The GOLD.db (Genomics of Lipid-Associated Disorders Database) was developed to address the need for integrating disparate information on the function and properties of genes and their products that are particularly relevant to the biology, diagnosis management, treatment, and prevention of lipid-associated disorders. provides a reference for pathways and information about the relevant genes and proteins in an efficiently organized way. The main focus was to provide biological pathways with image maps and visual pathway information for lipid metabolism and obesity-related research. This database provides also the possibility to map gene expression data individually to each pathway. Gene expression at different experimental conditions can be viewed sequentially in context of the pathway. Related large scale gene expression data sets were provided and can be searched for specific genes to integrate information regarding their expression levels in different studies and conditions. Analytic and data mining tools, reagents, protocols, references, and links to relevant genomic resources were included in the database. Finally, the usability of the database was demonstrated using an example about the regulation of Pten mRNA during adipocyte differentiation in the context of relevant pathways.The GOLD.db The GOLD.db will be a valuable tool that allow researchers to efficiently analyze patterns of gene expression and to display them in a variety of useful and informative ways, allowing outside researchers to perform queries pertaining to gene expression results in the context of biological processes and pathways. The excessive consumption of high calorie, high fat diets and the adoption of a sedentary life style have made obesity and atherosclerosis major health problems in Western societies. In the USA, over 50% of the population are over-weight BMI > 25) and close to 25% are considered obese (BMI > 30) ,2. As a 5 and cloAt the root of obesity and atherosclerosis is an excessive deposition of neutral lipids. Adipose tissue accumulates predominantly triglycerides, whereas macrophages along the blood vessel wall mainly accumulate cholesterol and cholesteryl esters. Accordingly, a detailed understanding of the molecular mechanisms that govern the balance between lipid deposition and mobilization is fundamentally important for the prevention and improved treatment of disease. In addition to the apparent environmental components involved in the pathogenesis of disorders related to lipid and energy metabolism, a large number of studies have provided undisputed evidence that susceptibility genes contribute around 50% of the phenotype. These genes encode products involved in the cellular uptake, synthesis, deposition and/or mobilization of lipids. However, characterization of many if not most of these genes and their products remains rudimentary. Deficiencies in the current level of understanding extend to key enzymes such as important triglyceride hydrolases in adipose tissue  or choleWhile medical molecular biology traditionally associates single genes and gene products with diseases, a growing body of evidence suggests that several common disease phenotypes arise from the delicate interaction of many genes as well as gene-environment interactions. To elucidate the development of obesity and atherosclerosis, it will be necessary to analyze patterns of gene expression and relate them to various metabolic states. To discover novel genes, processes and pathways that regulate lipid deposition and mobilization, a departure from hypothesis-driven research and turn to a discovery-driven approach is necessary. The application of high-throughput technologies and genome-based analysis will provide the tools for the analysis of gene-gene and gene-environment interactions in a systematic and comprehensive manner.To facilitate genomic research we have initiated the development of a system for storing, integrating, and analyzing relevant data needed to decipher the molecular anatomy of lipid associated disorders. In order to provide a reference for pathways and information of the relevant genes and proteins in an efficiently organized way, we have created the Genomics Of Lipid-Associated Disorders database (GOLD.db). The GOLD.db integrates disparate information on the function and properties of genes and their protein products that are particularly relevant to the biology, diagnosis management, treatment, and prevention of lipid-associated disorders.The main goal of the GOLD.db was to provide biological pathways with image maps and visual pathway information. For each element in the pathway, specific information exists including structured information about a gene, protein, function, literature, and links. The GOLD.db provides also the possibility to map gene expression data individually to each pathway. Additionally, analytic and data mining tools, reagents, protocols, references, and links to relevant genomic resources were included in the database.The GOLD.db was implemented in Java technology . Hence, For tracking the repository of the reagents like clone resources and libraries which can be used for microarray studies, we have developed a relational database. Information about the vector, the sequence and length of the clone insert, primers for the PCR amplification, tissue, organism, accession number, library, container, storage information, date and person and access to other clone bases (e.g. IMAGE Consortium) can be stored. Users of the GOLD.db can list these clones and get all the information about each available clone. With restricted access, clone information or even clone lists can be uploaded and selection lists can be created and deleted. The input mask is designed in such way that the user can choose one of the elements of the created selection lists.In order to deal with the huge amount of data associated with large scale studies and to perform sequence based and microarray analysis, several bioinformatic tools were integrated or can be downloaded. Sequence similarity search against databases can be performed with BLAST  , FASTA  or HMM  to the corresponding elements of the available pathways similar to previous efforts . Either As key for the mapped relation the RefSeq number  is used. or ArrayExpress , it is still very difficult for researchers to obtain the original data. Web sites with Supplementary information are not maintained and/or not further developed. Hence, a database with a large collection of curated datasets will be enormously valuable for the community. Approaches to upload and retrieve gene expression data were pursued within the GOLD.db. Large scale gene expression data sets can be uploaded in form of tab delimited text files (Stanford file format) [Analysis of gene expression patterns in animal and cell models for lipid-associated disorders will help to understand the fundamental gene relations and regulatory mechanisms responsible for the development of obesity related diseases. The huge amount of data associated with the analysis of large scale gene expression analysis raises the demand of tools for storing, processing and retrieving complex information. Although a number of studies have been published and despite the requirements of some journals to deposit microarray data in public databases like GEO  format)  as used Recently, it was shown that insulin sensitivity, energy expenditure, and thermogenesis were enhanced in adipose-specific Pten-deficient (AdipoPten-KO) mice. Body and adipose tissues weight in these mice were significantly lower than those of control mice in spite of a larger food intake . We addrDuring adipocyte differentiation cyclin dependent kinase inhibitors, like p21 leads to a hypophosphorylation of the Retinoblastoma protein (Rb) which allows binding to the E2F transcription factor, causing cells to permanently exit the cell cycle – a required step in adipocyte differentiation called mitotic clonal expansion – before entering the terminal differentiation state. pRb interacts physically with adipogenic CCAAT/enhancer-binding proteins and positively regulates transactivation by C/EBPβ and therefore plays a pivotal role in adipocyte differentiation ,25. HencThus, using recently identified key player for food intake and weight control and using the GOLD.db, it is possible to address relevant questions and generate testable hypotheses on the molecular mechanisms of fat cell development.The vast quantity of gene expression data generated in genomic studies presents a number of challenges for their effective analysis and interpretation. In order to fully understand the changes in expression that will be observed, we must correlate these data with phenotype, genotype, metabolism and other information including the tissue distribution and time course expression data gleaned from previous studies. The goal of our work was the development of a specialized database and tools that allow researchers to efficiently analyze patterns of gene expression and to display them in a variety of useful and informative ways, allowing outside researchers to perform queries pertaining to gene expression results in the context of biological processes and pathways. The uniqueness of the GOLDdb database we have developed is threefold: 1) the inclusion of annotated pathways, 2) the availability of curated datasets and 3) the possibility to map experimental data on biological pathways. The upcoming challenges will be to include data from functional analysis and proteomics data, which will give us new opportunities in understanding mechanisms of different applications and lipid-associated disorders in particular.. The pathway editor and the clone tracker are available free of charge to academic, government, and other nonprofit institutions.The GOLD.db database should be cited with the present publication as a reference. Access to GOLD.db is possible through the world wide web at HH was responsible for the content, the annotation process, webdesign, and processing of data sets. MM was responsible for the implementation of the database and web application as well as the relational database for the clone tracker. BM and JH had implemented the mapping of expression data to pathways. GS is involved in providing of sequence analysis tools and server software. DMS has annotated the insulin signaling pathway. ZT was responsible for the design of the study and for overall project coordination.


