Metabolic pathway diagrams are a classical way of visualizing a linked cascade of biochemical reactions. However, to understand some biochemical situations, viewing a single pathway is insufficient, whereas viewing the entire metabolic network results in information overload. How do we enable scientists to rapidly construct personalized multi-pathway diagrams that depict a desired collection of interacting pathways that emphasize particular pathway interactions?Escherichia coli metabolic model.We define software for constructing personalized multi-pathway diagrams called pathway-collages using a combination of manual and automatic layouts. The user specifies a set of pathways of interest for the collage from a Pathway/Genome Database. Layouts for the individual pathways are generated by the Pathway Tools software, and are sent to a Javascript Pathway Collage application implemented using Cytoscape.js. That application allows the user to re-position pathways; define connections between pathways; change visual style parameters; and paint metabolomics, gene expression, and reaction flux data onto the collage to obtain a desired multi-pathway diagram. We demonstrate the use of pathway collages in two application areas: a metabolomics study of pathogen drug response, and an Pathway collages enable facile construction of personalized multi-pathway diagrams. However, individual metabolic pathways are embedded in a larger cellular biochemical network — connections among pathways abound, giving rise to high visual complexity. This article describes a new software tool that is a component of our Pathway Tools software  for assipersonalized multi-pathway diagrams that is identified (but not solved) in [Pathway collage diagrams are assembled from a combination of automated and manual layout mechanisms to enable the user to create a visually simplified, publication-quality figure that satisfies a variety of layout constraints. Put another way, pathway collages fulfill the use case of creating lved) in . A persolved) in .super pathways, which are database-defined assemblies of individual pathways. Super pathways and pathway collages are similar in that both depict connected sets of pathways, however, super-pathway layouts are generated automatically, and thus users have essentially no control over their layout. Also, the layout algorithms used to depict super pathways do not scale to large collections (roughly more than six) of pathways. We next developed software to generate diagrams containing the entire metabolic network of an organism; but often the full network contains far more information than is relevant to a user in a given situation. In contrast to these prior tools, pathway collages aim to depict a subset of the metabolic network in a configuration and graphical style specified by the user, possibly overlaid with omics data.Pathway collages fill a gap in the pathway layout services provided by the Pathway Tools software . HistoriIn related work, KEGG maps , like ouThe Collage Viewer application is a web-browser-based JavaScript application that works in concert with Pathway Tools and can be invoked either from a website powered by Pathway Tools in its web-server mode (such as the BioCyc.org website), or from a local Pathway Tools installation running in desktop mode. We consider this ability to use a single implementation of pathway collages to be a major benefit; providing separate implementations for the web and desktop modes of Pathway Tools would be costly.Generation of the initial layout of the pathway collage is performed by Pathway Tools (i.e. running on the BioCyc web server or the desktop application). This approach allows us to reuse the sophisticated Pathway Tools pathway layout algorithms. The resulting diagram is converted to a JSON graph format and transmitted to the Collage Viewer application running in the web browser.Using a browser-based application for user manipulation of the diagram has several advantages over a server-based application. Allowing the majority of interactivity to take place on the user’s machine limits network delays and decreases load on the server. Once generated, collage graphs and the various modifications to them need not be stored on the server — they can be saved as files on the user’s machine so they can be returned to later and/or shared with others. And this approach enables us to take advantage of modern web technologies.The Pathway Collage Viewer is implemented using the Cytoscape.js  open-souOnce the graph has been loaded, most further user interaction is handled solely by the web application, without the need to communicate with the Pathway Tools server. Exceptions occur when additional data must be retrieved from the server via AJAX requests, such as when adding pathways to a collage, or when importing a new omics dataset. Tooltips on nodes and edges also contain hyperlinks that link back to the server. When creating a pathway collage from Pathway Tools running in desktop mode, a lightweight HTTP server is started within Pathway Tools to facilitate handling of any such AJAX requests from a Collage Viewer running on the same machine.Performance and scalabilityThe Pathway Collage Viewer performs best with a small to medium-sized number of pathways. On a Mac laptop using the Chrome browser, pathway collages consisting of 5-10 pathways (roughly 50-100 metabolites and 50-100 enzymes) take about 10 seconds to generate and render from the BioCyc website, and 20 seconds to upload and display a 5-timepoint gene expression dataset. Other user interactions such as zooming are fast with no noticeable lag time. Performance of the application degrades as the number of display elements increases. A pathway collage consisting of 40 pathways (480 metabolites and 425 enzymes) took about 3 minutes to generate and render; uploading and displaying the same gene expression dataset took 14 minutes. When pathway collages reach 100 pathways, with over 1000 metabolites and enzymes, performance deteriorates further and even zooming becomes sluggish.The automatically generated layout and spacing of pathway elements does not change to accommodate omics data. The display of multi-timepoint omics data as rows of colored boxes is expected to fit in to the existing layout . This approach works well for omics datasets with up to about 6 timepoints. For larger numbers of timepoints, spacing becomes constricted, requiring more manual adjustments in order to eliminate overlaps and generate a pleasing display.The user specifies the pathways to be included in the collage based on experimental observations and/or aspects of metabolism of interest.Pathway Tools computes automatic layouts of the individual pathways within the collage, then positions those diagrams next to one another horizontally, and sends that initial layout of the collage to the user’s web browser.The user can interactively reposition pathways within the collage and change the appearance of the pathways. The user imports large-scale datasets that they want to super-impose on the collage.The user saves the collage and/or exports it to an image file.There are five steps to creating and working with a pathway collage: We now present these steps in more detail.Export →Export pathways to Pathway Collage command will generate a pathway collage containing the pathways in the SmartTable.Scientists need flexible ways of identifying the subsets of metabolism to include in a collage based on a variety of criteria relating to their scientific questions and interests. For example, they may want to include all pathways involving one or more genes or metabolites of interest, all pathways whose genes are up-regulated in an omics experiment, or all pathways involved in a single biological process. The most versatile way to assemble a desired set of pathways, therefore, is via the Pathway Tools SmartTables facility  (either Overviews →Select Pathway Subset). On the BioCyc website, users can select pathways to include by name from a checklist of all pathways at [In addition to defining a collage from a SmartTable, users running the locally installed desktop Pathway Tools can interactively build up a list of pathways by clicking on them directly in the cellular overview diagram  and enzyme and gene names, but excludes “side” metabolites . The pathways are then laid out relative to each other, such that pathways in the same functional class are located near each other (this is the same organizing principle as is used when generating the cellular overview diagram), with biosynthetic pathways to the left and degradative pathways to the right, if both are present, and with an attempt to make the diagram as compact as possible. At this time, no attempt is made to place pathways with common metabolites near each other.Once generated, the pathway collage is exported to the collage viewer web browser application.Zooming and panning. Labels for pathways, metabolites, enzymes, and genes automatically appear at zoom levels where they are likely to be readable.Selecting and re-positioning items. Any node can be manually repositioned by clicking and dragging it. If pathway background boxes are visible, clicking and dragging the background box for a pathway drags the entire pathway. To