Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery. Increasingly powerful technologies, including genome-wide molecular measurements, have accelerated progress toward a complete map of molecular interaction networks in cells and between cells of many organisms. The growing scale of these maps requires their representation in a form suitable for computer processing, storage and dissemination by means of software systems. The BioPAX project aims to facilitate knowledge representation, systematic collection, integration and wide distribution of pathway data from heterogeneous information sources. This will enable these data to be incorporated into distributed biological information systems that support visualization and analysis. BioPAX supports efforts working toward a complete representation of basic cellular processes. Biology has come a long way since the BoehringerMannheim wall chart of metabolic pathways 1 and the Nicholson Metabolic Map 2 . Since then, several groups have developed methods and databases for organizing pathway information 3-16 , but only recently have groups collaborated as part of the BioPAX project to develop a generally accepted standard way of representing these pathway maps. Complete molecular process maps must include all interactions, reactions, dependencies, influence and information flow between pools of molecules in cells and between cells. For ease of use and simplicity of presentation, such network maps are often organized in terms of subnetworks or pathways. Pathways are models delineated within the entire cellular biochemical network that help us describe and understand specific biological processes. Thus, a useful definition of a pathway is a set of interactions between physical or genetic cell components, often describing a cause-and-effect or time-dependent process, that explains observable biological phenomena. How do we represent these pathways in a generally accepted and computable form? Challenges posed by the many fragmented pathway databases The total volume of pathway data mapped by biologists and stored in databases has entered a rapid growth phase, with the number of 9 3 6 VOLUME 28 NUMBER 9 SEPTEMBER 2010 nature biotechnology p e r s p e c t i v e online resources for pathways and molecular interactions increasing 70%, from 190 in 2006 to 325 in 2010 (ref. 17). In addition, molecular profiling methods, such as RNA profiling using microarrays, or protein quantification using mass spectrometry, provide large amounts of information about the dynamics of cellular pathway components and increase the power of pathway analysis techniques 18, 19 . However, this growth poses a formidable challenge for pathway data collection and curation as well as for database, visualization and analysis software, as these data are often fragmented. The principal motivation for building pathway databases and software tools is to facilitate qualitative and quantitative analysis and modeling of large biological systems using a computational approach. Over 300 pathway or molecular interaction-related data resources 17 and many visualization and analysis software tools 3,20-22 have been developed. Unfortunately, most of these databases and tools were originally developed to use their own pathway representation language, resulting in a heterogeneous set of resources that are extremely difficult to combine and use. This has occurred because many different research groups, each with their own system for representing biomolecules and their interactions in a pathway, work independently to collect pathway data recorded in the literature (estimated from text-mining projects 23 to be present in at least 10% of the >20 million articles currently indexed by PubMed). As a result, researchers waste time collecting information from different sources and converting it from one form of representation to another. Fragmented pathway data results in substantial lost opportunity cost. For instance, visualization and analysis tools developed for one pathway database cannot be reused for others, making software development efforts more expensive. Therefore, it is imperative to develop computational methods to cope with both the magnitude and fragmented nature of this expanding, valuable pathway information. Whereas independent research efforts are needed to find the best ways to represent pathways, community coordination and agreement on standard semantics is necessary to be able to efficiently integrate pathway data from multiple sources on a large scale. BioPAX requirements and implementation A common, inclusive and computable pathway data language is necessary to share knowledge about pathway maps and to facilitate integration and use for hypothesis testing in biology 24 . A shared language facilitates communication by reducing the number of translations required to exchange data between multiple sources (Fig. 1) . Developing such a representation is challenging owing to the variety of pathways in biology and the diverse uses of pathway information. Pathway representations frequently use abstractions for metabolic, signaling, gene regulation, protein interaction and genetic interaction, and these serve as a starting point toward a shared language 25 . Also, several variants of this common language may be required to answer relevant research questions in distinct fields of biology, each covering unique levels of detail addressing different uses, but these should be rooted in common principles and must remain compatible. BioPAX addresses these challenges. We developed BioPAX as a shared language to facilitate communication between diverse software systems and to establish standard knowledge representation of pathway information. BioPAX supports representation of metabolic and signaling pathways, molecular and genetic interactions and gene regulation. Relationships between genes, small molecules, complexes and their states (e.g., post-translational protein modifications, mRNA splice variants, cellular location) are described, including the results of events. Details about the BioPAX language are available in online documentation at http://www.biopax.org/. The BioPAX language provides terms and descriptions, to represent many aspects of biological pathways and their annotation. It is implemented as an ontology, a formal system of describing knowledge (Box 1) that helps structure pathway data so that they are more easily processed by computer software (Fig. 2) . It provides a standard syntax used for data exchange that is based on OWL (Web Ontology Language) (Box 1). Finally, it provides a validator that uses a set of rules to verify whether a BioPAX document is complete, consistent and free of common errors. BioPAX is the only community standard for biological pathway exchange to and from databases, but it is related to other standards (discussed below in the "What is not covered?" section). Example of a pathway in BioPAX Pathway models are generally described with text and with network diagrams. Here we use the AKT signaling pathway 26, 27 as an example to show how a typical pathway diagram that can only be interpreted by people (Fig. 3, top left) would be represented using BioPAX (Fig. 3,  right) . The AKT pathway is a cell surface receptor-activated signaling cascade that transduces external signals to intracellular events through a series of steps including protein-protein interactions and protein kinase-mediated phosphorylation. The pathway eventually activates transcription factors, which turn on genes to promote cell survival. By representing the