Signal transduction is one of the most important biological processes by which cells convert an external signal into a response. Novel computational approaches to mapping proteins onto signaling pathways are needed to fully take advantage of the rapid accumulation of genomic and proteomics information. However, despite their importance, research on signaling pathways reconstruction utilizing large-scale genomics and proteomics information has been limited.We have developed an approach for predicting the order of signaling pathway components, assuming all the components on the pathways are known. Our method is built on a score function that integrates protein-protein interaction data and microarray gene expression data. Compared to the individual datasets, either protein interactions or gene transcript abundance measurements, the integrated approach leads to better identification of the order of the pathway components.As demonstrated in our study on the yeast MAPK signaling pathways, the integration analysis of high-throughput genomics and proteomics data can be a powerful means to infer the order of pathway components, enabling the transformation from molecular data into knowledge of cellular mechanisms. Signal transduction is the primary means by which eukaryotic cells respond to external signals from their environment and coordinate complex cellular changes. It plays an important role in the control of most fundamental cellular processes including cell proliferation, metabolism, differentiation, and survival . ExtraceRecently, with the accumulation of genome sequence information, large-scale genomic and proteomic techniques have offered insights into the components of signal transduction pathways and the molecular and cellular responses to cell signaling. For example, large-scale yeast two-hybrid screening methods and Co-IP technique have been used to identify physical interactions between proteins -5. Synthab to that of a single mutant a, or a single mutant b. However, this analysis is time-consuming, expensive and sometimes the results can be misinterpreted  byThe two data sources are considered with equal importance, so we rescale the score Smin and Smax are the minimum and the maximum scores of all the possible pathways respectively for either protein-protein interaction data or gene expression data. The rescaling procedure is performed on both data sets. The integrated score is the sum of the rescaled scores for each individual data set.where SYL designed the study, performed the pathway analysis, and drafted the manuscript. HZ conceived and guided the study. Both authors read and approved the final manuscript.


Microarray technologies have evolved rapidly, enabling biologists to quantify genome-wide levels of gene expression, alternative splicing, and sequence variations for a variety of species. Analyzing and displaying these data present a significant challenge. Pathway-based approaches for analyzing microarray data have proven useful for presenting data and for generating testable hypotheses.To address the growing needs of the microarray community we have released version 2 of Gene Map Annotator and Pathway Profiler (GenMAPP), a new GenMAPP database schema, and integrated resources for pathway analysis. We have redesigned the GenMAPP database to support multiple gene annotations and species as well as custom species database creation for a potentially unlimited number of species. We have expanded our pathway resources by utilizing homology information to translate pathway content between species and extending existing pathways with data derived from conserved protein interactions and coexpression. We have implemented a new mode of data visualization to support analysis of complex data, including time-course, single nucleotide polymorphism (SNP), and splicing. GenMAPP version 2 also offers innovative ways to display and share data by incorporating HTML export of analyses for entire sets of pathways as organized web pages.GenMAPP version 2 provides a means to rapidly interrogate complex experimental data for pathway-level changes in a diverse range of organisms. Advances in DNA microarrays, RNA interference, and genome-wide gene engineering have contributed a wealth of genomic data to the public domain. The average researcher is faced with the challenge of connecting these genome level results to specific biological processes. Therefore intuitive tools for integrating, analyzing, and displaying this data are welcomed by many biologists. One popular approach is pathway-oriented data analysis, which enables biologists to interpret genomic data in the framework of biological processes and systems, rather than in a traditional gene-centric manner.We developed Gene Map Annotator and Pathway Profiler (GenMAPP) as a free, open-source, stand-alone computer program for organizing, analyzing, and sharing genome-scale data in the context of biological pathways . GenMAPPGenMAPP was developed by biologists and remains focused on pathway visualization for bench biologists, our major user base as judged from publications citing GenMAPP. Unlike other computational systems biology tools , gene databases (.gdb), and pathways (.mapp) – are stored in separate files accessible by GenMAPP. All three file types are stored in Microsoft Jet format. Experimental datasets store any data imported by the user, together with a set of custom coloring criteria (color sets). The gene databases contain species-specific gene annotation from a number of public resources. Databases are created through an ETL  process, by which information is collected from Ensembl, Entrez Gene, Affymetrix , and GOADatabases and pathway archives are available through the Data Acquisition Tool in GenMAPP and from the GenMAPP website. The tools known as MAPPFinder 2 and MAPPBuilder 2 are bundled with and accessible from GenMAPP. MAPPBuilder creates .mapp files from imported lists of genes, and MAPPFinder  computesGenMAPP version 2 provides 1) new built-in features to support user data import and mapping, 2) expanded pathway resources and 3) increased support for different high-throughput biological assays. These improvements substantially increase the usability and flexibility of this tool for pathway level genomic analysis.Several new features have been implemented in GenMAPP version 2. A new gene database schema supports a variety of gene and protein identifiers, annotations, and microarray probe set IDs, more thoroughly connecting user data to the archive of pathway MAPPs and Gene Ontology terms and to external gene annotation. A new visualization mode allows for simultaneous access to multiple data points, statistics or custom annotations. A new export option packages sets of pathways, including data, to a web-ready format for display and browsing.A major shortcoming of GenMAPP 1.0 and other pathway analysis programs has been the limited number of species supported, permitting analysis of a few model organisms  and a few gene identifier or ID systems . To solve this problem, the GenMAPP version 2 gene database schema has been redesigned to allow expanded gene content and greater species support. Support of many diverse gene and protein ID systems is essential to establish critical relationships between disparate sources of information, providing greater flexibility for users importing data associated with virtually any identifier. In addition to expanded gene and protein ID support, secondary annotation systems such as GO, OMIM, and PDB have been added into the GenMAPP gene databases. These IDs and annotations are provided on HTML "backpages" of MAPP gene objects, providing critical links to primary resources. As additional genomes are assembled and annotated, GenMAPP can readily integrate the information and support pathway analysis for these species.Escherichia coli K12  and Saccharomyces pombe [Databases in GenMAPP version 2 are created through a semi-automated process, using information extracted from major public resources, primarily Ensembl, Entrez Gene, UniProt, and Affymetrix. The process of extracting gene information has been greatly simplified by populating our gene database with data from Ensembl's "mart" tables , which ees pombe . A detaiAs microarray experimental designs grow increasingly complex researchers require tools to examine data across multiple time-points and conditions, and over multiple datasets. The types of biological entities measured have also increased. Various array platforms measure polymorphisms, splice variants, regulatory protein binding and genomic amplifications and deletions. Methods for visualizing the complex outputs from these technologies have not been well established and remain a critical challenge for researchers. With previous versions of GenMAPP, users could view multiple sets of criteria only serially. For example, genes up-regulated at different time-points of an experiment could be viewed by creating a custom set of coloring criteria (color set) for each time point. While informative, this method is not well suited to assess the temporal effects of gene level changes over an extended time or to examine multiple data simultaneously. To expedite the analysis of such datasets, GenMAPP version 2 now allows multiple color sets to be viewed simultaneously, depicted as vertical stripes within each gene box. In the case of multiple time points, the stripes could represent the criteria at each time point.The ability to view multiple color sets concurrently can also be extended to datasets where different biological substrates are examined, such as transcription and mRNA splicing. Demand for this feature is increasing because current microarrays can assay distinct regions of mRNA transcripts, such as exons and exon junctions, thereby allowing assessment of both transcriptional changes and changes in splice isoform expression. While there are many possible ways to view such data, using multiple color sets in GenMAPP is now a powerful way to explore such complex data in a single view. Similar visualization options are only available in a few freely available  and commIn addition to visualizing data on pathway MAPPs, GenMAPP version 2 also exports pathways with data to various graphical formats and to the web. Because genome-scale data are difficult to share with a larger community, GenMAPP version 2 includes the option to export any number of MAPPs with their associated data to an organized web-ready format. This MAPP Set Export feature allows any or all established color sets to be exported with the pathway, including the striped view of multiple color sets. Instead of static images, each MAPP retains its interactive features, such as gene backpage information, including data display, gene annotations, and hyperlinks to external resources. The different criteria can be browsed through a pull-down tab on each exported MAPP. The MAPP Set can be navigated through an index of all MAPPs or through a gene index, which stores all gene-to-MAPP relationships for all related gene/protein IDs. MAPP Sets are stored in HTML format, ready for immediately posting on any web site, where collaborators can browse the data independently of the GenMAPP program. An example of how a GenMAPP MAPP Set can be used to display large-scale data is the International Gene Trap Consortium web site , where tIntegral to any pathway analysis tool is its access to pathway content. One of the goals of the GenMAPP project is to facilitate community curation of pathway content. GenMAPP's built-in drawing