drag multiple nodes together, the user can shift-click on any node to add it to the current selection. Shift-clicking on the background enables bounding-box selection (turn off pathway background boxes to select only the portion of a pathway within the bounding box).Showing connections. When a metabolite appears multiple times in the graph, the user can show a connecting line between a node and all other nodes for the same metabolite. This can be done either for a single metabolite of interest or for all metabolites in one operation.Adding or deleting elements. Although “side” metabolites are omitted by default, side metabolites can be added to any reaction . And, for any metabolite in the graph, the user can request that it be added everywhere it appears as a side metabolite. New metabolite nodes can be repositioned manually after they have been added. For any metabolite, the user can request a menu of other pathways involving that metabolite, and add selected pathways to the collage. The added pathways will be placed below the collage, and will therefore need to be manually repositioned. Any node, edge or pathway can be manually deleted. Nodes representing the same metabolite can be merged.Highlighting and editing. Individual nodes and edges can be highlighted in a user-specified color. The text of metabolite, enzyme and pathway labels can be manually edited.Customization of pathway diagrams. A variety of customization options exist for specifying icon colors and styles, label colors and font sizes, and edge colors and thicknesses. Pathway background boxes can be turned on or off, as can pathway labels, enzyme labels, and display of omics data (see below).A variety of options are available for interactively refining the collage. They fall into the following general categories: Just as highway traffic data can be painted onto Google maps, Pathway Tools and the BioCyc website have supported for several years the overlay of large-scale omics datasets, such as gene expression data, metabolomics data, or reaction flux data, onto the cellular overview diagram and onto individual pathway diagrams. This capability is also available for pathway collages. When generating a pathway collage, Pathway Tools automatically includes the data from the omics dataset that was most recently loaded onto either the cellular overview or onto a pathway display, so that the same data can easily be viewed in multiple contexts. Omics data can also be loaded directly into a pathway collage if there was no omics dataset loaded at the time it was generated, or if the user wishes to change datasets.The display of omics data on a pathway collage depends on whether the data consists of a single data value per gene, metabolite, or reaction, or of multiple data values (such as in a time series experiment). For the single data value display, the relevant enzyme or metabolite node (text and/or icons) and/or reaction edge is colored based on the data value and the specified color scheme. For time series data, we instead create rows of colored boxes see Fig.  that appAt any time, a pathway collage can be saved as a JSON-format (JavaScript Object Notation) graph file on the user’s computer; that file can later be loaded back in to the collage viewer .A pathway collage can also be exported to a PNG-format image file for use in presentations or publications. The image will be generated with a resolution comparable to that of the display at the time the image is created (up to some maximum), therefore, the highest-quality images are obtained if the collage is displayed at a high zoom level when exporting.Escherichia coli metabolic model.We demonstrate the use of pathway collages in two application areas: a metabolomics study of pathogen drug response, and an Export → Export pathways to Pathway Collage in the right-sidebar menu.To build your own pathway collage, first create a BioCyc.org SmartTable containing your pathways of interest (for documentation on SmartTables see ), or staHelicobacter pylori, enabling us to observe the early phase of drug action and to identify the unique, drug-specific effects on pathogen metabolism versus more general stress responses and late-onset drug effects. Following a global metabolomics study, one can focus on statistically significant metabolic changes in the data and compare these changes between parallel treatments to arrive at a discrete set of metabolites uniquely disrupted by a given drug for subsequent analysis using databases and metabolic analysis tools.Metabolomics has emerged as a front line technology for interrogating biological systems, particularly to gain insights into the mode of action of drugs . MetabolMetabolomics data sets can be complex, even when data reduction steps are taken, making data analysis a major challenge to deriving biological insights from the data.Using Pathway Tools’ prior SmartTable feature, one can systematically survey existing biochemical knowledge of the reactions and pathways involving metabolites that change during drug exposure. With SmartTables, one can define a table of those metabolites implicated in the drug interaction, and then add columns enumerating all reactions and pathways involving each metabolite to seek new patterns in the data. Metabolomics data can also be visualized on the cellular overview of a pathogen, to map drug-induced perturbations onto its full metabolic map for visual investigation of global changes to metabolism see Fig. . AlthougPathway collages provide an essential tool to enable one to construct a user-defined subset of metabolism to interactively visualize metabolomics data to aid interpretation. In this example, we examine metabolites that significantly change in a bacterial pathogen in response to drug specific perturbations. The first step in defining the collage was to construct a SmartTable containing these metabolites, and to transform the metabolite column into pathways that contain each metabolite. The pathways column, in turn, was exported to the Pathway Collage application to generate a subset of metabolism of relevance to our biological question, resulting in Fig. Examination of the “mature” edited collage reveals key metabolic changes at the intersections of numerous pathways. For example, nucleotide salvage is most affected in our drug-exposure experiment, and adenine specifically is critical to these effects, given that this metabolite is at the cross roads of several pathways Fig. . Visual E. coli metabolic model derived from the EcoCyc database [E. coli transitions from anaerobic to aerobic conditions.Genome-scale metabolic models compute steady-state fluxes through the cellular metabolic network. Pathway collages can aid us in understanding the data produced from multiple metabolic model simulations. Using the database , called O2 availability is varied from 0 to 4 mmol/gDW/hr in increments of 1 mmol/gDW/hr. Blue arrows indicate reactions. The colored series of boxes next to each reaction indicate the level of flux through each reaction at each level of oxygen availability. The leftmost box in each reaction label indicates flux under conditions of zero O2 availability; the rightmost box indicates flux at a saturated O2 availability of 5 mmol/gDW/hr. The legend on the left describes the flux level associated with each box color. Green lines indicate metabolite connections between pathways.During this transition see Fig. , glucoseO2 concentration increases, flux through glycolysis is fairly constant, whereas fermentative pathways shut down with increasing availability of oxygen (respiratory pathways activate). The pentose phosphate pathway shows a small increase in flux as O2 concentration increases due to increased NADPH requirements for biosynthesis. The rate of L-glutamate biosynthesis also increases because the higher aerobic cellular growth rate increases demand for L-glutamate for biosynthesis. Acetate, ethanol, and formate-derived CO2/hydrogen are produced in large quantities during anaerobic fermentation. This mixture shifts to acetate production during aerobic glycolysis, followed by completely aerobic respiration at saturating levels of O2. Pyruvate formate lyase activity under fermentative conditions transitions to pyruvate dehydrogenase activity under respiratory conditions.The figure shows that as The Pathway Collage application is an extension to Pathway Tools that allows users to quickly define high-quality multi-pathway diagrams that enable insights to a variety of data, from metabolomics data to metabolic flux predictions. Our approach to the problem of defining personalized pathways combines automated layout — which saves the user time and produces high-quality diagrams of individual pathways — with manual re-positioning of pathways that gives the user extensive control over the arrangement of pathways to emphasize connections that the user deems important. Users define the set of pathways within a collage using SmartTables or by selection from the cellular overview diagram. Additional elements of our approach include the provision of extensive user-defined pathway style parameters, semantic zooming  of collage diagrams, and the ability to paint numeric data  over metabolite nodes and reaction edges within the collage.


