Bernard de Bono

Reactome knowledgebase of human biological pathways and processes.

Reactome (http://www.reactome.org) is an expert-authored, peer-reviewed knowledgebase of human reactions and pathways that functions as a data mining resource and electronic textbook. Its current release includes 2975 human proteins, 2907 reactions and 4455 literature citations. A new entity-level pathway viewer and improved search and data mining tools facilitate searching and visualizing pathway data and the analysis of user-supplied high-throughput data sets. Reactome has increased its utility to the model organism communities with improved orthology prediction methods allowing pathway inference for 22 species and through collaborations to create manually curated Reactome pathway datasets for species including Arabidopsis, Oryza sativa (rice), Drosophila and Gallus gallus (chicken). Reactomes data content and software can all be freely used and redistributed under open source terms.

SIGLEC16 encodes a DAP12-associated receptor expressed in macrophages that evolved from its inhibitory counterpart SIGLEC11 and has functional and non-functional alleles in humans

Sialic acid binding immunoglobulin‐like lectins (Siglec) are important components of immune recognition. The organization of Siglec genes in different species is consistent with rapid selection imposed by pathogens. We studied SIGLEC11 genes in human, rodent, dog, cow and non‐human primates. The lineages of SIGLEC11 genes in these species have undergone dynamic gene duplication and conversion, forming a potential inhibitory (SIGLEC11)/activating (SIGLEC16) receptor pair in chimpanzee and humans. A cDNA encoding human Siglec‐16, currently classed as a pseudogene in the databases (SIGLECP16), is expressed in various cell lines and tissues. A polymorphism screen for the two alleles (wild type and four‐base pair deletion, 4bpΔ) of SIGLEC16 found their frequencies to be 50% amongst the UK population. A search for donor sequences for SIGLEC16 revealed a subfamily of activating Siglec with charged transmembrane domains predicted to associate with ITAM‐encoding adaptor proteins. In support of this, Siglec‐16 was expressed at the cell surface in the presence of DAP12, but not the FcRγ chain. Using antisera specific to the cytoplasmic tail of Siglec‐16, we identified Siglec‐16 expression in CD14+ tissue macrophages and in normal human brain, cancerous oesophagus and lung. This is the first activating human Siglec receptor found to have functional and non‐functional alleles within the population.

Integrating systems biology models and biomedical ontologies

BackgroundSystems biology is an approach to biology that emphasizes the structure and dynamic behavior of biological systems and the interactions that occur within them. To succeed, systems biology crucially depends on the accessibility and integration of data across domains and levels of granularity. Biomedical ontologies were developed to facilitate such an integration of data and are often used to annotate biosimulation models in systems biology.ResultsWe provide a framework to integrate representations of in silico systems biology with those of in vivo biology as described by biomedical ontologies and demonstrate this framework using the Systems Biology Markup Language. We developed the SBML Harvester software that automatically converts annotated SBML models into OWL and we apply our software to those biosimulation models that are contained in the BioModels Database. We utilize the resulting knowledge base for complex biological queries that can bridge levels of granularity, verify models based on the biological phenomenon they represent and provide a means to establish a basic qualitative layer on which to express the semantics of biosimulation models.ConclusionsWe establish an information flow between biomedical ontologies and biosimulation models and we demonstrate that the integration of annotated biosimulation models and biomedical ontologies enables the verification of models as well as expressive queries. Establishing a bi-directional information flow between systems biology and biomedical ontologies has the potential to enable large-scale analyses of biological systems that span levels of granularity from molecules to organisms.

An infrastructure for ontology-based information systems in biomedicine

SUMMARYnThe article presents an infrastructure for supporting the semantic interoperability of biomedical resources based on the management (storing and inference-based querying) of their ontology-based annotations. This infrastructure consists of: (i) a repository to store and query ontology-based annotations; (ii) a knowledge base server with an inference engine to support the storage of and reasoning over ontologies used in the annotation of resources; (iii) a set of applications and services allowing interaction with the integrated repository and knowledge base. The infrastructure is being prototyped and developed and evaluated by the RICORDO project in support of the knowledge management of biomedical resources, including physiology and pharmacology models and associated clinical data.nnnAVAILABILITY AND IMPLEMENTATIONnThe RICORDO toolkit and its source code are freely available from http://ricordo.eu/relevant-resources.nnnCONTACTnsarala@ebi.ac.uk.