tool allows users to illustrate biology and associate gene objects with identifiers maintained in a given gene database. The ability to customize the layout and to annotate a pathway with basic graphics provides a powerful means of communication to the biological community. The expertise of the biological research community is the most important source of new pathway information, and GenMAPP's pathway content is primarily contributed by this community. We have added several new sources of MAPPs. For example the NetPath project is a human pathway annotation project, initiated by the Pandey lab at Johns Hopkins University ,21 and tWe now also provide pathways that have been mapped through homology so that users with genomic data from relatively unsupported species can perform pathway analyses. These homology MAPPs represent a starting point for further curation, an interim solution until species-specific pathways are elucidated and contributed. Another means of increasing the biological content available to the user is the extension of existing pathways using interaction and coexpression data. Together, these methods only begin to address the paucity of pathway content available for the analysis of complex genomics data across the multitude of organisms.Despite the relative ease with which we can gather gene information for many species, pathway information is generally not available for many of the newly supported species Table . To addrThe process of rapidly mapping pathways between species relies on the Converter function in GenMAPP version 2, which allows for conversion of genes on MAPPs between gene ID systems in the database without altering the graphical layout of the MAPP. MAPP conversion is possible between any gene ID systems linked in the database; adding homology information to a GenMAPP database consequently enables conversion of MAPPs between species.The GenMAPP human MAPPs were chosen as template MAPPs because they represent the highest quality of curation in our archive. Homology information between human and the applicable target species was obtained from Homologene  and fromThe development of homology MAPPs in GenMAPP builds upon similar efforts at other databases  and addrThe use of homology mapping addresses the critical need to extend biological representations across species. Yet it is also necessary to expand the pathway content within a given species. In the case of mammalian model organisms, such as mouse, only ~14% of annotated genes in the genome are represented in curated pathways  compared to mid-pregnancy (14 days of pregnancy)  alongside the relative expression of these genes at mid-pregnancy versus the virgin state (first stripe). Making such comparisons in the new version of GenMAPP is relatively straightforward and flexible, supporting not only multiple data points, but also multiple types of data , and supporting structured pathway vocabularies for more efficient pathway migration, update, curation and exchange.To accelerate development and take full advantage of the growing base of open source pathway tools we are actively working with the Cytoscape Consortium ,46 and BGenMAPP version 2 represents a step towards fostering the critical link between the biologist and their data, providing powerful analyses and intuitive representations of increasingly large and complex high-throughput datasets.Project Name: GenMAPPProject Home Page: Operating System: WindowsProgramming Language: Visual BasicRequirements: Species-specific databases and pathway file collections distributed by GenMAPP.orgLicense: Open-source (Apache)Any Restrictions to Use by Non-academics: NoneGenMAPP version 2 features were conceived by BC, KD, AP, NS, SD, KH, KV, AZ and SL. Computer code for the GenMAPP version 2 application was written by SL and SD. AP, NS and KH drafted the manuscript. The pathway extension method was performed by AP and JS; and homology mapping was performed by KH. All authors read and approved the final version of the manuscript.Supplemental Methods.Click here for file


The analysis of biological data is greatly enhanced by existing or emerging databases. Most existing databases, with few exceptions are not designed to easily support large scale computational analysis, but rather offer exclusively a web interface to the resource. We have recognized the growing need for a database which can be used successfully as a backend to computational analysis tools and pipelines. Such database should be sufficiently versatile to allow easy system integration.GeneKeyDB is a gene-centered relational database developed to enhance data mining in biological data sets. The system provides an underlying data layer for computational analysis tools and visualization tools. GeneKeyDB relies primarily on existing database identifiers derived from community databases  as well as the known relationships among those identifiers. It is a lightweight, portable, and extensible platform for integration with computational tools and analysis environments.GeneKeyDB can enable analysis tools and users to manipulate the intersections, unions, and differences among different data sets. As we move toward large-scale research into complex molecular and cellular networks, the research community will need to develop new interfaces to complex data sets. Existing databases and interfaces, such as those at EBI and NCBI,2, oftenAn interesting alternative to the above mentioned databases is BioMART. This isThe development of GeneKeyDB is motivated by a desire to have a smaller-sized database that could tightly interoperate with different local computational tools and local data sets. While providing support for different data mining tools, the database may remain lightweight, by just storing the keys (database identifiers) of objects, some important attributes, and the relationships among the objects. The database does not actually need to store large objects, like sequence records, as long any tool built on top of GeneKeyDB can retrieve the subset of the objects of interest on demand from other sources – EBI, NCBI or local storage. The system allows us to create centrally shared functions to manipulate biological data sets. Each analysis tool, analysis pipeline, or computer can rely on these core functions and data found in GeneKeyDB.GeneKeyDB consists of several sub-modules Figure , correspThe database is created using Oracle and the parsers are written in Perl. Bioperl and ENSEMBL API are also used to create some of the tables. A brief overview of the database creation process is shown in Figure LocusLink tables are derived through parsing LocusLink flat files and inferring additional data such as absolute chromosome coordinates, calculated by joining NCBI contig builds . These It is important to note that no extensive cleansing of the data is performed during the database creation and update process. This allows automatic updates and eliminates some well known problems created by data cleansing . At the Currently we are migrating from LocusLink data to Entrez Gene data as it becomes available from NCBI.GeneKeyDB is primarily used for automating the integration of large data sets with a variety of computational tools that examine secondary gene relationships, such as the analysis of gene regulatory networks and their evolution. Current tools heavily relying on GeneKeyDB include the BSA pipeline (manuscript in preparation), GoTree machine and other utilities available on our web site. GoTree and Webgestalt  for examThe database was constructed, following some of the guidelines and logic described by Waterman et al. . GeneKey1. Define a set of candidate genes, based on the genome localization and tissue expression pattern2. Get all genes expressed in a specific tissue that share a conserved protein domain.3. Get a set of orthologues to a group of genes (defined by the input) and obtain their genome coordinates These examples are designed to help formulate sets of genes, sharing some functional, sequence or expression similarity. These results are not permanent as the information differs between releases. The times for executing the queries was less than 30 s. per each one and have been produced under Oracle 9.0i database on Sparc Ultra-4, SunOS 5.8.While such queries are complex, GeneKeyDB's lightweight nature allows its conversion to popular databases such as Microsoft Access and FileMaker Pro, where graphical user interfaces allow for the creation of queries with only a rudimentary knowledge of SQL. Even though Microsoft Access and FileMaker have limited capabilities compared to Oracle, MySQL or PostgreSQL, they succeeded in handling GeneKeyDB.The development of more exhaustive high-throughput experimental procedures has led to the accumulation of abundant biological data resources. As a consequence, the LocusLink approach of one-gene-at-a-time is an insufficient method to properly mine the data and therefore, analysis of sets of genes is more productive. Additionally, converting between multiple database identifiers is still a challenge as there is no current method to uniquely identify the same gene across multiple databases. Therefore a data mining environment that can synchronize multiple sources and provide general annotation information is going to be beneficial for comparing and using results originating from different experimental groups. One excellent existing resource is EnsMART, but itsCompared to the most similar database, DRAGON, GeneKeyDB has significantly more keys and relationships , where DRAGON has only some PFAM, Unigene and Incyte data as TREMBL, Transfac and Interpro data is not implemented yet in this database (see DRAGON database website ).Through the GeneKeyDB database, it is possible to bring together different data such as evolutionary , expression (CGAP), physical location and functional annotation (GO) in a high throughput fashion, making this resource valuable both to experimental and bioinformatics groups. Though the database is most useful when installed locally, some web based functionality also exists. The most significant function that this system provides is a small lightweight database that can enable analysis tools and local database to have the flexible functions of database queries about genes, gene products, and sets of genes in the course of their large-scale analysis.Current and future development includes improvement to the MySQL/PostgreSQL export, migration toward Entrez Gene , test integration with BioSQL, etc. We intend to convert GeneKeyDB to an open source project and share our parsers with other relevant open source project such as BioPerl wherever appropriate. We expect this step to help GeneKeyDB maintenance..SQL access (through web), Oracle, PostgreSQL and MySQL downloads are available from Project Name: GeneKeyDBProject Homepage: Operating System: Platform independent, tested under Linux and UnixOther Requirements: RDBMS, preferably Oracle, MySQL or PostgreSQLLicense: GNU GPLAny Restrictions to use by non-academics: License neededRDBMS- relational database management systemSQL- simple query languageUIs- user interfacesAPIs- application programming interfacesGOTM- GO Tree MachineGRIF- Gene Reference Into FunctionSK drafted the manuscript and is currently implementing the newest features in GeneKey. SK, EB and BZ are using aspects of GeneKeyDB to extend the data mining of gene sets. DS, SK, BZ, XP and EB developed the GeneKeyDB database. JS guided and coordinated execution of the project. All authors read and approved the final manuscript.