Do genome-wide association study (GWAS) data for endometriosis provide insight into novel biological pathways associated with its pathogenesis?GWAS analysis uncovered multiple pathways that are statistically enriched for genetic association signals, analysis of Stage A disease highlighted a novel variant in MAP3K4, while top pathways significantly associated with all endometriosis and Stage A disease included several mitogen-activated protein kinase (MAPK)-related pathways.Endometriosis is a complex disease with an estimated heritability of 50%. To date, GWAS revealed 10 genomic regions associated with endometriosis, explaining <4% of heritability, while half of the heritability is estimated to be due to common risk variants. Pathway analyses combine the evidence of single variants into gene-based measures, leveraging the aggregate effect of variants in genes and uncovering biological pathways involved in disease pathogenesis.n = 1686) and B  cases separately. The identified significant pathways were compared with pathways previously investigated in the literature through candidate association studies.Pathway analysis was conducted utilizing the International Endogene Consortium GWAS data, comprising 3194 surgically confirmed endometriosis cases and 7060 controls of European ancestry with genotype data imputed up to 1000 Genomes Phase three reference panel. GWAS was performed for all endometriosis cases and for Stage A (revised American Fertility Society (rAFS) I/II, The most comprehensive biological pathway databases, MSigDB  and PANTHER were utilized to test for enrichment of genetic variants associated with endometriosis. Statistical enrichment analysis was performed using the MAGENTA  software.P = 6.45 × 10−8, OR = 1.71, 95% CI = 1.23–2.37), an intronic single-nucleotide polymorphism (SNP) within MAP3K4. This SNP was not associated with Stage B disease (P = 0.086). MAP3K4 was also shown to be differentially expressed in eutopic endometrium between Stage A endometriosis cases and controls (P = 3.8 × 10−4), but not with Stage B disease (P = 0.26). A total of 14 pathways enriched with genetic endometriosis associations were identified -P < 0.05). The pathways associated with any endometriosis were Grb2-Sos provides linkage to MAPK signaling for integrins pathway , Wnt signaling  and p130Cas linkage to MAPK signaling for integrins pathway ; with Stage A endometriosis: extracellular signal-regulated kinase (ERK)1 ERK2 MAPK  and with Stage B endometriosis: two overlapping pathways that related to extracellular matrix biology—Core matrisome  and ECM glycoproteins . Genes arising from endometriosis candidate gene studies performed to date were enriched for Interleukin signaling pathway (P = 2.3 × 10−12), Apoptosis signaling pathway (P = 9.7 × 10−9) and Gonadotropin releasing hormone receptor pathway (P = 1.2 × 10−6); however, these pathways did not feature in the results based on GWAS data.The first genome-wide association analysis for Stage A endometriosis revealed a novel locus, rs144240142  variants in/near genes that can be assigned to pathways, excluding intergenic variants; (ii) the gene-based pathway definition as registered in the databases; (iii) women of European ancestry.MAPK activation and intracellular ERK/MAPK acting downstream in the MAPK cascade, both acting in the control of cell division, gene expression, cell movement and survival. Other top enriched pathways in Stage B disease include ECM glycoprotein pathways important for extracellular structure and biochemical support. The results highlight the need for increased efforts to understand the functional role of these pathways in endometriosis pathogenesis, including the investigation of the biological effects of the genetic variants on downstream molecular processes in tissue relevant to endometriosis. Additionally, our results offer further support for the hypothesis of at least partially distinct causal pathophysiology for minimal/mild (rAFS I/II) vs. moderate/severe (rAFS III/IV) endometriosis.The top ranked pathways associated with overall and Stage A endometriosis in particular involve integrin-mediated The genome-wide association data and Wellcome Trust Case Control Consortium (WTCCC) were generated through funding from the Wellcome Trust  and the National Health and Medical Research Council (NHMRC) of Australia . N.R. was funded by a grant from the Medical Research Council UK (MR/K011480/1). A.P.M. is a Wellcome Trust Senior Fellow in Basic Biomedical Science (grant WT098017). All authors declare there are no conflicts of interest. Endometriosis is a chronic inflammatory disease in which endometrial-like tissue is located outside the uterus. It is associated with debilitating pelvic pain and reduced fertility, and affects 1.5 M women in the UK (176 M worldwide)  but, crua priori biological hypothesis.One method to improve understanding of complex disease causality is through the identification of genetic factors underlying disease risk, through genome-wide association studies (GWAS). The involvement of genetic factors in the development of endometriosis is supported by numerous studies , and itsThe GWAS for endometriosis conducted to date have identified 10 genome-wide significantly associated variants (P < 5 × 10−8) to identify variants likely to be genuinely associated with a complex disease. Such a stringent threshold is necessary because of the large number of statistical tests being conducted when testing each individual variant for association with the disease; however, it results in true associations not reaching this threshold being missed. One approach to uncover genetic variants associated with the disease under this multiple-testing burden is to aggregate genetic variants passing a pre-defined significance threshold for association with a disease into gene-based measures and to investigate whether the number of associated genes in a given pathway is greater than expected by chance  and Stage B   GWAS data included 3194 surgically confirmed endometriosis cases, and 7060 controls of European ancestry from Australia and the UK  (Painter = 1364) . Austral = 1364) .Cases were genotyped using the Illumina Human670Quad BeadArray, the Australian controls on Illumina Human610Quad and the UK controls on Illumina Human1M-Duo. The genotype data, including the autosomes and chromosome X, were imputed to the latest 1000 Genomes Phase three reference panel (October 2014). Pre-phasing and imputation were performed using SHAPEIT2 (n = 3194), (ii) Stage A cases (n = 1686) and (iii) Stage B cases (n = 1364) vs. controls (n = 7060) to see whether the two sub-phenotypes share similar underlying or have distinct underlying genetic factors in their causation. Logistic regression analysis including a covariate representing the Australian and the UK strata was performed in SNPTESTv2 (n = 8 943 157). QQ plots for all cases, Stage A and Stage B disease associations are provided in Three sets of genome-wide association analysis were performed: (i) All cases (P < 1 × 10−6) were checked for functional evidence in the genomic region in the Encyclopedia of DNA Elements (ENCODE) consortium data  were tested for differential expression using a previously published, publicly available data set (GEO Accession: GSE51981) of eutopic endometrium from 77 endometriosis cases  and 71 endometriosis-free controls assayed using the Affymetrix Human Genome U133 Plus 2.0 Array  the physical size of the gene, (ii) number of SNPs per kilobase for each gene, (iii) estimated number of independent SNPs per gene, (iv) number of recombination hotspots spanning each gene, (v) genetic distance of the gene and (vi) linkage disequilibrium (LD) unit distance per gene.Pathway genetic enrichment analysis was performed for (i) all endometriosis, (ii) Stage A endometriosis, (iii) Stage B endometriosis GWAS results, using MAGENTA  software . MAGENTAhttp://software.broadinstitute.org/gsea/msigdb/index.jsp) is the most extensive resource of gene sets/pathways, currently comprising 13 311 non-independent gene sets. From this database, we downloaded (i) the curated canonical pathways (C2-CP), which include 1330 gene sets amalgamated from BioCarta, Kyoto Encyclopedia of Genes and Genomes (KEGG), Matrisome, Pathway Interaction Database (PID), Reactome, SigmaAldrich (SA), Signaling Gateway (SIG), Signaling Transduction KE (ST) and SuperArray and (ii) Gene Ontology (GO) gene sets, which include 1454 gene sets comprised of biological processes, cellular components, molecular functions. We also downloaded 149 curated canonical pathways from the PANTHER database version 10.0 (n = 2860 (excluding pathways with <10 genes)  and PANTHER pathway database v10.0 (15 December 2015), and used this library in the pathway genetic enrichment analyses . The Mol0 genes) .P-values adjusted for confounding factors. The null hypothesis in the analysis is that the tested genes are randomly distributed in terms of significance  of association with the disease of interest, within each pathway. The alternative hypothesis assumes enrichment above a given rank cutoff compared with multiple random gene sets. The rank cutoff is a pre-determined gene P-value cutoff, and is defined as a given percentile of all gene P-values in the genome. We used a 95% cutoff threshold for significance. Multiple-testing correction was applied through calculation of the false discovery rate (FDR) for resources with >25 genesets/pathways. Results from smaller resources with <25 pathways, which were more likely to be independent of each other, were also adjusted using a Bonferroni correction (P = 0.05/number of pathways tested). The software was obtained from http://www.broadinstitute.org/mpg/magenta/ and ran locally in MATLAB R2013a.MAGENTA employs a Gene Set Enrichment Analysis (GSEA)-like