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Biomedical subjects

Ralf Hofestädt

Publications and source records attributed to Ralf Hofestädt.

8 recordsLinked to original sources
PubMed2006

RAMEDIS: the rare metabolic diseases database.

UNLABELLED: The RAMEDIS system is a platform-independent, web-based information system for rare diseases based on individual case reports. It was developed in close cooperation with clinical partners and collects information on rare metabolic diseases in extensive detail (e.g. symptoms, laboratory findings, therapy and genetic data). This combination of clinical and genetic data enables the analysis of genotype-phenotype correlations. By using largely standardised medical terms and conditions, the contents of the database are easy to compare and analyse. In addition, a convenient graphical user interface is provided by every common web browser. RAMEDIS supports an extendable number of different genetic diseases and enables cooperative studies. Furthermore, use of RAMEDIS should lead to advances in epidemiology, integration of molecular and clinical data, and generation of rules for therapeutic intervention and identification of new diseases. AVAILABILITY: RAMEDIS is available from http://www.ramedis.de CONTACT: Thoralf Töpel (thoralf.toepel@uni-bielefeld.de).

Computational Biology↗
PubMed2005

A medical bioinformatics approach for metabolic disorders: biomedical data prediction, modeling, and systematic analysis.

UNLABELLED: During the past century, studies of metabolic disorders have focused research efforts to improve clinical diagnosis and management, to illuminate metabolic mechanisms, and to find effective treatments. The availability of human genome sequences and transcriptomic, proteomic, and metabolomic data provides us with a challenging opportunity to develop computational approaches for systematic analysis of metabolic disorders. In this paper, we present a strategy of bioinformatics analysis to exploit the current data available both on genomic and metabolic levels and integrate these at novel levels of understanding of metabolic disorders. PathAligner is applied to predict biomedical data based on a given disorder. A case study on urea cycle disorders is demonstrated. A Petri net model is constructed to estimate the regulation both on genomic and metabolic levels. We also analyze the transcription factors, signaling pathways and associated disorders to interpret the occurrence and regulation of the urea cycle. AVAILABILITY: PathAligner's metabolic disorder analyzer is available at http://bibiserv.techfak.uni-bielefeld.de/pathaligner/pathaligner_MDA.html. Supplementary materials are available at http://www.techfak.uni-bielefeld.de/~mchen/metabolic_disorders.

Animals↗
PubMed2004

Web-based information retrieval system for the prediction of metabolic pathways.

Analysis of metabolic pathways is a central topic in understanding the relationship between genotype and phenotype. The rapid accumulation of biological data provides the possibility of studying metabolic pathways both at the genomic and metabolic levels. Our motivation is to develop a conceptual framework and computational system that will allow retrieval of metabolic information and prediction of metabolic pathways. In this paper, we introduce a metabolic pathway prediction framework that extracts metabolic information from biological databases via the Internet, and builds metabolic pathways with data sources of genes, sequences, enzymes, metabolites, etc. It provides an easy-to-use interface to retrieve, display, and manipulate metabolic information. The system has been implemented into PathAligner, available at http://bibiserv.techfak.uni-bielefeld. de/pathaligner/.

Computer Simulation↗
PubMed2004

PathAligner: metabolic pathway retrieval and alignment.

MOTIVATION: Analysis of metabolic pathways is a central topic in understanding the relationship between genotype and phenotype. The rapid accumulation of biological data provides the possibility of studying metabolic pathways at both the genomic and the metabolic levels. Retrieving metabolic pathways from current biological data sources, reconstructing metabolic pathways from rudimentary pathway components, and aligning metabolic pathways with each other are major tasks. Our motivation was to develop a conceptual framework and computational system that allows the retrieval of metabolic pathway information and the processing of alignments to reveal the similarities between metabolic pathways. RESULTS: PathAligner extracts metabolic information from biological databases via the Internet and builds metabolic pathways with data sources of genes, sequences, enzymes, metabolites etc. It provides an easy-to-use interface to retrieve, display and manipulate metabolic information. PathAligner also provides an alignment method to compare the similarity between metabolic pathways. AVAILABILITY: PathAligner is available at http://bibiserv.techfak.uni-bielefeld.de/pathaligner.

Algorithms↗
PubMed2003

Quantitative Petri net model of gene regulated metabolic networks in the cell.

A method to exploit hybrid Petri nets (HPN) for quantitatively modeling and simulating gene regulated metabolic networks is demonstrated. A global kinetic modeling strategy and Petri net modeling algorithm are applied to perform the bioprocess functioning and model analysis. With the model, the interrelations between pathway analysis and metabolic control mechanism are outlined. Diagrammatical results of the dynamics of metabolites are simulated and observed by implementing a HPN tool, Visual Object Net ++. An explanation of the observed behavior of the urea cycle is proposed to indicate possibilities for metabolic engineering and medical care. Finally, the perspective of Petri nets on modeling and simulation of metabolic networks is discussed.

Algorithms↗
PubMed2002

BioDataServer: a SQL-based service for the online integration of life science data.

Regarding molecular biology, we see an exponential growth of data and knowledge. Among others, this fact is reflected in more than 300 molecular databases which are readily available on the Internet. The usage of these data requires integration tools capable of complex information fusion processes. This paper will present a novel concept for user specific integration of life science data. Our approach is based on a mediator architecture in conjunction with freely adjustable data schemes. The implemented prototype is called BioDataServer and can be accessed on the Internet: http://integration.genophen.de. To realize a comfortable usage of the resulted data sets of the integration process, a SQL-based query language and a XML data format were developed and implemented.

Computational Biology↗
PubMed2002

Supporting genotype-phenotype correlation with the rare metabolic diseases database Ramedis.

To gain further knowledge about rare genetic diseases, a world wide method for data collection via the Internet has been established. This new approach will improve collecting valuable data from single case reports. Ramedis saves standardised patient data which will be usable for statistics, longitudinal examinations and cooperative studies in future time. Embedded in the scene of the German Human Genome Project, Ramedis directly will enable phenotype-genotype correlations. Beside the better characterisation of clinical heterogeneity of rare metabolic diseases, there may be a great benefit for the treatment of these patients in whom prospective studies are otherwise expensive and difficult to perform. This contribution presents the motivation for this system, introduces features, current state and the future of the project. Additionally, first experiences of using Ramedis by health professionals are explained.

Database Management Systems↗