The development of e-Science presents a major set of opportunities and challenges for the future progress of biological and life scientific research. Major new tools are required and corresponding demands are placed on the high-throughput data generated and used in these processes. Nowhere is the demand greater than in the semantic integration of these data. Semantic Web tools and technologies afford the chance to achieve this semantic integration. Since pathway knowledge is central to much of the scientific research today it is a good test-bed for semantic integration. Within the context of biological pathways, the BioPAX initiative, part of a broader movement towards the standardization and integration of life science databases, forms a necessary prerequisite for its successful application of e-Science in health care and life science research. This paper examines whether BioPAX, an effort to overcome the barrier of disparate and heterogeneous pathway data sources, addresses the needs of e-Science.We demonstrate how BioPAX pathway data can be used to ask and answer some useful biological questions. We find that BioPAX comes close to meeting a broad range of e-Science needs, but certain semantic weaknesses mean that these goals are missed. We make a series of recommendations for re-modeling some aspects of BioPAX to better meet these needs.Once these semantic weaknesses are addressed, it will be possible to integrate pathway information in a manner that would be useful in e-Science. In many aspects, pathway data form the nexus of industrial scale in silico biology, and therefore are a microcosm of bioinformatics within e-Science. We use the BioPAX initiative as an exemplar of the kind of semantic activity that enables e-Science to happen and we explore BioPAX's ability to deliver on the requirements of e-Science via use of its BioPAX ontology. The term e-Science is used to describe both the pursuit of global, collaborative in silico science and the computational infra-structure to support it. There are several factors present in the development of e-Science. Typically, such endeavors are computationally intensive and are carried out in highly distributed network environments that tend to use large, heterogeneous data sets [In this paper, we test the semantic integration capability of pathway data for e-Science. In particular, we explore the utility of the BioPAX ontology for providing a ata sets ,2. The iata sets .in silico experiments necessitates a transition from hand-crafted single sequence analyses towards systems level approaches. e-Science is bringing about the industrialization of bioinformatics by providing the infrastructure to support these kinds of in silico experiments. It is easy to see how bioinformatics fits into this paradigm; it has highly distributed and heterogeneous data, along with communities of people working together on biological problems . Indeed,In 2002, Stein  called fpost genomic era has provided the foundation for modeling the interactions of genes and their products in the whole cell. In addition, transcriptome analysis through microarray experiments has produced large volumes of data which have been used effectively and have transformed the field of experimental biology. Coupled with the catalogues of genes, the further layers of 'omic data has begun to provide enough information for the systems approach to molecular biology.This kind of industrialization, a result of the high-throughput processing of data and the supporting infrastructure, has already been seen, in particular in the 'omics. The catalogues of the genes present in an organism that are now available for many species in this The factors described above, computationally intensive research, highly distributed network environments, catalogues of organisms' genes, heterogeneous data sets, etc., have come together to create a situation where information about biological networks, of which pathways are fragmentary views, is both the goal of biology and the means through which biologists ask questions.in vivo cellular network. These fragments are the commonplace pathways seen in numerous papers, presentations and text-books. It is important to remember, however, that they are a conceptualization; simply a way that biologists describe the cellular molecular domain. Nevertheless, they form a point at which genes, gene products, small molecules, reactions, etc. all come together in a virtual organization. Pathways are the fundamental form that knowledge must take for the needs of systems biology; the entities in pathways and the data, such as equilibria, rate constants, etc. are all vital for the pursuit of systems biology.For most biologists, these networks are conceptualized as fragments or views of parts of the The move towards systems biology has begun to capitalize on the availability of genomic and other information to start mathematically modeling genetic, metabolic and macro-molecular  networks. Data on these networks are recorded as pathways. These pathway models emphasize systems of components rather than the components themselves. It is possible only because the information that has been gathered by molecular biologists over the past twenty years is now readily available.Pathways are the nexus, the semantic communication point in this endeavor. If we are to model genetic, metabolic and macromolecular networks, then we have to assemble our knowledge from pathway data to reform these networks. e-Science infra-structure is necessary for an industrial approach to systems biology, but it is not in itself sufficient. Unless the data can be co-ordinated, especially at a semantic level, the wealth of biological data will not offer up its knowledge to biologists. It is vital, therefore, that we can reliably interpret the semantics of molecular biological data, particularly at the pathway level.Unfortunately, pathway information is scattered across many disparate database resources and tends to be conceptualized in different ways, each with its own semantic and syntactic representation. Metabolic pathways capture the precise way in which one molecule is converted into another in a series of biochemical reactions; molecular interaction databases are typically binary relationships, such as protein-protein interactions, or protein-DNA interactions; gene regulatory networks will represent the connections between transcription factors and the genes whose transcription they activate or repress. Signaling pathway representations, which capture the ways in which cells respond to their environment can range from vague or general representations of the form "there's an activation chain in which A activates B activates C" to specific and detailed representations involving a series of complex binding reactions and protein post-translational modifications.For all these pathway data to be of use to systems biology efforts and e-Science, the following requirements must be met:• Common conceptual framework. A common model of the entities and their relationships is needed so that all the elements of pathways  are all interpreted in the same way.referential integrity.• Common instances. Once a common conceptualization of the things within the domain of pathways and the relationships among these things is made, the model must be populated. Each resource will have its own conceptual framework that holds data. The same data instances will appear in different resources. When united in one resource it is necessary to know that instances are not redundantly represented in any manner. In database terms this refers to • Common vocabulary. Both the conceptual framework and the instances it holds need a common vocabulary with which to refer to 'things' in the model. This will include common terms for pathways, small and large molecules; atoms and ions; etc.• Common format. All the data must have the same format. All resources must present the data model and the values within that model in a common form with a common interpretation. XML, RDF, OWL and CSV are all different formats which to a greater or lesser degree can represent data. This refers to the syntax of the language.If all pathway resources had these aspects in common, it would mean that data from any pathway resource, be it metabolic, regulatory or signaling, could be integrated and interoperated in order to pose research questions.A large proportion of the concepts that make up the broad definition of the systems biology approach are outlined above. There is, however, no one resource for the network of macro- and small-molecules, together with genes. The numerous databases holding subsets of these data need to be integrated at each level in order to enable systems biology in an e-Science context. In the next section, we describe the BioPAX initiative which aims to define a conceptual framework, or semantic model, that spans these conceptual domain boundaries and includes the definition of the requisite common terms and common data format. This common language would facilitate the exchange and aggregation of pathway knowledge initially, and the assembly of this knowledge in new ways in order to increase the body of scientific knowledge ultimately.The BioPAX initiative was undertaken to address the issues of interoperation between pathway data resources. A BioPAX Workgroup oversees the creation of a formal, open-source standard for the representation of biological pathways in a form that can support all pathway data; thus to provide a common conceptual framework, a set of common terms and a common format for exchange and integration. This means that access to all pathway databases in BioPAX format can be achieved with a single parser. Alternatively parsers would be needed by each user for each pathway database. The BioPAX initiative also sought to provide a representation for new data providers and thereby