statistic (P < 0.05 after Bonferroni multiple-testing correction) over-representation of the candidate genes were defined as the pathways that have been investigated indirectly through candidate gene association studies (The 122 candidate genes ( studies .n = 3194) or Stage B (n = 1364) (Table P = 2.45 × 10−9 with Stage B disease). The second genome-wide significant locus remained at FN1, though imputation altered the strongest associated SNP from the previously reported rs1250258 (r2 = 0.96) rs1250248 . We found 11 loci (Table P < 1.0 × 10−6), 3 of which were established endometriosis loci  and 8 had not previously been reported. Of these eight loci, three were associated with all endometriosis , and six with Stage B disease .After imputation to the latest 1000 Genomes Phase three reference panel, no novel genome-wide significant associations with overall endometriosis (4) Table  were obsn = 1686 cases) revealed a novel locus, rs144240142 , an intronic SNP with MAF of 0.01, imputation info score >0.8, within the MAP3K4 gene (Table P < 1.0 × 10−6): two INDELS within CDC73 and CSMD1 genes, and two intergenic SNPs near EMX2 and ALCAM genes , is located in known epigenetic regulators , in/near transcription factor binding (TFB) sites for a number of genes, and near a microRNA ; the nearest gene is NFE2L3 , located 290.2 Kb away  while rs144240142 is located near (±1 kb) TFB sites for EGR1, CEBPB, FOSL1 and FOS.For each of the loci, we explored potential functionality using ENCODE (the Encyclopedia Of DNA Elements) Project data  see Mat. The celay Table . MAP3K4 ID4, NFE2L3, MAP3K4, CDC73, CSMD1, FN1, NR2C1and C14orf132 (FDR-corrected P < 0.05). Of these, ID4, NFE2L3and MAP3K4 were also differentially expressed between proliferative, early and mid secretory phases. Differential expression analysis within each phase for these loci showed that ID4 and MAP3K4 were significantly differentially expressed between cases and controls in the proliferative phase, while NFE2L3 was not differentially expressed between cases and controls in any phase. All the loci associated with Stage A disease in our GWAS  were more strongly differentially expressed when the analysis of the expression data set was restricted to Stage I/II cases vs. controls (27 cases vs. 71 controls). For Stage B associated loci, FN1, NR2C1, C14orf132, NFE2L3, we similarly observed stronger evidence of differential expression when analysis was restricted to Stage III/IV cases vs. controls (48 cases vs. 71 controls)  .P < 0.05 or Bonferroni-P < 0.05 ). WNT signaling—a key pathway implicated in our previous GWAS and pathway analyses—was significantly associated with all endometriosis at FDR < 0.05 but not after stringent Bonferroni correction taking account of the number of pathways in the SA database . The top pathways significantly associated with all and Stage A endometriosis were mitogen-activated protein kinases (MAPK)-related, and originated from the large REACTOME database  is a pathway that includes 15 genes, involving integrin clustering by fibronectin stimulation, linked to extracellular signal-regulated kinase (ERK)2 MAPK signaling. The genes enriched for association with endometriosis (P < 0.05) in this pathway were (i) FN1 ; (ii) FGA ; (iii) FGB ; (iv) FGG ; (v) RAP1B  and (vi) SOS1 . The second MAPK-related pathway significantly enriched for association with all endometriosis  through integrin, activating the ERK1 ERK2 MAPK pathway. The two MAPK-related pathways define similar functions as they overlap by 12 genes  database . The top associated genes in this pathway were TRAF3 ; MAP2K1 ; SOS1; (P = 0.019); SHC1 ; CREB5 ; NFKB1 ; and BRAF . The ERK1 ERK2 MAPK pathway overlaps with p130Cas linkage to MAPK signaling for integrins pathway by two genes: GRB2 and SOS1  MAPK pathway. The p38 MAPK pathway overlaps with the ERK1 ERK2 MAPK pathway by 10 genes , while the JNK MAPK pathway overlaps by two genes   in GWAS results for Stage A endometriosis were (Table TRKA receptor (SA) and PIP3 signaling in cardiac myocytes (SIG). G alpha S pathway (ST), Phosphoinositide three kinase pathway (ST), Differentiation pathway in PC12 cells (ST), B cell receptor complexes (SA), Insulin receptor pathway in cardiac myocytes (SIG) and PTEN pathway (SA) were significant at FDR < 0.05 but not after stringent Bonferroni correction .Other pathways significantly enriched (FDR-re Table  TRKA recP and Bonferroni-P < 0.05): ECM glycoproteins  including 196 genes defined by Matrisome Project gene sets (http://web.mit.edu/hyneslab/matrisome/) . The ECM glycoproteins gene set is a subset of the Core Matrisome and thus many of the genes enriched in the endometriosis GWAS overlap. Notably, this includes FN1, FGG and FGA, which are also part of the Reactome MAPK-related pathways associated with all endometriosis , Apoptosis signaling pathway (P = 9.7 × 10−9) and Gonadotropin releasing hormone receptor pathway (P = 1.2 × 10−6). Others covered inflammation-related pathways, cellular homeostatic signaling, thrombolysis, angiogenesis and steroid biosynthesis. Only 2/122 genes were part of a pathway that was highlighted by our GWAS pathway analysis: NFKB1 in ERK1 ERK2 MAPK pathway and CDKN1B in PIP3 signaling in cardiac myocytes , there was significant over-representation of investigation of 16 pathways Table , the topes Table . The resWe have presented the first comprehensive analysis investigating biological pathways underlying endometriosis pathogenesis using genome-wide methodology. This analysis was based on GWAS results from all endometriosis cases, Stage A and Stage B cases vs. controls. Because the pathway results are based on genetic data, they provide strong evidence of causal involvement of these pathways in disease development and/or maintenance. In addition, we presented the first GWAS analysis for Stage A disease, further exploring the hypothesis of distinct causal pathophysiology on endometriosis subtypes.MAPK signaling in its pathogenesis. The GWAS analysis of Stage A disease resulted in a novel associated variant, rs144240142, in an intronic region of MAP3K4, a gene that was also differentially expressed in endometrium from endometriosis cases and controls in our analysis of an independent data set , and reflect different routes through which integrins (adhesion molecules) can activate ERK1 ERK2 MAPK signaling. Wnt signaling was also implicated in all endometriosis, in line with previous reports of both single-SNP and pathway associations involving Wnt pathway genes , MAPK kinase (MAPKK) and MAP kinase (MAPK), that are activated consecutively Fig. . ExtraceERK MAPK pathway has been suggested to be dysregulated in eutopic endometrium after experimental endometriosis induction in a baboon model  disease were related to ECM and adhesion processes: the ane Fig. . GWAS anThe role of integrins and the ECM in endometriosis has long been postulated . IntegriP < 5 × 10−8). For instance, for breast cancer the largest GWAS meta-analysis has included >55 000 cases to date, finding 67 GWAS variants together explaining 14% of heritability  only variants in or near genes are assigned to genes so that a large proportion of GWAS evidence pertaining to intergenic variants is disregarded; (ii) reliance on the contents of pathway databases and on the available software tools within them. Better studied biological processes are more likely to be defined into (more detailed) pathways than less well-studied ones. In our analysis, this may have resulted in the relatively greater detail of the different An important method for understanding the effects of variants on downstream molecular processes is their integration with transcriptomic and epigenomic data. However, such functional studies need to be conducted in disease-related tissues, such as endometrium for endometriosis. There are no genome-wide data sets available currently that associate genetic variants with gene expression levels (expression quantitative trait loci) in endometrium. Currently, none of the large-scale genomic profiling initiatives such as GTex  and the ERK1 ERK2 pathway were observed to control deep infiltrating endometriosis progression  vs. moderate/severe (rAFS III/IV) endometriosis. Based on our results, we postulate that integrin-mediated MAPK activation plays a key causal role in the establishment of endometriotic lesions. Development of Stage I/II disease is further characterized by involvement of the intracellular O.U. and N.R. are the lead authors. K.T.Z. developed the idea for the study and proposed the hypothesis. O.U., N.R. and K.T.Z. conceived and designed the study. K.T.Z. and G.W.M. obtained funding. N.R., D.R.N., G.W.M. and K.T.Z. provided study materials and collected and collated data. O.U., N.R. and A.P.M. did the statistical analysis. O.U., N.R. and K.T.Z. analyzed and interpreted the data. O.U. and N.R. made initial drafts of Tables and figures, and drafted the manuscript. D.R.N., K.V., S.A.M., C.B., A.P.M. and G.W.M. critically revised the manuscript for important intellectual content. All authors read and approved the final version of the manuscript. K.T.Z. is the study guarantor.The genome-wide association data and WTCCC were generated through funding from the Wellcome Trust  and the National Health and Medical Research Council (NHMRC) of Australia . N.R. was funded by a grant from the Medical Research Council UK (MR/K011480/1). A.P.M. is a Wellcome Trust Senior Fellow in Basic Biomedical Science (WT098017).All authors declare there are no conflicts of interest.Supplementary Figure 1Click here for additional data file.Supplementary Figure 2Click here for additional data file.Supplementary Figure 3Click here for additional data file.Supplementary Table 1Click here for additional data file.Supplementary Table 2Click here for additional data file.