pathway using the BioPAX language ( Fig. 3 and Supplementary Tables 1 and 2) , it can be analyzed by computational approaches, such as pathway analysis of gene expression data. Representing a pathway using the BioPAX language sometimes necessitates being more explicit to avoid capturing inconsistent data. For instance, the typical notion of an 'active protein' is dependent on context, as the same molecule could be active in one cellular context, such as a cellular compartment with a set of potentially interacting molecules, and inactive in another context. Thus, capturing the specific mechanism of activation, such as phosphorylation modification, is usually required, and the presence of downstream events that include the modified form signifies that the molecule is active. Interactions where the mechanism of action is unknown can also be specified. BioPAX covers all major concepts familiar to biologists studying pathways, including metabolic and signaling pathways, gene regulatory networks and genetic and molecular interactions (Supplementary Table 3 ). The BioPAX language is distributed as an ontology definition (Fig. 4) with associated documentation, a validator for checking a BioPAX document for errors and other software tools ( Figure 1 BioPAX is a shared language for biological pathways. BioPAX reduces the effort required to efficiently communicate between pathway users, databases and software tools. Without a shared language, each system must speak the language of all other systems in the worst case (black lines). With a shared language, each system only needs to speak that language (central red box). nature biotechnology VOLUME 28 NUMBER 9 SEPTEMBER 2010 9 3 7 p e r s p e c t i v e Pathway abstractions frequently used in several pathway databases and software programs are supported as follows: • Metabolic pathways are described using the 'enzyme, substrate, product' abstraction 28 where substrates and products of a biochemical reaction are often small molecules. An enzyme, often a protein, catalyzes the reaction, and inhibitors and activators can modulate the catalysis event. Metabolic pathways use BioPAX classes: PhysicalEntity, Conversion, Catalysis, Modulation, Pathway. • Signaling pathways involve molecules and complexes participating in biochemical reactions, binding, transportation and catalysis events (Fig. 3) 5, 9, [29] [30] [31] . These pathways may also include descriptions of molecular states (such as cellular location, covalent and noncovalent modifications, as well as fragments of sequence cleaved from a precursor) and generic molecules (such as the family of homologous Wnt proteins). Signaling pathways use BioPAX classes: PhysicalEntity, Conversion, Control, Catalysis, Modulation, MolecularInteraction, Pathway. • Gene regulatory networks involve transcription and translation events and their control 12, 14 . Transcription, translation and other template-directed reactions involving DNA or RNA are captured in a 'template reaction' in BioPAX, which maps a template to its encoded products (e.g., DNA to mRNA). Multiple sequence regions on a single strand of the template, such as promoters, terminators, open reading frames, operons and various reaction machinery binding sites, are active in a template reaction. Transcription factors (generally proteins and complexes), microRNAs and other molecules, participate in a 'template reaction regulation' event. Gene regulatory networks use BioPAX classes: PhysicalEntity, TemplateReaction, TemplateReactionRegulation. • Molecular interactions, notably protein-protein 32-36 and protein-DNA interactions 37 , involve two or more 'physical entities' . BioPAX follows the standard representation scheme of the Proteomics Standards Initiative Molecular Interaction (PSI-MI) format 38 . Molecular interactions use BioPAX classes: PhysicalEntity, MolecularInteraction. • Genetic interactions occur between two genes when the phenotypic consequence of perturbing both genes is different than expected given the phenotypes of each single gene perturbation 39 . BioPAX represents this as a pair of genes that participate in a 'genetic interaction' measured using an observed 'phenotype' . Genetic interactions use BioPAX classes: Gene, GeneticInteraction. Metabolic-, signaling-and gene regulatory-pathway abstractions are process oriented. They imply a temporal order and can be thought of as extensions of the standard chemical reaction pathway notation to accommodate biological information. Molecular and genetic interactions, however, imply a static network of connections among system components, instead of the temporally ordered process of reactions that defines a metabolic or signaling pathway. BioPAX supports combining these different types of data into a single model that is useful to gain a more complete view of a cellular process.  An ontology is a formal system for representing knowledge 64 . Such representation is required for computer software to make use of information. Example ontologies include organism taxonomies 65 and the Gene Ontology 40 . A formal representation allows consistent communication of knowledge among individuals or computer systems and helps manage complexity in information processing as knowledge is broken down into clear concepts that can be considered independently. Ontologies also enable integration of knowledge between independent resources linked on the World Wide Web. Such linked, structured data form the basis of the semantic web, an extension of the web that promises improved information management and search capability 61 . Representing and sharing knowledge using ontologies is simplified by availability of the standard web ontology language (OWL; http://www.w3.org/TR/owl-features/). Tools to edit OWL, such as Protégé 63 , have been developed by the semantic web community and adopted in the life sciences. Implementing BioPAX using OWL enables both the ontology and the individuals and values to be stored in the same XML-based format, which makes data transmission easier. Using OWL also enables BioPAX users to take advantage of existing software tools for editing, transmitting, querying, reasoning about and visualizing OWL data. An ontology is composed of classes, properties (representing relations) and restrictions and is used to define individuals (instances of classes, also known as objects) and values for their properties. Classes (also known as concepts or types) are often arranged into a hierarchy (or taxonomy) where child classes are more specific than, and inherit the properties of, parent classes. For example, in BioPAX, the BiochemicalReaction class is a subclass of the Conversion class. Classes may have properties (also known as fields, attributes or slots), which express possible relations to other classes (that is, they may have values of specific types). For example, a SmallMolecule is related to the ChemicalStructure class by the property structure. Restrictions (also known as constraints) define allowable values and connections within an ontology. For example, molecularWeight must be a positive number. Individuals are instances of classes where values occupy the properties of those instances. BioPAX defines the classes, properties and restrictions required to represent biological pathways and leaves creation of the individuals to users (data providers and consumers). npg
Both of these tools load pathway data that is represented in a standardized format, such as BioPAX REF .
The BioPAX community standard for pathway data sharing
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