DC-ATLAS: a systems biology resource to dissect receptor specific signal transduction in dendritic cells

BACKGROUND: The advent of Systems Biology has been accompanied by the blooming of pathway databases. Currently pathways are defined generically with respect to the organ or cell type where a reaction takes place. The cell type specificity of the reactions is the foundation of immunological research, and capturing this specificity is of paramount importance when using pathway-based analyses to decipher complex immunological datasets. Here, we present DC-ATLAS, a novel and versatile resource for the interpretation of high-throughput data generated perturbing the signaling network of dendritic cells (DCs). RESULTS: Pathways are annotated using a novel data model, the Biological Connection Markup Language (BCML), a SBGN-compliant data format developed to store the large amount of information collected. The application of DC-ATLAS to pathway-based analysis of the transcriptional program of DCs stimulated with agonists of the toll-like receptor family allows an integrated description of the flow of information from the cellular sensors to the functional outcome, capturing the temporal series of activation events by grouping sets of reactions that occur at different time points in well-defined functional modules. CONCLUSIONS: The initiative significantly improves our understanding of DC biology and regulatory networks. Developing a systems biology approach for immune system holds the promise of translating knowledge on the immune system into more successful immunotherapy strategies

Analysis of benzo[a]pyrene metabolites formed by rat hepatic microsomes using high pressure liquid chromatography: optimization of the method

A simple and sensitive method was developed to separate the carcinogenic polycyclic aromatic hydrocarbon (PAH), benzo[a]pyrene (BaP), and six of its oxidation metabolites generated by rat hepatic microsomes enriched with cytochrome P450 (CYP) 1A1, by high pressure liquid chromatography (HPLC). The HPLC method, using an acetonitrile/water gradient as mobile phase and UV detection, provided appropriate separation and detection of both mono- and di-hydroxylated metabolites of BaP as well as BaP diones formed by rat hepatic microsomes and the parental BaP. In this enzymatic system, 3-hydroxy BaP, 9-hydroxy BaP, BaP-4,5-dihydrodiol, BaP-7,8-dihydrodiol, BaP-9,10-dihydrodiol and BaP-dione were generated. Among them the mono-hydroxylated BaP metabolite, 3-hydroxy BaP followed by di-hydroxylated BaP products, BaP-7,8-dihydrodiol and BaP-9,10-dihydrodiol, predominated, while BaP-dione was a minor metabolite. This HPLC method will be useful for further defining the roles of the CYP1A1 enzyme with both in vitro and in vivo models in understanding its real role in activation and detoxification of BaP

BEL2ABM: agent-based simulation of static models in Biological Expression Language

While cause-and-effect knowledge assembly models encoded in Biological Expression Language are able to support generation of mechanistic hypotheses, they are static and limited in their ability to encode temporality. Here, we present BEL2ABM, a software for producing continuous, dynamic, executable agent-based models from BEL templates

Computational Modelling Approaches on Epigenetic Factors in Neurodegenerative and Autoimmune Diseases and Their Mechanistic Analysis

Neurodegenerative as well as autoimmune diseases have unclear aetiologies, but an increasing number of evidences report for a combination of genetic and epigenetic alterations that predispose for the development of disease. This review examines the major milestones in epigenetics research in the context of diseases and various computational approaches developed in the last decades to unravel new epigenetic modifications. However, there are limited studies that systematically link genetic and epigenetic alterations of DNA to the aetiology of diseases. In this work, we demonstrate how disease-related epigenetic knowledge can be systematically captured and integrated with heterogeneous information into a functional context using Biological Expression Language (BEL). This novel methodology, based on BEL, enables us to integrate epigenetic modifications such as DNA methylation or acetylation of histones into a specific disease network. As an example, we depict the integration of epigenetic and genetic factors in a functional context specific to Parkinson’s disease (PD) and Multiple Sclerosis (MS)

HuPSON: The human physiology simulation ontology

BACKGROUND: Large biomedical simulation initiatives, such as the Virtual Physiological Human (VPH), are substantially dependent on controlled vocabularies to facilitate the exchange of information, of data and of models. Hindering these initiatives is a lack of a comprehensive ontology that covers the essential concepts of the simulation domain. RESULTS: We propose a first version of a newly constructed ontology, HuPSON, as a basis for shared semantics and interoperability of simulations, of models, of algorithms and of other resources in this domain. The ontology is based on the Basic Formal Ontology, and adheres to the MIREOT principles; the constructed ontology has been evaluated via structural features, competency questions and use case scenarios. The ontology is freely available at: http://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads.html webcite (owl files) and http://bishop.scai.fraunhofer.de/scaiview/ webcite (browser). CONCLUSIONS: HuPSON provides a framework for a) annotating simulation experiments, b) retrieving relevant information that are required for modelling, c) enabling interoperability of algorithmic approaches used in biomedical simulation, d) comparing simulation results and e) linking knowledge-based approaches to simulation-based approaches. It is meant to foster a more rapid uptake of semantic technologies in the modelling and simulation domain, with particular focus on the VPH domain

Spatial pattern of occurrence of eleven epiphytic lichen species in a heterogeneous landscape

Oaks (Quercus robur) are an important substrate for many epiphytic lichens, and with increasing age the bark of oaks becomes suitable for red-listed species. These species may respond to environmental and landscape factors differently, and at different spatial scales. We tested the effect of tree, environmental and land use factors on the occurrence and richness patterns of lichens species at various spatial scales (circles with radius ranging from 28 to 1225 m), in a heterogeneous landscape in South Eastern Sweden. The occurrence patterns of Cliostomum corrugatum and Chaenotheca phaeocephala were best explained by the density of oaks within radii of 400 and 302 m, respectively. In contrast, Ramalina baltica was best explained at smaller scale (263 m) as was species richness (302 m). This study shows that the most important factor for the occurrence and richness patterns of lichens was oak density at almost all the considered scales. Tree circumference also positively affected all four response variables

Additional file 1: of PDON: Parkinson’s disease ontology for representation and modeling of the Parkinson’s disease knowledge domain

Convergence of miRNA Expression Profiling, ?-­Synuclein Interacton and GWAS in Parkinson's Disease. (DOCX 449 KB

Knowledge Retrieval from PubMed Abstracts and Electronic Medical Records with the Multiple Sclerosis Ontology

Background In order to retrieve useful information from scientific literature and electronic medical records (EMR) we developed an ontology specific for Multiple Sclerosis (MS). Methods The MS Ontology was created using scientific literature and expert review under the Protégé OWL environment. We developed a dictionary with semantic synonyms and translations to different languages for mining EMR. The MS Ontology was integrated with other ontologies and dictionaries (diseases/comorbidities, gene/protein, pathways, drug) into the text-mining tool SCAIView. We analyzed the EMRs from 624 patients with MS using the MS ontology dictionary in order to identify drug usage and comorbidities in MS. Testing competency questions and functional evaluation using F statistics further validated the usefulness of MS ontology. Results Validation of the lexicalized ontology by means of named entity recognition-based methods showed an adequate performance (F score = 0.73). The MS Ontology retrieved 80% of the genes associated with MS from scientific abstracts and identified additional pathways targeted by approved disease-modifying drugs (e.g. apoptosis pathways associated with mitoxantrone, rituximab and fingolimod). The analysis of the EMR from patients with MS identified current usage of disease modifying drugs and symptomatic therapy as well as comorbidities, which are in agreement with recent reports. Conclusion The MS Ontology provides a semantic framework that is able to automatically extract information from both scientific literature and EMR from patients with MS, revealing new pathogenesis insights as well as new clinical information