eliminate the mapping of concepts and terms from a native database format to BioPAX.In support of future uses of pathway data, the BioPAX Workgroup included machine computability among its design principles . This reAs OWL was soon to become a W3C recommendation for the standard web ontology language , OWL satTo understand the needs of pathway research within e-Science and the issues that arise in meeting these requirements, we present an example at each level of pathway integration mentioned above: conceptual framework, instances, vocabulary, and syntax. First we show how pathway database sources differ at each level, then illustrate how BioPAX addresses these requirements at each level.To illustrate the semantic difference in database conceptualizations, consider the insulin signaling pathway and the glycolysis pathway shown in figures, While the difference at a conceptual level may be expected between such different categories of pathway types, there are also differences between conceptualizations of pathways of the same type, but defined by different researchers. Because BioCarta does not make a machine readable format available, we will exemplify this with the glycolysis pathway in KEGG ,22 and Hβ-D-glucose-6-phosphate while the other database, KEGG, uses the vocabulary term β-D-Glucose-6P. It is clear that we are referring to the same molecule, i.e. the same real world class of instances, but the vocabulary term used to name these instances differs and while this difference is insignificant for a human reader it is significant for computational processing. The syntax in KEGG is XML and a biochemical reaction is defined as an XML ELEMENT. In KEGG reaction elements have two components, a substrate and a product. The substrate and product elements each have one required attribute, a name which is a KEGG identifier. The data instances in the two resources, for example the representation of β-D-glucose-6-phosphate, obviously refer to the same thing. It is, however, hard to do this automatically at the computational level. This semantic integration of data instances  is one of the deepest problems in the industrialization of bioinformatics.To illustrate the issues at the instance, vocabulary, and syntax levels, we focus on the representation of a single reaction, E.C. # 5.3.1.9, and compare its representation in these two databases. One database, HumanCyc, uses the vocabulary term In BioCyc, a collection of organism-specific pathway genome databases (PGDB), including HumanCyc, a biochemical reaction is represented as an ENZYMATIC-REACTION in OCELOT . Ocelot Although there is a clear difference in visual design between the KEGG and BioCyc glycolysis pathway, fundamentally, these are both representations of a biochemical reaction within a metabolic pathway, and as such, the underlying conceptualization of the pathway data they include is very similar, with largely comparable details accessible by clicking through on the interface.In this section, we have given an example to show what we mean by biochemical reactions being represented differently by different databases at the four levels, conceptualization, instances, vocabulary and syntax. Now we will describe BioPAX followed by an example of how to represent one of the biochemical reactions from HumanCyc in BioPAX. Following this example, you should be able to do the same for a biochemical reaction from KEGG or one of the other databases.There are two top level classes in the BioPAX ontology: entity and utilityClass. Entities describe the biology while the utility classes are there to record knowledge about the pathway data such as cross references to other databases, evidence codes, and experimental conditions. Pathways are a subclass of entity, along with two sibling classes, interaction and physicalEntity. A pathway has components, PATHWAY-COMPONENTS. The PATHWAY-COMPONENTS will be instances of the class pathwayStep, a utility class. Each pathwayStep contains a set of STEP-INTERACTIONS that describe the physical interactions, such as catalysis, modulation, biochemical reaction, complex assembly, and transport that make up that step in the pathway, or another pathway. A pathway, such as glycolysis, MAPK, or apoptosis is composed of instances of interactions. Interactions can occur between entities so that interactions of interactions and interactions of pathway can be represented. A note on notation: classes in BioPAX use camel-back notation; properties are all upper case with a hyphen separating each word.β-D-glucose-6-phosphate and D-fructose-6-phosphate, and one an instance of the class protein, phosphoglucose isomerase, (enzyme is not explicitly represented as a subclass of protein in BioPAX). This biochemical reaction converts β-D-glucose-6-phosphate into D-fructose-6-phosphate. The reaction itself is controlled by the enzyme phosphoglucose isomerase. Once the physical entities that participate in the reactions are identified, together with the interaction roles they play in the reaction, we can represent them in BioPAX. Thus in BioPAX, an instance is created of biochemicalReaction with the property LEFT filled with β-D-glucose-6-phosphate, the property RIGHT filled by D-fructose-6-phosphate, and E.C.# property filled with 5.3.1.9. An instance of the catalysis class is created with property CONTROLLER filled with phosphoglucose isomerase, and the property CONTROLLED with the reaction name PGLUCISOM-RXN.Figure In the previous section we describe how a reaction within a pathway is represented in BioPAX. In this section we look at how pathways are represented. In BioPAX, pathways are defined by their components (the property PATHWAY-COMPONENTS). The components of pathways are defined to be any of the classes of interaction, pathwayStep or pathway. Pathway steps are defined as a subclass of utilityClass. The utilityClass is defined in the BioPAX documentation to be a meta-class to assist with the description of pathways, see the discussion section for more on this topic. Each pathwayStep defines a property called NEXT-STEP which provides the order in the pathway and a list of properties that define the set of interactions that occur at that step. This property is named STEP-INTERACTIONS. The interactions at each step can be any subclass of the class physicalInteraction  Each of these physical interactions has participants which are instances of the one of the subclasses of physicalInteraction or instances of the class physicalEntityParticipant. The class physicalEntityParticipant is also a utilityClass and is used to describe a physical entity in the context of an interaction. A physicalEntityParticipant specifies the physicalEntity in the context of an interaction by adding the properties CELLULAR-LOCATION and STOICHIOMETRIC-COEFFICIENT. Figure Above we presented the BioPAX class hierarchy and pathway representation. Now we will look at it from the perspective of the levels of integration required for e-Science. First we want to make clear that the conceptualization as described above defines BioPAX as an OWL ontology in terms of its conceptual framework, the instances , the vocabulary terms and the syntax. The syntax used for BioPAX is RDF/XML which is a serialization of OWL. OWL and RDF are graph representations, XML is a tree structure. RDF and OWL must be serialized when written for export. It is important to note that BioPAX defines the classes in which to represent pathway data, the instances are provided by the databases.The major contribution of BioPAX to e-Science is that it provides a single conceptual framework for the various multiple conceptualizations of pathway databases, i.e. metabolic, molecular interaction, signal transduction and regulatory pathways. It also provides a common format. While the BioPAX ontology has a common terminology at the class level, there is no attempt to provide a common view of instances within those classes. The question then is, now that we have data from different conceptualizations of pathways into the BioPAX representation, can we do in silico science? Can we deliver pathway information to industrial scale analyses? Does BioPAX fulfill the requirements set forth in the introduction?In this section, we present some experiments using the BioPAX ontology; its instance data and Description Logic reasoning machinery in order to answer questions about pathways. We shall see if BioPAX can match the needs of e-Science outlined in the introduction and the goals of BioPAX itself.instance of the class smallMolecule. The instance in this case and all others in BioPAX data should be classes. We will see later that we have to perform some tricks with BioPAX data because classes are treated as instances.We now have the BioPAX ontology that acts as a common conceptualization for a large quantity of data. These data are encoded as instances of the classes in the BioPAX ontology. In the ontological sense, these are not really instances. For example, we have ATP as an restrictions upon classes through combinations of properties and the instances of other classes that act as successors to those properties. Restrictions are aptly named as they restrict what instances can be members of a class. These restrictions or conditions take two forms:In OWL, a class represents a set of instances. Classes of instances are described by placing what OWL calls necessary conditions; that is, any instance must fulfill that condition in order to be a class member. It is not true, however, that fulfilling that condition is enough to recognize an instance as a member of that class. For example, all hexokinase proteins phosphorylate glucose, but the ability to phosphorylate glucose is not enough to recognize an enzyme as a hexokinase.1. Conditions can be simply necessary and sufficient conditions for class membership; that is not only must any instance of a class hold that condition, but any instance holding that condition or combination of conditions can be recognized by a software reasoner to be a member of the class. For example, the class Enzyme can be defined to be any protein that catalyses a reaction.  