Escherichia coli as an example, we show how knowledge about the relation of regulation, kinetic efficiency and intermediate toxicity can be used to identify drug targets, which control endogenous toxic metabolites and prevent microbial growth. Beyond prokaryotes, we discuss the potential of our findings for the development of antifungal drugs.A precise and rapid adjustment of fluxes through metabolic pathways is crucial for organisms to prevail in changing environmental conditions. Based on this reasoning, many guiding principles that govern the evolution of metabolic networks and their regulation have been uncovered. To this end, methods from dynamic optimization are ideally suited since they allow to uncover optimality principles behind the regulation of metabolic networks. We used dynamic optimization to investigate the influence of toxic intermediates in connection with the efficiency of enzymes on the regulation of a linear metabolic pathway. Our results predict that transcriptional regulation favors the control of highly efficient enzymes with less toxic upstream intermediates to reduce accumulation of toxic downstream intermediates. We show that the derived optimality principles hold by the analysis of the interplay between intermediate toxicity and pathway regulation in the metabolic pathways of over 5000 sequenced prokaryotes. Moreover, using the lipopolysaccharide biosynthesis in  Understanding the guiding principles behind the evolution of metabolic networks and their regulation is of fundamental importance in a broad range of disciplines reaching from the identification of novel targets to treat infections to the utilization of microbial organisms in biotechnological production processes. In our study, we used an approach that allowed us to identify optimal regulatory strategies for the control of metabolic pathways in a scenario in which the intermediates of a metabolic pathway differ in their toxicity for the host organism. The results of our approach, whose validity we demonstrate through the large-scale analysis of pathway regulation in several thousand prokaryotic metabolic networks, show that toxic intermediates are often associated to strongly regulated enzymes. Additionally, transcriptional regulation preferably targets highly efficient enzymes thereby minimizing the effort in terms of protein production that is required to adjust the flux through a metabolic pathway. Moreover, in an example case, we show how knowledge about key targets of regulation can be used to identify novel antimicrobials that inhibit cellular growth through self-poisoning of the pathogen. The consideration of organisms from the point of view of evolutionary adaptation is at the core of a large number of biological considerations –5. The uWhile most optimization approaches employed in Systems Biology are considering optimality in a balanced (growth) state, the optimality in the dynamics of adaptation has recently received increasing attention –10. WhilIn previous works, we have identified a plethora of mechanisms and regulatory strategies by which organisms can reduce response times to adapt metabolic fluxes , 14, 15 An important problem that we did not consider in our previous works are differences in the toxicity of intermediates of metabolic pathways. Especially, after a change in environmental conditions, the adjustment of fluxes in a pathway can lead to a temporary build-up of intermediates , 18. WhiEscherichia coli and a study of the relationship between regulatory effort as well as intermediate toxicity in the metabolic networks of more than 5000 prokaryotes. Moreover, we illustrate the usefulness of our approach through a detailed investigation of the influence of intermediate toxicity on the production of endogenous toxic metabolites in lipopolysaccharide metabolism.In this work, we use dynamic optimization to study how the toxicity of intermediates influences regulatory programs that control metabolic pathways. For such pathways, our optimization approach predicts a more focused regulation of enzymes that occur upstream of toxic intermediates. Moreover, there is a strong tendency of transcriptional regulation to target enzymes that are efficient in terms of catalytic activity as well as substrate affinity and possess less toxic upstream intermediates. Especially a focus on transcriptional regulation of efficient enzymes is surprising since it is often assumed that rate-limiting steps, which are catalyzed by inefficient enzymes, are key points of pathway control , 21. We βi , which vary over time:kcat and Km. As discussed previously [vg(t) (see vg(t) like in Wessely et al. [While the substrate of the pathway is assumed to be constant in concentration, the product undergoes a drain e.g. by growth or other environmental changes g(t) see . TherefoThe toxicity of intermediates is simulated as constraints on the concentration of the metabolites:A biological interpretation of the toxicity threshold used in the optimization is, for instance, the half inhibitory concentration (IC50) of a metabolite, that is, the concentration at which half of a bacterial population is inhibited in its growth.σ. Hence, the following objective function:σ models the costs for the whole pathway and not of single enzymes. In previous studies [In the objective function we minimize two factors: the regulatory effort and protein costs. The regulatory effort is modeled as the deviation from initial enzyme concentrations and protein costs by the initial concentration of enzymes. Both objectives are weighted with the factor  studies , 16 we dβ) and kinetics  are tested for their influence on optimal regulatory programs for two values of σ corresponding to high and low enzyme costs, respectively. To test the influence of toxicity and kinetic parameters, we performed 1000 runs in which their values were chosen randomly from the intervals indicated in The model consists of several parameters with arbitrary units . The time(t)) and state variables (metabolite concentrations), and hence has to be solved using dynamic optimization. Similar to previous works [The above described optimization problem has time dependent and continuous decision  sites as reference. We used predicted PTM-sites as estimator for post-translational regulation since they offer a view on non-transcriptional regulation. Predicted as well as experimentally validated PTM-Sites were obtained from dbPTM . In a prE. coli. Predicted logarithmized IC50 values for all compounds listed in the MetaCyc database were kindly provided by Dr. Pablo Carbonell.Since experimentally determined toxicity of compounds is only available for a small number of metabolites and organisms, the prediction tool EcoliTox was used . This toWe focused our analysis on sparsely regulated metabolic pathways, since, in contrast to pervasively regulated metabolic pathways, only the most important enzymes are under transcriptional control in such pathways and influence of intermediate toxicity is stronger. In accordance to our previous work , sparselodel see  and the odel see .log IC50-values were Z-transformed to make data comparable across different pathways and organisms:Z(x) is the transformed, avg the mean and sd the standard deviation of the corresponding values x across the entire pathway.Promoter lengths, number of PTM-sites and To match the analysis of the optimization results, we defined strongly or weakly regulated intervals of linear pathways as those where the mean regulatory effort of the interval was among the 10% highest (strong regulation) or lowest (weak regulation) across all pathways. Results show similar tendencies and significance for higher thresholds (25%).Subsequently, the underlying pathway characteristics, like intermediate toxicity, of strongly and weakly regulated pathways were compared for each interval see  or interIn a first step, we analyzed the influence of the inclusion of toxicity constraints on general properties of pathway regulation. To this end, we determined the relative distribution of regulatory effort for each pathway position across the 1000 randomly sampled parameter combinations for the model including toxicity constraints. In accordance to our previous results [To better understand the influence of parameters on the targets of regulation, we separated all parameter samples into cases with weak and strong regulation for the individual pathway positions (upper and lower 10% percentile) and compared the distribution of the corresponding parameter samples between those cases see . Based or = 0.57, P < 1 ⋅ 10−308) of keff and fraction of regulatory effort. The correlation calculation includes all optimization results and shows that our observations are valid not only for the extreme cases (strong or weak), which are chosen to depict our results. Moreover, we observe a strong influence of intermediate toxicity on pathway regulation. The relation of toxicity thresholds, where low values correspond to high toxicity, and regulatory effort reveals that strongly regulated enzymes are followed by intermediates with low toxicity thresholds . Further, to make pathways comparable and to match our model, we defined five pathway intervals to which enzymes at different pathway positions were assigned , intermediate toxicity and kinetic parameters (for gned see .P = 0.019), that is they are less toxic. From an evolutionary point of view, this observation makes intuitively sense since substrates and products of pathways are usually present in higher concentration than intermediates that are only transiently formed [In a first step, we investigated the distribution of intermediate toxicity across the pathway positions collected in the MetaCyc database see . We obsey formed . This suy formed .Similar to the analysis of the optimization results, we separated data sets into cases of strong and weak regulation at individual pathway positions. To this end, we defined for each pathway interval the 10% of enzymes with highest regulatory effort as strongly regulated and the 10% of enzymes with smallest regulatory effort as weakly regulated see . Since tP < 1 ⋅ 10−16, see In accordance with the optimization results, we observed a significantly lower toxicity of intermediates before the strongly regulated enzymes  and mapped the properties.We retrieved all experimental determined values of database  and obtakeff shows that the initial enzymes of pathways (11.6 mM−1s−1 average efficiency) tend to be much less efficient than the terminal enzymes (94.1 mM−1s−1 average efficiency) in a linear pathway  are reported to inhibit growth by various mechanisms (see green box in An interesting example is provided by lipid tate see . DespitemM−1s−1) and the highest promoter length (101 bp) within the pathway  has a very high catalytic efficiency -desaturase, so creating an important intermediate of ergosterol pathway [Candida albicans [Candida albicans is a potential target leading to accumulation of intermediates, which was also observed under anaerobic conditions where ERG3 is non-functional [Candida but also for Aspergillus fumigatus. Both fungal pathogens are the main causes of life-threatening invasive mycoses [Especially, our approach and these investigations can be applied to eukaryotic pathogens like the fungal species  pathway . Also azalbicans . Howevernctional . As all  mycoses  and drugS1 TextDetailed description and mathematical formulation of ODE system and optimization problem.(PDF)Click here for additional data file.S2 TextAnalysis of pathway models considering product inhibition, reversible reactions, different pathway lengths and random dilution rates.(PDF)Click here for additional data file.S1 Figβ) and kinetic efficiency (keff) between strong and weak regulation at each position. Arrow sizes are scaled to the maximal median difference depicted on the right column.Influence of parameters  on the regulatory strategy and the position of strongly regulated enzymes (blue) for low enzyme costs (A) and high enzyme costs (B). For each strongly regulated enzyme position (row) arrows indicate the difference of medians of toxicity ((PDF)Click here for additional data file.S2 FigRelation of regulation (A) promoter length and (B) PTM-sites with toxicity of intermediates. Arrows pointing upwards showing higher toxicity thresholds (lower toxicity) and arrows pointing downwards vice versa.(PDF)Click here for additional data file.S1 TableTables of promoter length and PTM-sites, as well as IC50 of downstream intermediates for each defined reaction of linear metabolic pathway.(7Z)Click here for additional data file.


