Conversely, the phosphorylated Akt (pAkt) can be dephosphorylated by protein phosphatase 2A (PP2A) and the pleckstrin homology domain leucine-rich repeat protein phosphatase (PHLPP) /[Akt]total is the percentage of phospho-Akt.To reveal such regulatory mechanisms, the backbone structure of the PI3K/Akt/mTOR pathway must first be obtained to filter out unimportant information. Currently, there exist sufficient data to evaluate the backbone structure, on the basis of which a simplified mathematical model can be built to realistically simulate both the normal function and the pathologic alterations of the pathway. Such a model may be useful to test strategies to rectify a deranged pathway. Our previous studies have constructed such a backbone network and its computational model to facilitate the systematic study of the PI3K/Akt/mTOR pathway ,128. TheThe regulatory mechanisms may reflect key features of physiologic/pathologic phenotypes. That is, the shape of the response curve may contain information about the pathway functioning: a normal response may correspond to one kind of shape, and a pathologic response may correspond to another. Of course, such a mathematical model cannot be expected to reveal all of the complexities of diseases or normal functions, but the insights gained are valuable and may provide crucial insights into further investigations. This is especially true when one only considers the latency phase of diseases, during which non-essential body reactions do not happen.in silico through numerical simulation. One first assigns a set of parameter values to the model and then runs the model to obtain a response curve A(I). By sampling of the parameter space for sufficiently long, one can obtain a broad canvas of the biologically relevant responses with certain confidence. However, because the search depends on sampling and the search size scales exponentially with the number of parameters, it cannot be guaranteed that all phenotypes will be found. More importantly, simulation studies without analysis do not readily reveal system-level regulatory mechanisms or illuminate connections between various phenotypes.With the simplified model, regulatory mechanisms, as visualized in the resultant response curves, can be discovered To overcome this difficulty, a novel approach was developed to analyze the pathway ,128, whiPK and dPK. The parameter β positively correlates with EP/EdP. Assuming EdP is a constant, then β positively correlates with EP, which, in turn, positively correlates with the strength of the edge IRS1 → EP . The parameter α = φ − ψ represents the difference between φ  and ψ . The parameters:i.e., PK = dPK = K), the number of parameters reduce to three: β, α and K.Mathematical analysis revealed that the parameter space of the pathway can be well characterized. Although there are many parameters in the model, it was found that several parameters always group together to form a composite parameter, assuming that the system is in the steady state. In total, there are only four such composite parameters: β, α, K should not be too large. According to numerical simulations, it was found that K ≤ 0.05 provided sufficient resolution irection . The matand mTOR ,128.Our discussion thus far may give the impression that α and β are mTORC1 and PI3K activities, respectively. In fact, α and β are composite parameters, which also involve molecules other than mTORC1 and PI3K. These molecules may also be targeted for combinatorial therapy.dP, has not been fully explored in the mathematical model. However, it can be predicted that the modulation of EdP would render a parallel shift of the response curve, because the parameter β negatively correlates with EdP. That is, the increase (decrease) of EdP would shift the response curve to the right (left). Although it is up to future data enrichment, the EdP wing  and X-ray crystallographic structures, well-defined binding pockets can be revealed by molecular modeling . Yet, unPTEN mutation [In total, there are eight PI3K enzymes, which are grouped into three classes, among which, class I is the most important to cancer. Class I PI3K consists of four members, PI3Kα, PI3Kβ, PI3Kγ and PI3Kδ. They are each comprised of a 110-kDa catalytic subunit, p110α, p110β, p110γ and p110δ and a regulatory subunit. The activation mechanisms of the four isoforms of PI3K are different, yet they crosstalk with one another . The firmutation ,149; PI3mutation ; PI3Kδ imutation . In addimutation .in silico [MD simulation and its subsequent studies provided valuable tools for the development of isoform-specific PI3K inhibitors, complementing the molecular modeling method . It has n silico ,154. So n silico . For exan silico ,156; p11n silico ; GS-1101n silico . Furthern silico , whereasn silico ,160.in silico docking and MD simulation study of 33 PI3K inhibitors binding to conformations of the wild-type and mutant PI3Kα has demonstrated the conformation differences between an H1047 mutation and wild-type p110α for mutant-PI3Kα-specific drug design [PIK3CA-mutated xenograft tumor to chemotherapy [Of the four class I PI3Ks, only PI3Kα has been frequently mutated in human cancers . Three hg design . Other sg design ,163. Theotherapy . FurtherThere are also studies using MD simulation to explore inhibitors for other components in the PI3K/Akt/mTOR pathway, such as Akt and mTOR ,167,168.in silico simulation of different isoforms and mutated conformations of the pathway components may help design more highly selective drugs, achieving better therapeutic effects with less toxicity.The PI3K/Akt/mTOR pathway has essential pro-survival and pro-growth functions in the cell, amongst a variety of other cellular functions. The perturbation of this important pathway is associated with many diseases. We reviewed a number of alterations of this pathway and the pathological consequences, including genetic mutations, viruses, chemical toxicants, metabolites and inflammatory factors. Over-activation of this pathway is essential for the survival and proliferation of the malignant cells and is therefore a feature of many cancers. Toxicology studies find that the PI3K/Akt/mTOR pathway is commonly targeted by toxins and viruses to produce disease, but also suggest that the pathway is pharmaceutically modifiable. A new generation of therapy, aiming at rectifying the pathological alterations of this pathway, is under vigorous development. It has already been demonstrated that PI3K/Akt/mTOR targeted therapy inhibits components of the pathway and shows therapeutic efficacy, albeit with a variety of toxic side effects. Thus, a thorough understanding of this pathway, with a systemic perspective and computational modeling, is important. Here, we reviewed the current status of computational-aided targeting of this pathway, including mathematical modeling at the system level, as well as MD simulations at the molecular level. For future applications, further analysis of the dynamics of normal and pathologically-altered pathway signaling, coupled with pharmacodynamics characterizations, presents new opportunities to compose therapy optimized for rectification of the aberrant pathway. Computational analysis of patient cell sampling may help predict metabolic toxicity by molecular targeted inhibition. Finally,