Thus, the ability to catalyze a reaction is enough to recognize a protein as an enzyme and all enzymes must catalyze reactions.2. Conditions can be both As OWL classes describe sets of instances, we can use classes to ask questions of the instance data. Description Logic ontologies have been used in this manner before in systems such as TAMBIS . Here, w1. What pathways include the vitamin D receptor? That is, what are the pathways that include a step in which the vitamin D receptor participates?2. What pathways are implied by the gene products of a set of genes? That is, what is the set of pathways that have steps in which a gene product from a set of genes participates?3. What pathways involve cholesterol? That is, what is the set of pathways in which there is a step that involves cholesterol?4. What pathways would I expect to see invoked when I add 9-cis-retonic acid to a macrophage? That is, what set of pathways are used in a macrophage and are induced by addition of 9-cis-retonic acid?5. What pathways exist in a particular cell? That is, what is the set of pathways known to exist in a particular cell type?6. What pathways exist within particular types of tissues? That is, what is the set of pathways that is the union of all sets of pathways seen in the range of cell types in a tissue?7. What pathways involve retinoic acid or any of its derivatives (it's oxidative breakdown products)? That is, the set of pathways in which retinoic acid or any of its derivatives is a component of a pathway step?8. What databases do these instances come from? That is, what is the set of databases from which any of the instances in a set of pathways originate?9. We have a phenotype, what are the pathways which involve a specified metabolite that might be linked to this effect? That is, what is the set of pathways that is known to be linked to a particular phenotype?10. What are the metabolites that a set of pathways has in common? That is, what is the set of small molecules that participate in a step of a set of pathways?11. What is the set of pathways that are active in the particular time interval?common conceptualization for these data but also as the mechanism for querying these data. Current use of the BioPAX ontology does not take advantage of what OWL can do. Rather than a static artifact, we can use the BioPAX ontology as a dynamic, flexible software component. We can use it to recover sets of pathways based on biological descriptions inherent in the BioPAX ontology itself.This list is by no means exhaustive, but provides a sample of the kind of questions that one might ask of pathway data. Other researchers who have attempted to ask such questions have used the BioPAX data in its RDF form and used RDF technology, such as RDF stores and RDF query languages to ask these questions . Here weFor testing, we used a small subset of the BioCyc BioPAX data, the Argobacterium tumefaciens C58 (ArgoCyc) glycolysis pathway. We chose a subset where we could count the number of instances we expected to retrieve. The following are defined classes, we created, which function as queries over the instance data :1. ATPSmallMolecule2. ATPPhysicalEntityParticipant3. ATPBiochemicalReaction4. ATPCatalysis5. ATPPathwayStep6. PathwayInvolvingATPBiochemicalReactionEach of these classes is described such that instances are retrieved based on the involvement of ATP. So, small molecules that are ATP, reactions involving ATP, pathway steps involving ATP and finally pathways involving ATP are all retrieved from the database. The following list shows what was returned for each of the classes defined above:ATPSmallMoleculeReturns:   smallMolecule127380 ("ATP")ATPPhysicalEntityParticipantReturns:   phys-ent-participant127589            ("6-phosphofructokinase")ATPPathwayStepReturns:   pathwayStep127652 ("glycolysis I")   pathwayStep127583 ("glycolysis I")   pathwayStep127696 ("glycolysis I")ATPCatalysisReturns:   catalysis127540 ("pyruvate kinase")   catalysis127476 ("phosphoglycerate kinase")ATPBiochemicalReactionReturns:   biochemicalReaction127530            ("Pyruvate kinase")   biochemicalReaction127468            ("Phosphoglycerate kinase")   biochemicalReaction127378            ("6-phosphofructokinase")PathwayInvolvingATPBiochemicalReactionReturns:   pathway127351 ("glycolysis I")Note: The string value of the BioPAX property NAME is shown in parentheses for the readers' convenience. The OWL file used in this example is additional file 1. PathwayInvolvingATPBiochemicalReaction or PathwayInvolvingH2OBiochemicalReaction or PathwayInvolvingNADPlusBiochemicalReaction or PathwayInvolvingOxygen2BiochemicalReaction• pathway79839, pathway17042 2. CholesterolSmallMolecule• smallMolecule79898 ("cholesterol")3. ATPCatalysis• catalysis17524, catalysis17369, catalysis17161, catalysis17364, catalysis17718, catalysis17511, catalysis17173, catalysis17149, catalysis173534. H2OCatalysis• catalysis17789, catalysis17767, catalysis17772, catalysis17778, catalysis178015. NADPlusCatalysis• catalysis17818, catalysis17328, catalysis17333, catalysis17304, catalysis17279, catalysis17686, catalysis17338, catalysis17289, catalysis17831, catalysis17309, catalysis17274, catalysis17087, catalysis17294, catalysis17299, catalysis17284, catalysis178266. Oxygen2Catalysis• catalysis80292, catalysis80235, catalysis80214, catalysis80101, catalysis80170, catalysis80116, catalysis79970, catalysis80161, catalysis80246, catalysis80152, catalysis80125The following used the entire HumanCyc data set, which can be found at . As we aClass: PathwayInvolvingCholesterolBiochemicalReaction1. • pathway106063Class: NoradrenalinePathwayStep2. • pathwayStep106053 pathwayStep106054Class: PathwayInvolvingNoradrenalineBiochemicalReaction3. • pathway106051Class: PathwaysInvolvingH2OBiochemicalReaction 4. • pathway106411 pathway106531 pathway105873 pathway106934 pathway106008 pathway105463 pathway105937 pathway106460 pathway105742 pathway106804 pathway106785 pathway106682 pathway106296 pathway105696 pathway107053 pathway106919 pathway106379 pathway106331 pathway106791 pathway106477 pathway106150 pathway107067 pathway106123 pathway105810 pathway106321 pathway106451 pathway106689 pathway105689 pathway106851 pathway105642 pathway106544 pathway106002 pathway106421 pathway105825 pathway106354 pathway106057 pathway105681 pathway106037 pathway106829 pathway105734 pathway106394 pathway106206 pathway106249 pathway106565 pathway106306 pathway105667 pathway107061 pathway106585 pathway106365 pathway107134 pathway106717 pathway106656 pathway106581 pathway107152 pathway107079 pathway106553 pathway106384 pathway107093 pathway106814 pathway106389 pathway107017 pathway105704 pathway105715 pathway105798 pathway106571 pathway105955 pathway106284 pathway106268 pathway105792 pathway107031 pathway106645 pathway106776 pathway105457 pathway106337 pathway105452 pathway106908 pathway106807 pathway106833 pathway106981 pathway105885 pathway106195 pathway106416 pathway106432 pathway106499 pathway106428 pathway106729 pathway105473 pathway105727 pathway106596 pathway105677 pathway107180 pathway105721 pathway106467 pathway105655 pathway106548 pathway107036 pathway106219 pathway106939 pathway106399 pathway105979 pathway106756 pathway105966 pathway106494 pathway106347 pathway106441 pathway105674 pathway105928 pathway106671 pathway106664 pathway105971 pathway106028 pathway105684 pathway106015 pathway105757 pathway1066345. • PathwayInvolvingNADPlusBiochemicalReaction pathway106389 pathway107017 pathway105798 pathway106531 pathway105873 pathway105955 pathway106460 pathway106750 pathway105742 pathway106268 pathway107053 pathway106645 pathway106776 pathway106919 pathway105457 pathway106379 pathway106130 pathway106150 pathway107067 pathway106765 pathway106451 pathway105810 pathway106689 pathway106981 pathway106902 pathway106954 pathway106170 pathway106421 pathway106416 pathway106432 pathway106428 pathway106729 pathway105825 pathway106037 pathway106596 pathway106394 pathway105734 pathway106467 pathway107172 pathway106219 pathway105667 pathway105979 pathway106585 pathway107134 pathway106365 pathway105966 pathway106486 pathway106441 pathway106656 pathway107079 pathway106970 pathway106949 pathway106553 pathway106617 pathway106671 pathway106028 pathway106384 pathway106015 pathway107093 pathway106634and bp2:PATHWAY-COMPONENTS some H2OPathwayStep and PathwayInvolvingNADPlusBiochemicalReaction EquivalentTo bp2:pathway and bp2:PATHWAY-COMPONENTS some NADPlusPathwayStep)6. PathwayInvolvingH2OBiochemicalReaction and PathwayInvolvingNADPlusBiochemicalReaction  are somewhat ugly . We have not, as yet, explored whether we can answer questions asking for pathways that involve a small molecule or any of its derivatives.In these experiments we did not actually integrate any data from more than one resource. We only queried data from one resource. Proper integration is our next step. We know this is possible by writing more complex definitions in the current style. This would simply use constructs such as hasName (name-in-db-1 or name-in-db2). This is performing the instance reconciliation we stated as one of the necessary steps for true integration. This approach obviously has its scalability issues. Until such reconciliation is done by the bioinformatics as a whole it will remain as a significant barrier to analytical work.The points for discussion arising from this work are not so much that it worked, but what we had to do in order for it to work. Examining what we had to do in order to make our approach work, an approach that should be inherent to BioPAX due to its use of a Description Logic, is informative to the future