Understanding complicated networks of interactions and chemical components is essential to solving contemporary problems in modern biology, especially in domains such as cancer and systems research. In these domains, biological pathway data is used to represent chains of interactions that occur within a given biological process. Visual representations can help researchers understand, interact with, and reason about these complex pathways in a number of ways. At the same time, these datasets offer unique challenges for visualization, due to their complexity and heterogeneity.Here, we present taxonomy of tasks that are regularly performed by researchers who work with biological pathway data. The generation of these tasks was done in conjunction with interviews with several domain experts in biology. These tasks require further classification than is provided by existing taxonomies. We also examine existing visualization techniques that support each task, and we discuss gaps in the existing visualization space revealed by our taxonomy.Our taxonomy is designed to support the development and design of future biological pathway visualization applications. We conclude by suggesting future research directions based on our taxonomy and motivated by the comments received by our domain experts. Pathways are curated subsets of a theoretical graph of all known biomolecular entities and events that occur on the cellular level, and a given pathway usually represents a particular biological process, such as mitosis, that is relevant within a given research context.Understanding complicated networks of biomolecular entities and interactions is essential to solving contemporary problems in modern biology, especially in computational domains such as systems biology . Networkprovenance of the information related to a particular entity or relationship. Provenance is typically a list of records, such as publications, that reflects the collective history of research related to a given entity or relationship. Provenance is essential to the field of bioinformatics, as the “ground truth” related to any given entity is not immutable, and can be derived from a potentially large and evolving history of research.Pathways are modeled as labeled graphs of entities, relationships, and meta-data. An entity is a component of a pathway such as a gene, a gene product (such as a protein), a complex of proteins, a small biomolecule, or even another pathway. Edges between vertices in this graph can be directed or undirected, can involve multiple entities in one relationship, and can represent a wide range of biological relationships. Meta-data can include external information such as experimental data, as well as the BioPax [BioPax schema is itself a reflection of the complexity of information that can exist within bio-chemical pathway datasets.Researchers who work with pathway data are confronted with a number of challenges. Pathway files may contain hundreds or thousands of entities that are connected by a wide variety of relationship types. For instance, the BioPax  specificParticipants in a pathway — genes, proteins, and other molecules within a cell — can act as inputs or outputs to multiple interactions, and the set of relationships between biochemical interactions inherently includes feedback loops and other complex relationships. Importantly, reactions and other interactions can have a “cascading” effect, where one interaction will inhibit or promote the effect of another. Molecular activation pathways also have an inherently dynamic quality, which can limit the utility of static  graph representations . Understin situ analyses.Pathway diagrams are used in two contexts: for the presentation of results, and as an active (and interactive) part of the process of data analysis. In the presentational sense, pathway diagrams can contextualize a set of biological processes within a cell, and in these contexts will often show the location of cellular membranes and other large cellular structures to help to provide a frame of reference for the viewer. Ideally, a pathway diagram — when used in a presentational context — allows a viewer to efficiently understand a complex set of biological relationships. While pathway diagrams may be useful for presenting and contextualizing a set of results in a research or educational context, they are also an important part of For example, metabolic activation networks are of critical importance to cancer researchers, who hope to understand — and potentially disrupt — malignant cycles of uncontrolled cellular growth, replication, and mediated cell death . EffectiIn the last two decades, as the availability of large stores of data to researchers has increased, analyses that involve hundreds or thousands of genes and gene products have become common. When analyzing such large and complex data, visual representations can be essential, and in many cases static, non-interactive, representations will fail to adequately convey the dynamic nature of a pathway. The complexity and amount of information that needs to be incorporated in a given diagram can also make static representations cluttered and difficult to interpret. Thus, modern applications in these domains employ a wide variety of interactive visualization techniques to allow a user to effectively explore and analyze pathway data.Developing and designing effective visual analytics applications requires a detailed understanding of the visual analysis tasks that will be performed by a user, and the “user” in this case is a biological researcher in the midst of some analysis relevant to their domain. User tasks can thus be designed and understood best through an in-depth understanding of the nature of information needed by the researcher in the course of their analyses. Some of these tasks may not be known a priori and may be exploratory in nature, where an ideal visualization of pathway data could reveal important new insights to a researcher. A comprehensive understanding of tasks performed by domain researchers in a typical analysis is essential to the design and implementation of an effective visual analytics application .In this work, we present a description and analysis of tasks related to the analysis of biological pathway data. Tasks were derived from interviews with several domain experts in biology. After an introduction to the structure and content of pathway data, we describe the task taxonomy that was constructed from these interviews. We also review visual representations of pathway data in the context of our taxonomy, along with a brief discussion of existing tools which implement those visual representations. Finally, avenues of future research are considered, along with a brief summary of lessons learned from domain experts.Pathway models are an important concept in biological research –7. VisuaChiBe [Entourage [Reactome Pathway Browser [VisAnt [MetaViz [VitaPad [A number of surveys exist that describe the large number of existing tools for biological network visualization –11. In tChiBe , Entourantourage , Reactom Browser , VisAnt  [VisAnt , MetaViz[MetaViz , and Vit[VitaPad .BioFabric [Cytoscape [Cerebral [RenoDoI [NodeTrix [Node-link diagrams are the nearly-universal choice of visual representation used in existing applications (exceptions to this rule include ioFabric ). Cytoscytoscape  is a popCerebral  and Reno[RenoDoI . However[RenoDoI , 23. For[RenoDoI , 25. WhiNodeTrix , which cBioPAX [KEGG [SBML [Pathway data can be stored in a variety of file formats which capture the underlying structure of pathway data. In particular, BioPAX , KEGG [2AX [KEGG  and SBMLGG [SBML  are the BioPAX, in particular, was designed to be a general format for biological pathway data across a variety of domain contexts [Systems Biology Graph Notation (SBGN) [BioPAX and SBML file formats. Features particular to SBGN include the definition of multiple edge and node types, as well as allowing edges to connect to more than two nodes, resulting in a hypergraph. Other formats are used for the visualization of biological pathways that are not specific to the field of biology. For instance, the SIF Simple Interaction Format is used by Cytoscape [All three of these popular formats are XML-based and represent data as an ontology. contexts . Systemsn (SBGN)  is a visytoscape  to repretask taxonomies, which are written in an effort to understand how the various tasks performed by an analyst and user are related to (and enabled by) different visualization tools and techniques, and, conversely, how visualization tools might inform analytic tasks. These taxonomies help to clarify the utility of existing techniques while also providing a low-level template for the design and evaluation of new techniques. Wehrend and Lewis [The field of visualization has produced a number of nd Lewis  provide nd Lewis  defines nd Lewis  extend tany visualization of data. These early taxonomies were written as very general classifications of low level analytic tasks related to any data visualization. In more recent publications, and as visualization research has progressed, task taxonomies have increasingly focused on more constrained subsets of tasks related to particular types of data structures and analytic domains.These seminal taxonomies were, like many later taxonomies, independent of a specific visualization application domain, and their purpose was to provide a low level description and categorization of the analysis tasks enabled by More recent taxonomies tend to focus on more narrow categories and domains relevant to visualization. For instance, Valiati et al.  provide enRoute [enRoute, which is specifically used for the analysis of experimental data.Several recent taxonomies focus on aspects of graph visualization that extend the work of Lee et al. . For insenRoute  include Aside from explicit task taxonomies, several contemporary surveys and state-of-the-art reports are worth mentioning. Hadlak et al.  provide While these recently-published task taxonomies have focused on particular data structures (or datasets with particular characteristics), to our knowledge the present work is the first taxonomy of tasks written in the context of the domain of biological pathway analysis.The nearest existing work is that of Saraiya et al. , which bIn this work we focus more on the tasks themselves and look not only at existing biological visualization applications, but at general visualizations and techniques which may be useful in supporting the tasks. Biological pathway visualization is a complex application domain that poses many specific analytic challenges that are not encountered in pre-existing task taxonomies. The data structures underlying biological pathways are dynamic multivariate hyper-graphs, and are more complex than any of those described in previously-published taxonomies. The tasks to be completed by biologists are also highly complex, involving many different entity and relationship types, and are not fully covered by the existing taxonomies.Interviews were conducted with seven domain experts in biology, each of whom works with pathway data in some form. A summary of the interviews is described in Table The interviews were free-form discussions aimed at understanding the research process of each domain expert, the tasks performed by the researcher in