Correction: Reactome: a knowledge base of biologic pathways and processes

Reactome http://www.reactome.org, an online curated resource for human pathway data, provides infrastructure for computation across the biologic reaction network. We use Reactome to infer equivalent reactions in multiple nonhuman species, and present data on the reliability of these inferred reactions for the distantly related eukaryote Saccharomyces cerevisiae. Finally, we describe the use of Reactome both as a learning resource and as a computational tool to aid in the interpretation of microarrays and similar large-scale datasets.

Reactome: An integrated expert model of human molecular processes and access toolkit

Summary The behaviour of pervasive molecular processes in human biology can be studied through the large-scale modeling of the molecular events that define them. Constructing detailed models of such extent and scope is a considerable undertaking well beyond the reach and capability of individual efforts, due to the range of expertise required. Reactome (http://www.reactome.org) is an open-access project that collaborates with field experts to integrate their pathway knowledge into a single quality-checked human model. This resource dataset is systematically cross-referenced to major molecular and literature databases, and is accessible to the community in a number of well-established formats. Various tools have been developed to facilitate querying and interaction with this content. The salient features of the annotation strategy are discussed here, and examples of pathway and genomic data integration using flexible interfacing methods from the associated toolkit are also presented.

Reactome – a knowledgebase of biological pathways

Reactome, located at http://www.reactome.org is a curated, peer-reviewed resource of human biological processes. Given the genetic makeup of an organism, the complete set of possible reactions constitutes its reactome. The basic unit of the Reactome database is a reaction; reactions are then grouped into causal chains to form pathways. The Reactome data model allows us to represent many diverse processes in the human system, including the pathways of intermediary metabolism, regulatory pathways, and signal transduction, and high-level processes, such as the cell cycle. Reactome provides a qualitative framework, on which quantitative data can be superimposed. Tools have been developed to facilitate custom data entry and annotation by expert biologists, and to allow visualization and exploration of the finished dataset as an interactive process map. Although our primary curational domain is pathways from Homo sapiens, we regularly create electronic projections of human pathways onto other organisms via putative orthologs, thus making Reactome relevant to model organism research communities. The database is publicly available under open source terms, which allows both its content and its software infrastructure to be freely used and redistributed.

Logic-based representation of connectivity routes in the immune system

This work is part of a general treatment of physiological phenomena grounded on connectivity between anatomical compartments. As a number of immune-related mechanisms may be formally described in terms of white cell movement across body compartments, this paper is concerned with the representation of routes of connectivity in the immune system, focusing on its lymphatic part. The approach relies on ontologies and their expression in a logic-based language supporting spatial knowledge representation and reasoning. The paper discusses informally, and provides elements of formalisation for, a core ontology of the immune system. This ontology is designed to support the representation of topological aspects of immune phenomena at the levels of systems and subsystems of connected tissues, organs, and conduits. The result is a theory i) grounding the representation of immune-related mechanisms on spatial relationships between immunological sites and ii) allowing to infer affordances that relations between sites create for agents.

Integrating volumetric biomedical data in the virtual physiological human

Biomedical imaging has become ubiquitous in both basic research and the clinical context. Technology advances and the resulting multitude of imaging modalities have led to a sharp rise in the quantity and quality of such images. In addition, computational models are increasingly used to study biological processes involving spatio-temporal changes in organisms, e.g. the growth of a tumor, and models and images are extensively described in natural language, for example in research publications and patient records. Together this leads to a major spatio-temporal data and model integration challenge for the next generation of biomedical and eHealth information systems.n In this paper, we discuss a pilot study of volumetric data integration in the context of the Virtual Physiological Human initiative. Three types of spatio-temporal biomedical data sources are briefly introduced and the motivation for their integration presented in use case scenarios. The sources include a computational model of the human heart from the heart physiome project, a statistical atlas of human heart, and a 3D framework for the developing mouse embryo. We report on our experiences of integrating these resources and discuss the wider requirements of volumetric data integration in the biomedical research and eHealth domain.