directions of BioPAX.First, however, a question that should be asked is whether this approach is at all useful or justified? Scalability is an issue. We performed these experiments in memory and this limits the number of instances that can be queried. Tools such as the Instance Store  would liOur definitions are in terms of the data about reality, not the reality itself. We have already observed that defining a class ATPSmallMolecule as an instance that has the name "ATP" is not elegant to say the least (see section 'An Experiment with BioPAX'). ATP is really defined by a certain chemical structure of atoms and covalent bonds. Using the name, however, was one of only two ways we could use the BioPAX instance data to define ATPSmallMolecule. In this experiment, we are restricted to what the BioPAX instance data allowed us to do.BioPAX allows for use of other controlled vocabularies to label things in its instance data. For example, locations are given by an instance of controlledVocabularyTerm. This sort of works, but what we are stating is that a molecule's location is a term of a controlled vocabulary, not some part of a cell. Much better, in modeling terms, would be to include or refer to external ontologies and use them as ontologies. So, for physical entities, portions of the OBO version of the Chemicals of Biological Interest (ChEBI) dictionary  would beWhat effect would inclusion of external ontology as ontology have? We could, for example, very easily make a class CytosolicKinaseProtein by simply referring, via restrictions, to instances of kinase activity and Cytosol from GO's molecular function and cellular component ontology respectively. Similarly, ATPSMallMolecule, instead of being defined by the name of an individual, would simply be an instance of the ChEBI class for ATP, namely CHEBI:15422. Similarly, we could describe a class of pathways by describing it as performing a biological process of, for instance GO's Glycogen Biosynthesis.nominals  to make class definitions [As we describe for the case of defining ATP, we have used what are known as initions . It woulMappings to such an ontology would have to be performed as part of the conversion from client resource to the BioPAX ontology target. Not only would this make the ontological modeling easier, but perform reconciliation at the level of values/instances at the same time.What are described as "instances" in BioPAX are really classes. All the nominals we used are really classes: ATP; reactions; interactions; pathway steps are really entities that describe sets of instances in the biological world. As described, working at the class level might actually make this work easier. Again, BioPAX has taken a database view of instances. HumanCyc, for example, contains a data instance of ATP. This view has been propagated into the BioPAX ontology and instance data.We moved pathwayStep to be a kind of pathway. In these experiments this worked. There is an issue, however, of whether a pathway step is a kind of pathway or a part of a pathway. If, in our conceptualization of pathways, we can have a single step pathway, then it is legitimate that a pathwayStep is a kind of pathway. It would also be legitimate (we can find no counter examples) that any one step pathway is part of a greater pathway. In the end, such a choice probably does not make any practical difference.This brings in some upper level ontology issues. Most upper level ontologies -43 make Based on these discussions and on points raised during this paper, we would advocate the following changes be made to the BioPAX ontology:1. The use of an upper level ontology to draw distinctions between classes of instances, such as continuants and occurants, independent and dependent continuants etc.2. That the BioPAX ontology separates descriptions of data from descriptions of biology.3. The explicit inclusion of other ontologies to provide classes needed to describe the data about pathways.4. That the instances of BioPAX more reliably represent actual instances rather than classes .5. That a more careful use of OWL's disjoint axiom be used throughout the ontology. The BioPAX ontology has the basis for a well structured ontology based on trees of primitive classes . Along w6. Careful use of OWL semantics could make explicit some of the semantics held within the comments of the BioPAX ontology.7. Careful use of OWL semantics, such that there is one BioPAX ontology standard, not two and possibly more with each level release.In this paper we have explored the potential for the BioPAX initiative and its ontology to deliver the pathway data necessary for systems style bioinformatics enabled by e-Science. In particular we have looked at the semantics of such integration and how OWL-DL can be used to represent those semantics and be exploited to ask semantically rich questions. We have used the BioPAX ontology to ask questions that deliver sets of pathways. These pathways themselves can then be manipulated to deliver new sets of pathways. The ontology, using its biological aspects, allows sets of pathways or their various components to be described biologically. These sets of pathways can be used as parts of approaches to many analyses. Furthermore, until the BioPAX ontology includes the notions of gene, sequence, chromosome and location, it will not be possible to describe pathways in some of the important ways needed for biological investigations.common conceptualization of the domain and in one common syntax. It does, however, fail at the level of reconciling pathway instances. BioPAX almost provides for this by its inclusion of external controlled vocabularies, but not in the way really desired by this approach to data. Of course, BioPAX does not even attempt to reconcile the labels used for all the entities appearing within pathway data. This is a task beyond its means. We have, however, the technical means to achieve this goal, if not the will and financial means. The current reconciliation makes it possible to query across resources, but a deeper reconciliation would make such querying across resources easier and significantly powerful. Another conclusion to be drawn from this work are ontological conclusions about the BioPAX ontology:The BioPAX initiative succeeds in its basic goal of allowing pathway interoperation. There are now multiple pathway databases, increasing in number, whose data are compliant with the BioPAX ontology. As we described, pathways form a data nexus for modern bioinformatics in an e-Science setting, so this is a significant achievement. The BioPAX ontology acts as a model that allows a wide variety of pathway data to be assembled according to one 1. The BioPAX ontology conflates modeling of data and modeling of biology.2. By modeling each separately, a cleaner and easier formulation of queries and description of data would be possible.3. Raising most instance data to the class level would be a more true reflection of the biological world, but have no absolute effect on the queries possible. In fact, much of the technology would be more effective and modeling would be easier if class things were class things.4. A modular approach to other ontologies would again reflect the biological world and engender easier queries. Rather than modeling other ontologies as data, importing them as ontologies at the class level would enable richer descriptions to be made during queries. This would have no effect on mapping contributing resources into BioPAX. There are, however, issues of maintenance and synchrony of versions.5. A closer attention to OWL semantics would make possible a greater range of queries. Problems were caused by the misuse or lack of use of features available in the OWL semantics.6. Publish one BioPAX ontology; currently there are two separate BioPAX namespaces, one for BioPAX level one and another for BioPAX level 2.We would advocate, therefore, a re-modeling of the BioPAX ontology. This has to, of course, take the current data providers along. Interestingly, however, much of the re-modeling can be accomplished with little disruption. Many of the current classes are the ones needed. The changes made in these experiments did not necessitate any changes to the data. Simple re-arrangements of the classes are simply reflected automatically in the instance data. New asserted classes would, however, mean changes in instance data encoding.The BioPAX initiative is an exemplar of the needs of modern, systems level bioinformatics in an industrial, e-Science form. It is typical of bioinformatics data in being highly distributed and highly heterogeneous at all levels. The BioPAX ontology, despite flaws, makes it possible to deliver sets of pathways, described and queried based on biology to bioinformatics analyses.We have the following resources from which to ask these questions:• The BioPAX ontology.• The BioPAX data from HumanCyc .