the course of a typical analysis, and, importantly, the structure and content of the data used in their published research. They were intentionally open-ended, and were designed to capture a variety of tasks that are seen as important to domain experts. Researchers were prompted for any existing tools used for analysis, as well as for the types of behaviors that they think they would find useful in a pathway analysis framework. Each researcher also presented their views on the utility of pathway data and of pathway diagrams in general.We have developed a taxonomy of domain-specific visualization tasks based on these interviews. For each task category, we describe examples of how each task is addressed by current biological visualization applications and techniques.Biological pathways are represented as weighted, directed, labeled graphs which can include hyper-edges and compound nodes. While existing task taxonomies describe tasks related to the visual analysis of graphs in general , 36, theOur taxonomy divides tasks into three broad categories: Attribute, Relation, and Modification tasks. The attribute category includes the identification of attributes (A1), comparison of attributes A2), and the identification of provenance and uncertainty (A3 and A4). The relationship category includes the identification of relationship attributes (R1), directed relationships (R2), and grouped relationships (R3), as well as the identification of causality, cascading effects, and feedback loops (R4 and R5). The modification category includes tasks related to updating and curating data, including collaborative annotation (M1) and curation (M2). A summary of the taxonomy can be seen in Table , and theThe low-level identification of nodes, edges, and their attributes is an essential component of the visual analysis of any graph representation. In the context of biology, the attributes of a node or edge can themselves be complex objects. Here, we highlight three forms of attribute data that are particularly relevant to biological contexts: multivariate data from experimental results, provenance data, and measures of uncertainty. We also discuss the need for the integration of external data sources.DescriptionThe entities within a biological pathway can contain many attributes that reflect the state of that entity in a given context, such as an experimental condition. In interviews, researchers stressed the importance of being able to visualize potentially complex experimental data while viewing a pathway. For example, each entity in a pathway can be associated with gene expression levels across several different experimental conditions, and each of these conditions can include an additional temporal dimension , meaningThis multivariate data can also apply to relationships between entities, such as when one gene is up-regulated or down-regulated by another gene under different experimental conditions. Indeed, the identification (and comparison) of attributes is closely coupled with the identification (and comparison) of overall topological structure .context of an entity , especially when datasets can contain similar entity types that were measured across a variety of different contexts.An additional concern with biological attribute data is the biological Existing approaches and techniquesSBGNViz [ChiBE [Most applications provide access to the attributes through simple interactions . In many cases the attribute information is simply read from an input file, however more recent tools such as SBGNViz  and ChiBz [ChiBE  query onChiBE [Cytoscape application [RenoDoI application [Cytoscape for visualizing knowledge networks of biological data, uses “degree of interest” functions to highlight nodes based on attribute values. Such functionality could easily be extended to biological pathway visualizations. The general purpose visualization system, Candid [Cerebral application [Multivariate network visualization is a highly active field of visualization, in which the life sciences in general are a frequent application domain, and many more recent biological network visualizations include attribute information. ChiBE  provideslication , supportlication , a plug-, Candid  also uselication  uses attVan den Elzen and van Wijk’s  system fDescriptionRelated to the issue of multivariate attributes is the need to compare related pathways or sets of entities, or to compare a given pathway across a number of states. For instance, one of the researchers we interviewed described their use of microarray measurements, which are often used to measure gene expression levels for a control group and an experimental group over several time steps. The goal of this research is to discover significant empirical differences between groups and across time, and the visual comparison of these groups is an essential part of an analysis.In addition, analysts often want to reason about the same entity  across multiple pathways. In other words, the role or behavior of a biological entity in multiple different contexts is often important.Visualizations of comparative differences can also be closely coupled with common bioinformatic algorithms. For example, the algorithmic task of discovering subsets of a pathway dataset that are differentially regulated in a given biological context is an important computational problem, and is inherently a comparison task.The topic of contextualization includes a very important component of modern biology, which is the incorporation of multiple external datasets. Biological pathway data is inherently large, complex, and subject to ongoing contributions from contemporary research. Thus, for biological pathway visualization in particular, integration of attribute data from external data sources is essential.Existing approaches and techniquesCytoscape [Cerebral [In their 2011 survey, Gleicher et al.  describeytoscape  , but each relationship type has its own set of potential specifications, some of which can be quite detailed.One of the most obvious challenges for biological network visualization is the fact that the types of relationship between entities are numerous, and even hierarchical. For instance, an interaction between two entities could take many forms, including: the binding of proteins and molecules into complexes, the translocation of an entity from one cellular location to another, a change in gene expression activity, or the modification of existing compounds, to name a few. Each of these events can be further specified. For example, a modification can take many forms, such as Existing approaches and techniquesThe visual encoding of these complex and multivariate relationships is one of the more prominent challenges in the design of visual analytic platforms for biological pathway analysis.Pretorius and van Wijk’s  system fParallel Node-Link Bands (PNLBs) for exploring graphs with multiple edge types. In their examples, edge types are inferred based on their endpoint node types. Nodes are listed in vertical columns with the edges connecting only between neighboring columns. This technique is similar to Pretorious and van Wijk’s approach except that there are multiple columns of nodes and there is only ever one type of edge between two columns. It is an effective visualization, but is generally limited to smaller data sets and those in which the relationship types are multiple bimodal relationships (as there are no edges drawn between non-adjacent columns).Ghani et al.  developeDescriptionbi-directional, which is distinct from an undirected edge. A visual coding that indicates direction must also be able to account for cases in which there are two directional edges between the same two nodes.While some analyses and datasets involve undirected relationships between genes or gene products, the majority of studies of metabolic networks and other inter-cellular processes rely on directed relationships. Several researchers that we interviewed stressed the importance of understanding directed relationships between entities. Depending on the type of relationship in question, edges may be Existing approaches and techniquesSBGN and KEGG contain many different visual representations for edges, so applying the tapered edge visualization style to complex biological pathways is not trivial and would require an empirical evaluation. However, the results of Holten and van Wijk’s work suggest that investigating such an approach may be worthwhile.Many visualization applications use the more traditional approach of arrowheads to indicate edge directions, however work by Holten and van Wijk  shows thDescriptionPathway data is inherently hierarchical, and there are many ways in which nodes can be grouped into collections of elements that are related in an explicit biochemical sense  or in a more implicit informational sense . Grouping relationships describe relationships of containment, and these relationships can be abstract or based on real biochemical interactions within a cell. For example, a pathway (itself an abstraction) can be nested within other pathways. These nested pathways generally encapsulate some commonly-understood hierarchy of biological processes that take place within a cell, such as cellular replication. Other representations include the more general notion of a module of connected components, such as gene products. Grouping relationships can also represent physical interactions between biochemical participants. A common of example of this is in biomolecular complexes, which are themselves composed of other complexes or biomolecules.in parallel with other, non-hierarchical relationships, such as the relationship between one gene product that activates or inhibits another. Also, note that while non-hierarchical relationships can take a variety of forms, the only form of hierarchical relationship is one of containment, from parent to child, and is undirected.It is important to note that hierarchy and “structure” often co-exist with other types of relationships. In most cases, pathway data includes relationships of hierarchy  compound nodes. A vertex that contains other entities can be represented as a compound node, which is equivalent to a parent vertex or in some contexts a “module.” It is important to note that a one-to-one relationship between an entity and a parent is not the same as a one-to-many relationship between an entity and all of that parent’s children. For instance, the BioPax format allows for the abstract NextStep relationship, which defines, as the name suggests, an arbitrary notion of the next step of some biological process. A biochemical reaction could be connected, via a single NextStep relationship, to an entire pathway, which could potentially contain thousands of nodes. This relationship is clearly not the same as a biochemical reaction being connected to every entity within a pathway. This example also demonstrates the distinction between a compound relationship and a hierarchical relationship (which are two types of grouping relationships). A connection from a node to a compound node does not imply a relationship of ownership or containment.Grouping relationships also include the concept of Existing approaches and techniquesRenoDoI [Bubble Sets [There are a variety of visualization techniques for the display of “grouped” nodes and hierarchical data. Numerous tree