• The Pellet  and FaCT• Protégé 4Alpha .We use both the BioPAX level one and level two ontologies. Any new classes we make, we have placed in a additional file We wish to form classes that represented at least some of the types of questions listed above. This does, however, break one of the directives in the BioPAX documentation  : "changiconceptual lego [In order to answer the kinds of questions listed above, we form new classes that describe sets of entities involved in pathways in the BioPAX instances files. These are sets of pathway instances described in terms of the classes and instances that already exist in the ontology and the instance data. This is typical ual lego  in that entities.The general plan is, therefore, to create some classes that describe pathways. These pathways will be described in terms of, for instance: A catalysis they perform; a macromolecule they involve; a small molecule they involve; etc., as seen in the section BioPAX. The BioPAX ontology describes these types of The BioPAX ontology also has a broad collection of properties or relationships that link the instances of these classes together. In the BioPAX ontology, these have been encoded in two ways:1. As restrictions on classes. This means that if any instance of the property exists on an instance of the class, then each and every instance of that class must hold that property.2. As domain constraints. This means that if any instance is found to hold that property, then it is implied to be an instance of the domain class.must have a NAME. This is in the form of a restriction. The property PATHWAY-COMPONENTS is encoded with domain and range constraints. The domain is pathway and the range is interaction or pathwayStep or pathway. In OWL, this means that any instance that holds a property PATHWAY-COMPONENTS is reasoned to be a member of the class pathway. Note that this is not saying that all pathways must have a pathwayStep or an interaction. In reality they might well do so, but in BioPAX data it is possible for a pathway to be described without there being any data about its component steps or interactions. This is a compromise between modeling some perceived biological reality and modeling the data. Essentially, we can interpret this as meaning "we do not know what the parts of a pathway are, but we know the pathway exists.".This gives a flexible approach to encoding knowledge. The first statement is very strong and the second somewhat weaker, but with potentially profound effects . For exaOur goal is to describe pathways. The following sequence of connections exist:• Pathways have pathway components that are pathway steps.• Pathway steps are made of step interactions.• Step interactions are made up of interactions .• Interactions have participants.• Participants are physical entities of the class small molecule, protein, complex, RNA and DNA.join together descriptions to describe sets of pathway instances. We wish to retrieve data that are represented as instances. So, our classes have to have enough description by which to recognize instances as members of the class. This means we have to use conditions that are both necessary and sufficient. We will discuss the ontological soundness of this in the discussion section.These classes have other elements to their descriptions, but here we are principally concerned in how to If we wish to have a class for the small molecule ATP, we do the following:Class: ATPEquivalentTo: physicalEntityParticipantthat NAME value "ATP"   We are saying that any individual that is a smallMolecule and has the NAME "ATP" is a member of the class ATP. Obviously this is no real definition of the class ATP. It is, however, enough to satisfy the query "give me ATP chemical" in this particular data set. The BioPAX model uses references to the contents of the originating database that detail the primary identifier within a resource for a particular entity. We could use a combination of database identifier and instances identifier to define ATPSmallMolecule (or any other such instance). This could arguably be better modeling, but in this case has the same effect. We can extend our definition to accommodate other data descriptions. All other restrictions on the physicalEntityParticipant class can remain unspecified; they are inherited, but do not need to be specialized unnecessarily. One of OWL's strong points is its ability to model incomplete knowledge . We can So, we can create physicalEntityParticipant of various kinds. Once we have these classes, we can begin to make more pathway orientated classes. A biochemicalReaction has PARTICIPANTS that are physicalEntityParticipant. The PARTICIPANTS property of biochemicalReaction has two sub-properties, LEFT and RIGHT that describe physicalEntityParticipant on either side of a reaction. Using the super-property, it is easy to describe reactions that involve any kind of physicalEntityParticipant on either side of the reaction in any cellular location. In the example below, bp2: is a prefix used for QNames, the qualified name of the class, which is its URI.Class: ATPSmallMoleculeEquivalentTo:that   bp2:smallMolecule value "ATP" string   bp2:NAME Class: ATPBiochemicalReactionEquivalentTo:that   bp2:biochemicalReaction some   bp2:LEFT    ATPPhysicalEntityParticipantClass: ATPCatalysisEquivalentTo:that   bp2:catalysis some   bp2:CONTROLLED    ATPBiochemchemicalReactionClass: ATPPEPEquivalentTo:that   bp2:physicalEntityParticipant some   bp2:PHYSICAL-ENTITY    ATPSmallMoleculeClass: ATPPathwayStepEquivalentTo:that   bp2:pathwayStep some   bp2:STEP-INTERACTIONS or ATPBiochemchemicalReaction)    and these classes are disjoint. One of the classes we used in the construction of new classes  is a kind of utilityClass. As physicalEntityParticipant are defined in terms of physicalEntity, this caused the reasoner to return inconsistency errors. Consequently, the physicalEntityParticipant was moved from utilityClass to become a subclass of physicalEntity.• A similar inconsistency occurred when pathwayStep was analyzed. We made a pathwayStep a subclass of pathway.• The disjoint axiom between physicalEntityParticipant and physicalEntity was removed.• The disjoint axiom from pathway and interaction was removed.• The class physicalEntity was made disjoint with pathway and interaction.n copies of this property. Implicitly, the instance that qualifies this cardinality is owl:Thing. That is, only one property linking an instance of physicalEntityParticipant to an instance of owl:Thing can be present. In theory, this means any instance, a HoverCraft for example, can act as successor to the PHYSICAL-ENTITY property. That is not what what we want. What we want is qualified cardinality restraints, where we specify what type of instance acts as a successor. In this case, we want an instance of physicalEntity to act as successor. Changing BioPAX from the OWL 1.0 cardinality constraint to the more precise qualified cardinality restrictions (QCR) causes inconsistency errors with some reasoners. FaCT++ [• Many properties have ranges that are other BioPAX classes. For example, the property PHYSICAL-ENTITY has the domain of physicalEntityParticipant. physicalEntityParticipant itself has a restriction of PHYSICAL-ENTITY max 1 owl:Thing. The OWL 1.1 standard only allows cardinality restraints that say any instance holds . FaCT++  can reas. FaCT++ . We wish• In biopax-level1.owl, the property DELTA-G is defined as a DatatypeProperty and in biopax-level2.owl, it is defined as an ObjectProperty. The file biopax-level2.owl was modified to use the DatatypeProperty definition. Without this correction, this mistake causes the merged level 1 and level 2 ontologies to be OWL-Full.• The following two errors cause the reasoner to complain about redefinitions of XML schema types and thus make the ontology OWL-Full (again):"/>.- A fragment of an orphaned range definition needs to be removed from the biopax level 2 file: <owl:DatatypeProperty rdf:about="- All occurrences of the string &xsd; need to be replaced with the string xsd:.Changes were also needed in the HumanCyc.owl file (which contains the instance data) in order to run these experiments:• The name of the file was changed from biopax.owl to HumanCyc.owl. This was done exclusively for the convenience of the authors who were working with multiple organism and pathway files. SRI, International Inc. The supplier of the BioCyc family of databases reuses the filename biopax.owl for all BioPAX formatted files. Whether they are one pathway or all the pathways within an organism, all are all named biopax.owl. This is only a problem for the human reader; the important name, i.e. the namespace is defined within the file.• The RDF data type definitions were missing from DELTA-G, MOLECULAR-WEIGHT and STOICHIOMETRIC-COEFFICIENT. This was corrected by adding rdf:datatype=xsd:double to each instance.• All occurrences of the string level1 were replaced with level2, being the more recent and complete BioPAX ontology. This was an easy work around the problem that BioPAX level 1 and BioPAX level 2 are two independent ontologies (namespaces) notwithstanding the intent to be backward compatible. While this is a BioPAX mistake, it was easier in this case to alter the reference in the data.Not all of these changes are ontologically valid. Most of them were made simply to get the queries to work. Also, to make the ontology more explicit, we used the QCR feature from the proposed OWL 1.1 extension . ObviousThe authors declare that they have no competing interests.The authors collaborated in all aspects of the work reported on in this paper.ArgoCyc Glycolysis Pathway. This file contains the glycolysis pathway of the organism Argobacterium tumefaciens C58. The file is a subset of the organism specific ArgoCyc database of metabolic pathways from SRI, International. The file was modified from the original as described in the text.Click here for fileCholesterol Biosynthesis Pathway in Human. This file contains the cholesterol biosynthesis pathway in humans. The file is a subset of HumanCyc from SRI, International. The file was modified from the original as described in the text.Click here for filesuperpathway of glycolosis, pyruvate dehydrogenase, TCA, and glyoxylate bypass. This tile contains the superpathway of glycolosis, pyruvate dehydrogenase, TCA, and glyoxylate bypass in humans. The file is a subset of HumanCyc from SRI, International. The file was modified from the original as described in the text.Click here for fileEMPWR BMC Experiment Ontology. This file demonstrates how one can extend the BioPAX ontology by adding additional classes and properties that enable the user to query the pathway instance data. The classes build upon a modified BioPAX level 2. The modified BioPAX level 2 file it builds upon is biopax-level2-EMPWR fresh copy.owlClick here for file