based graph layouts position nodes to emphasize the hierarchical nature of data, however these are often not suitable for biological pathway layout as the constrains on position in a layout affect the readability of the lowest level of information. The RenoDoI  applicatble Sets  approachble Sets .BranchingSets technique [The echnique , 59 faciDescriptioncausal relationships that exist between biomolecular entities, and causal networks are of particular importance to the analysis of large-scale gene expression data.A category of tasks inherent to a variety of work in bioinformatics is the identification of upstream or downstream of another. For example, one gene product can increase the activity of other gene products that are downstream of it. Understanding these upstream and downstream relationships is particularly important to domains such as cancer drug research, where a drug may affect a small subset of genes or gene products, which in turn will affect various downstream processes. In most cases, a directed relationship is meant to represent a biochemical reaction, where one entity is consumed as a reactant and another is produced as a product. Thus, an upstream entity may be connected to a downstream entity through a chain of several directed links, and a researcher may be interested in understanding the path of reactions (or other relationships) that connects two entities. However, most cellular processes are inherently complex, and involve many competing sets of directed interactions. Any given gene is often mediated by many different reactants, some of which increase activity, and others which decrease activity. For instance, a causal network helps to reveal the likely regulators of a set of genes that are observed to be up-regulated or down-regulated in a particular setting [When discussing directed paths between entities, one entity is said to be  setting , 61.cascading effects, where many upstream entities have directed relationships with many mediating entities, which in turn affect the output of many downstream entities.Thus, determining the set of entities that are “responsible” for the increase or decrease in the expression of a particular gene is a challenging task that involves a complex array of directed relationships between many upstream entities. We characterize this problem as one of identifying In tandem with the problem of identifying cascading effects is the problem of reasoning about feedback . FeedbacenRoute [Causality and cascading effects depend on the both the structure of the graph, which determines the global propagation of change, and the attributes associated with individual graph entities, e.g., a change in a particular gene expression level from being up-regulated to down-regulated. In this case, the structure of the graph does not change, only entity attributes  in a traditional biological pathway network visualization can be difficult as there are relatively few visual encodings which can indicate attribute values . The approach of Pretorius and van Wijk  allows fsmall multiples seems to be the best approach to convey the dynamic attribute changes that cascade through a network. The small multiples approach is a form of comparative juxtaposition where multiple views of the network at different time points are displayed in a matrix. This approach has been used by the Cerebral application for showing cascades of data [enRoute [With respect to cascading changes of attributes, Archambault and Purchase  have per of data . Archamb[enRoute  briefly While most of the tasks in this taxonomy are directly related to visual analysis, the size and complexity of biological datasets makes data curation an essential part of modern research platforms.Descriptiondebug potentially flawed data. Two others expressed a need to create “personalized” pathways that only include a user-determined subset of entities and relationships. Ideally, visualization tools will seamlessly integrate these curation and maintenance needs.Several of the researchers we interviewed mentioned certain tasks related to the curation, maintenance, and understanding of pathway data. For instance, one researcher mentioned the importance of being able to An important aspect of data modification is the notion of collaboration — where several researchers are allowed, synchronously or asynchronously, to modify and update a dataset. The concept of collaboration is increasingly important as more analytics platforms move to the web, and the topic of effective user-centered design for scientific collaboration will become increasingly relevant in the future.The topic of contextualization includes a very important component of modern biology, which is the incorporation of multiple external datasets. Biological pathway data is inherently large, complex, and subject to ongoing contributions from contemporary research. Thus, for biological pathway visualization in particular, integration of attribute data from external data sources is essential.Existing Approaches and Techniques Most desktop pathway visualization applications allow for data files to be edited and exported in standardized formats, e.g., CellDesigner [SBML standard. Saving a personalized version of a pathway is basic functionality, but curating a large data set may take the input of many experts. Collaborative online visualizations such as Polychrome [Designer  allows flychrome  allow a Reactome [STRING [Reactome database is curated and peer reviewed is considered an important feature of the system. Most online publicly available pathway databases do allow users to provide feedback on potential errors, updated research, or areas for improvement.The approach of using web tools to disseminates knowledge is already very evident in many modern online biological data resources. Reactome  and STRI [STRING  are two In the process of creating a domain-specific task taxonomy that is based on interviews with domain experts, we have revealed several tasks that are important to domain researchers but which merit further attention and more focused scrutiny from the visualization community. While tools exist which support these tasks, existing approaches tend to be ad hoc and may lack features that would be revealed by more user-centered design methodologies. However, we do not mean to suggest that future research should be constrained to these tasks alone, but that they should be given more focused attention in the field of pathway visualization research. Below we outline some directions for future research.Visualizing causality and cascading effectsPrevious taxonomies only touch on the notion of network-level causality and cascading effects in passing. Lee et al.  describeVisualizing uncertainty and provenanceBioPAX do have robust support for citations, allowing published references to be connected to entities and relationships within a pathway. A tool that could effectively encode data related to uncertainty and provenance into a visualization would be very valuable to systems researchers who work with the results of hundreds or thousands of publications and experimental datasets.Many visualization tools generally do not attempt to visualize the uncertainty behind a connection in a pathway, which was particularly important to many of the domain experts we interviewed. Uncertainty may exist as a result of experimental measures  or as a result of varying provenance. In bioinformatics, provenance is an important and complex layer of metadata that accompanies any dataset. Furthermore, data provenance was mentioned as a particularly important concern by most of the domain researchers we interviewed, but there are relatively few examples of visual analytics tools which explore provenance visualization in a direct and robust way. Visualizing provenance and uncertainty is a challenging task, as even the definition of uncertainty may be difficult to operationalize, and each datum in a given pathway could be associated with a potentially large and complex hierarchy of research. However, data formats such as As mentioned earlier, our interviews with domain experts were intentionally open-ended and a structured interview process (such as grounded theory) was not used. This format was used in order to encourage a diverse assortment of feedback from researchers who engage in a wide variety of research within biology and bioinformatics. As a result of this free-form structure, our tasks identified in this taxonomy are by no means meant to be an exhaustive list of all possible visualization tasks — it is certainly possible for this taxonomy to be refined and extended, and additional contributions and suggestions are welcome. In addition, a second round of explicitly structured interviews will be a valuable next step in the continued refinement of this taxonomy. Our interviews have created a valuable base of user feedback that has guided the identification of tasks that are of particular importance to domain experts.While a wide variety of pathway visualization tools exist, there is still plenty of room for innovative platform development. Many tools tend to greatly overlap each other with respect to the analytical tasks available, and attempts to directly address the most challenging aspects of pathway data analysis are few and far between. Having a detailed understanding of the tasks performed by researchers who work with pathway data is essential to the development of effective visual analytics platforms for pathway analysis.any context. While our goal in creating this taxonomy was to identify visualization tasks relevant to researchers who work with biological pathway data, our taxonomy also acts as a template for domain-specific task taxonomies in the more general context of information visualization research. Existing visualization contexts are often deliberately generalized, and are meant to address challenges and provide guidelines for the visualization of certain categories of data, rather than certain categories of research. While such generalization is obviously useful, we have shown that the opposite approach can be useful as well. By focusing on specific research domains, visualization researchers can capture the needs of domain researchers, allowing them to build more user-centric task taxonomies. These domain-specific taxonomies have the potential to reveal real-world tasks that may not have been adequately captured by more generalized taxonomies.While relevant network visualization taxonomies exist, they largely describe tasks related to network data visualization in Here, we have built a task taxonomy by starting with low level research tasks identified by domain experts as important to their research. As the field of biological pathway visualization continues to grow, we hope to emphasize the needs of real-world domain experts, and to guide researchers towards avenues of research that could lead to valuable contributions. Through interviews with domain experts, we have identified tasks important to researchers who work with biological pathway data, and this taxonomy acts as a reference point for the large and growing field of biological pathway visualization. The continued refinement and understanding of tasks related to pathway data in bioinformatics research will create an important foundation for the ongoing development of pathway visualization.